Methods and processes for non-invasive assessment of chromosomal alterations

Abstract

To provide methods and processes for non-invasive assessment of chromosomal alterations.SOLUTION: In this specification, there are provided methods, processes, systems, machines, and apparatus for non-invasive assessment of chromosomal alterations. Provided are a method for determining the presence or absence of chromosomal alterations of one or more species in a sample nucleic acid; a system comprising a memory and one or more microprocessors; a method that includes memory and one or more microprocessors; and a non-transitory computer-readable storage medium having an executable program stored thereon, and the like.SELECTED DRAWING: None

Description

[Technical Field]

[0001] Related Applications This patent application is filed by inventors Sung Kim, Taylor Jacob Jensen and Mathias Ehrich. and "METHODS AND PROCESSES FOR A 2013 publication entitled "NON-INVASIVE ASSESSMENT OF CHROMOSOME ALTERATIONS" This application claims the benefit of U.S. Provisional Patent Application No. 61 / 887,801, filed October 7, 2007. The entire contents of the foregoing application, including all text, tables, and drawings thereof, are incorporated herein by reference.

[0002] Field The technology provided herein relates, in part, to methods, processes, machines, and devices for the non-invasive assessment of chromosomal alterations. [Background technology]

[0003] background The genetic information of living organisms (e.g., animals, plants, and microorganisms), and other replicating forms of genetic information (e.g., viruses), is encoded in deoxyribonucleic acid (DNA) or ribonucleic acid (RNA). Genetic information is a sequence of nucleotides or modified nucleotides that represents a chemical or hypothetical primary nucleic acid structure. In humans, the complete genome contains approximately 30,000 genes arranged on 24 chromosomes (see "The Human Genome," T. Strachan, BIOS Scientific Publishers, 1992). Each gene, after expression via transcription and translation, encodes a specific protein that performs a specific biochemical function within a living cell.

[0004] Identifying one or more chromosomal alterations can lead to a diagnosis of, or a determination of predisposition to, a particular medical condition. Identifying chromosomal alterations can result in easier medical diagnosis and / or the use of useful medical procedures. In certain embodiments, identifying one or more chromosomal alterations involves analyzing cell-free DNA. Cell-free DNA (CF-DNA) consists of DNA fragments derived from cell death and circulating in peripheral blood. High concentrations of CF-DNA can indicate certain clinical conditions, such as cancer, trauma, burns, myocardial infarction, stroke, sepsis, infection, and other diseases. In addition, cell-free fetal DNA (CFF-DNA) can be detected in the maternal bloodstream and can be used in a variety of non-invasive prenatal diagnostic methods.

[0005] The presence of fetal nucleic acid in maternal plasma allows non-invasive prenatal diagnosis through the analysis of maternal blood samples.For example, quantitative abnormalities in fetal DNA in maternal plasma can be associated with many pregnancy-related disorders and genetic diseases that are associated with fetal chromosomal changes.Therefore, the analysis of fetal nucleic acid in maternal plasma can be a useful mechanism for monitoring fetal-maternal well-being. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] "The Human Genome", T. Strachan, BIOS Scientific Publishers, 1992 Summary of the Invention [Means for solving the problem]

[0007] overview Certain aspects of the present disclosure provide a system comprising a memory and one or more microprocessors, wherein the memory comprises instructions, and the one or more microprocessors are configured to follow the instructions and implement a process for determining the presence or absence of one or more chromosomal alterations in a sample nucleic acid, the process comprising: (a) determining the mappability of a plurality of sequence read subsequences by aligning the sequence read subsequences; A system is provided that includes the steps of: (a) characterizing sequence reads, wherein for each sequence read, there are multiple sequence read subsequences, the sequence read subsequences for each sequence read vary in length, and the sequence read is a sequence read of a sample nucleic acid; (b) identifying a subset of sequence reads in which there is an alteration in the mappability of one or more subsequences; (c) comparing the number of each of the sequence reads in the subset identified from the sample in (i)(b) with the number of each of the sequence reads in the subset identified from the reference in (ii)(b), thereby generating a comparison; and (d) determining the presence or absence of one or more chromosomal alterations for the sample according to the comparison in (c).

[0008] Also provided in certain aspects herein is a method comprising a memory and one or more microprocessors, wherein the memory comprises instructions, and the one or more microprocessors are configured to perform a process for determining the presence or absence of one or more types of chromosomal alterations in a sample nucleic acid in accordance with the instructions, the process comprising: (a) characterizing the mappability of a plurality of sequence read subsequences for the sequence reads, wherein for each sequence read, there are multiple sequence read subsequences, and the sequence read subsequences for each sequence read vary in length, and the sequence read is a sequence read for the sample nucleic acid; (b) identifying a subset of sequence reads in which an alteration in the mappability of one or more subsequences exists; (c) comparing the number of each of the sequence reads in the subset identified from the sample in (i)(b) with the number of each of the sequence reads in the subset identified from the reference in (ii)(b), thereby generating a comparison; and (d) determining the presence or absence of one or more types of chromosomal alterations for the sample according to the comparison in (c).

[0009] Also provided in certain aspects herein is a non-transitory computer-readable storage medium having an executable program stored thereon, the program configured to instruct a microprocessor to: (a) characterize the mappability of a plurality of sequence read subsequences for a sequence read, where for each sequence read, there are multiple sequence read subsequences, the sequence read subsequences for each sequence read vary in length, and the sequence read is a sequence read of a sample nucleic acid; (b) identify a subset of sequence reads in which an alteration in mappability of one or more subsequences exists; (c) compare the number of each of the sequence reads in the subset identified from the sample in (i)(b) with the number of each of the sequence reads in the subset identified from the reference in (ii)(b), thereby generating a comparison; and (d) determine the presence or absence of one or more chromosomal alterations for the sample according to the comparison in (c).

[0010] In certain embodiments herein, for example, the following items are provided: (Item 1) 1. A method for determining the presence or absence of one or more chromosomal alterations in a sample nucleic acid, comprising: (a) characterizing the mappability of a plurality of sequence read subsequences for a sequence read; For each sequence read, there are multiple sequence read subsequences; the sequence read subsequences for each sequence read vary in length; The sequence read is a sequence read of the sample nucleic acid. Steps and (b) identifying a subset of sequence reads in which there is a change in mappability of one or more subsequences; (c) comparing the number of each of the sequence reads in the subset identified from the sample in (i)(b) with the number of each of the sequence reads in the subset identified from the reference in (ii)(b), thereby generating a comparison; (d) determining the presence or absence of one or more chromosomal alterations for the sample according to the comparison in (c); A method comprising: (Item 2) 1. A method comprising a memory and one or more microprocessors, wherein the memory contains instructions, and wherein the one or more microprocessors are configured to follow the instructions and carry out a process for determining the presence or absence of one or more chromosomal alterations in a sample nucleic acid, the process comprising: (a) characterizing the mappability of a plurality of sequence read subsequences for a sequence read; For each sequence read, there are multiple sequence read subsequences; the sequence read subsequences for each sequence read vary in length; The sequence read is a sequence read of the sample nucleic acid. Steps and (b) identifying a subset of sequence reads in which there is a change in mappability of one or more subsequences; (c) comparing the number of each of the sequence reads in the subset identified from the sample in (i)(b) with the number of each of the sequence reads in the subset identified from the reference in (ii)(b), thereby generating a comparison; (d) determining the presence or absence of one or more chromosomal alterations in the sample according to the comparison in (c); A method comprising: (Item 3) 1. A method for determining the presence or absence of one or more chromosomal alterations in a sample nucleic acid, comprising: loading a sequencing device with circulating cell-free nucleic acid from a test sample, or loading an engineered variant of the nucleic acid onto the sequencing device, wherein the sequencing device generates signals corresponding to the nucleotide bases of the nucleic acid; Optionally, generating sequence reads from the signals of the nucleic acids by a system comprising one or more computing devices after transferring the signals to the system, the one or more computing devices comprising a memory and one or more processors; determining the presence or absence of one or more chromosomal alterations in the sample nucleic acid by the system, wherein a computing device or combination of computing devices in the system aligns the sequence reads to a reference genome; (a) characterizing the mappability of subsequences of a plurality of sequence reads for the sequence reads, wherein: For each sequence read, there are multiple sequence read subsequences; the sequence read subsequences for each sequence read vary in length; the sequence reads are sequence reads of the sample nucleic acid; (b) identifying a subset of sequence reads in which one or more subsequence mappability changes are present; (c) comparing the number of each of the sequence reads in the subset identified from the sample in (i)(b) to the number of each of the sequence reads in the subset identified from the reference in (ii)(b), thereby generating a comparison; (d) determining the presence or absence of one or more chromosomal alterations for the sample according to the comparison in (c); and a step configured as follows: A method comprising: (Item 4) 4. The method of claim 1, wherein the sequence reads are sequence reads of circulating cell-free nucleic acid. (Item 5) 5. The method of any one of items 1 to 4, wherein the average length of polynucleotides in the sample nucleic acid is less than about 300 base pairs. (Item 6) 6. The method of item 4 or 5, wherein the circulating cell-free nucleic acid is derived from serum or plasma. (Item 7) 7. The method of any one of items 1 to 6, wherein the sequence reads are mapped to a reference genome or portion thereof. (Item 8) 8. The method of claim 7, comprising, prior to (a), identifying a subset of sequence reads for all bases that do not align to the reference genome or portion thereof, and performing (a), (b), (c), and (d) on the subset. (Item 9) 9. The method of any one of items 1 to 8, wherein the sequence reads are single-end sequence reads. (Item 10) 11. The method of any one of items 1 to 9, wherein the sequence reads are mismatched reads. 11. The method of claim 10, wherein a change in mappability is determined for the mismatched reads. (Item 12) 9. The method of any one of items 1 to 8, wherein the sequence reads are paired-end sequence reads. (Item 13) 13. The method of claim 12, wherein the sequence reads are mismatched read pairs. (Item 14) 14. The method of any one of items 1 to 13, comprising identifying mismatched read pairs, thereby providing mismatched read mates. (Item 15) Item 15. The system of item 14, wherein a change in mappability is determined for the non-matching readmates. (Item 16) 16. The method of any one of items 1 to 15, wherein chimeric read pairs are not identified prior to (a). (Item 17) 17. The method of any one of items 1 to 16, comprising, prior to the comparing step in (c), identifying candidate breakpoints for each sequence read in the subset. (Item 18) 18. The method of claim 17, wherein the candidate breakpoints for each sequence read are identified according to the change in mappability. (Item 19) 19. The method of claim 17 or 18, wherein the comparing step in (c) comprises comparing the number of each of the sequence reads in the subset identified from the sample associated with the candidate breakpoint in (i)(b) to the number of each of the sequence reads in the subset identified from the standard associated with the candidate breakpoint in (ii)(b). (Item 20) 20. The method of any one of items 1 to 19, wherein the sequence reads in the subset identified in (b) have a minimum length of about 32 contiguous bases. (Item 21) 21. The method of any one of items 17 to 20, wherein there are at least about 15 to about 20 contiguous bases on each side of the candidate breakpoint in each of the sequence reads in the subset identified in (b). (Item 22) 22. The method of any one of items 1 to 21, wherein the sequence reads are sequence reads of nucleic acid fragments having an average length, mean length, median length, or maximum length of about 20 bases to about 500 bases. (Item 23) Item 24. The method according to Item 22, wherein the sequence reads are sequence reads of nucleic acid fragments having an average length, mean length, median length, or maximum length of about 40 bases to about 500 bases. 24. The method of any one of items 1 to 23, wherein the one or more chromosomal alterations comprise a chromosomal translocation. (Item 25) 25. The method of any one of items 1 to 24, wherein the one or more chromosomal alterations comprise a balanced chromosomal translocation. (Item 26) 24. The method of any one of items 1 to 23, wherein the one or more chromosomal alterations comprise a chromosomal deletion. (Item 27) 24. The method of any one of items 1 to 23, wherein the one or more chromosomal alterations comprise a chromosomal inversion. (Item 28) 24. The method of any one of items 1 to 23, wherein the one or more chromosomal alterations comprise a heterologous insertion. (Item 29) 29. The method of any one of items 1 to 28, comprising providing the location of one or more breakpoints when determining the presence of the one or more chromosomal alterations in (d). (Item 30) 30. The method of item 29, wherein the position of each of the one or more breakpoints is provided at a resolution of one base pair. (Item 31) 31. The method of any one of items 1 to 30, wherein the identifying step in (b) comprises generating a fit relationship between the mappability and the length for each of the sequence read subsequences of each of the reads. (Item 32) 32. The method of claim 31, wherein the change in mappability is determined from the slope of the relationship. (Item 33) 33. The method of any one of items 1 to 32, wherein sequence reads in which a partial sequence of an increased length of a sequence read is aligned to a first chromosome, then to a second chromosome, and then to the first chromosome, are not included in the subset identified in (b). (Item 34) 34. The method of any one of items 1 to 33, wherein sequence reads in which the length-increasing sequence read subsequences align to a first chromosome and then to a second chromosome are included in the subset identified in (b). (Item 35) 35. The method of any one of items 1 to 34, wherein the comparison in (c) is determined according to a z-score between the number of sequence reads in (c)(i) and the number of sequence reads in (c)(ii). (Item 36) 36. The method of any one of items 17 to 35, wherein each of the sequence reads in the subset identified in (b) comprises substantially similar candidate breakpoints. (Item 37) 37. The method of any one of items 1 to 36, wherein each of the sequence read subsequences of each read is about 5 bases or less shorter than the next largest fragment or read. (Item 38) 38. The method of claim 37, wherein each of the sequence read subsequences of each read is one or two bases shorter than the next largest fragment or read. (Item 39) 39. The method of claim 38, wherein each of the sequence read subsequences of each read is progressively shorter than the next largest fragment or read. (Item 40) 40. The method of claim 39, wherein each of the sequence read subsequences of each read is progressively shorter than the next largest fragment or read by about one base. (Item 41) 41. The method of any one of items 31 to 40, wherein the characterizing the mappability of a plurality of sequence read subsequences comprises determining a slope of the fitting relationship. (Item 42) 42. The method of any one of items 1 to 41, wherein the identifying step in (b) is according to a mappability threshold. (Item 43) 43. The method of any one of items 1 to 42, comprising filtering the reads. (Item 44) Item 44. The method of item 43, wherein the filtering step includes excluding one or both of the mismatched readmates. (Item 45) 45. The method of claim 43 or 44, wherein the filtering step is selected from one or more of: (i) filtering out low quality reads, (ii) filtering out matched reads, (iii) filtering out PCR duplicated reads, (iv) filtering out reads that map to mitochondrial DNA, (v) filtering out reads that map to repetitive elements, (vi) filtering out unmappable reads, (vi) filtering out reads containing stepwise multiple alignments, and (vii) filtering out reads that map to centromeres. (Item 46) 46. ​​The method of any one of items 43 to 45, wherein the filtering step comprises excluding one or more singleton events. (Item 47) 47. The method of any one of items 43 to 46, wherein the filtering step comprises excluding the subset of reads identified in (b) if the number of each of the sequence reads in the subset derived from the sample is substantially similar to the number of each of the sequence reads in the subset derived from the reference. (Item 48) 48. The method of any one of items 36 to 47, wherein the locations of the candidate breakpoints are identified at single base resolution. (Item 49) Item 50. The method of any one of items 1 to 48, wherein in (d), the presence of a balanced translocation is determined. 50. The method of any one of items 1 to 49, wherein in (d) the presence of an unbalanced translocation is determined. (Item 51) 51. The method of any one of items 1 to 50, wherein determining the presence of the chromosomal alteration in (d) comprises identifying a substantially greater number of sequence reads from the sample compared to the reference in the comparison of (c). (Item 52) 52. The method of any one of items 1 to 51, wherein breakpoints are identified according to said comparison in (c). (Item 53) 53. The method of any one of items 1 to 52, wherein the first breakpoint and the second breakpoint are identified according to the comparison in (c). (Item 54) 54. The method of item 53, wherein in (d), the presence of the chromosomal alteration is identified according to the first breakpoint and the second breakpoint. (Item 55) 55. The method of any one of items 1 to 54, wherein the comparing in (c) comprises determining a confidence level. (Item 56) Item 57. The method of item 55, wherein determining the confidence level comprises determining a p-value. 56. The method of claim 55, wherein determining the confidence level comprises determining a Z-score. (Item 58) 58. The method of any one of items 2 to 57, wherein the memory comprises one or more of the sequence reads, the plurality of sequence read subsequences, the mismatched read pairs, the subset of reads, the candidate breakpoints, or combinations thereof. (Item 59) 59. The method of any one of items 4 to 58, comprising, prior to (a), determining the sequence reads by sequencing the circulating cell-free nucleic acid. (Item 60) 1. A method for determining the presence or absence of one or more chromosomal alterations in a sample nucleic acid, comprising: (a) identifying mismatched read pairs from paired-end sequence reads, wherein the paired-end sequence reads are reads of circulating cell-free nucleic acids from a test subject sample, thereby identifying mismatched read mates; (b) characterizing the mappability of a plurality of sequence read subsequences for each mismatched readmate aligned to a reference genome, wherein each of the sequence read subsequences for each mismatched readmate differs in length; (c) selecting a subset of the mismatched readmates according to change in mappability, the subset including reads containing candidate breakpoints; (d) comparing (i) the number of mismatched readmates from the sample associated with the candidate breakpoint, and optionally associated with one or more substantially similar breakpoints, with (ii) the number of mismatched readmates from a reference associated with the candidate breakpoint, and optionally associated with the one or more substantially similar breakpoints, for the mismatched readmates in the subset selected in (c), thereby generating a comparison; (e) determining the presence or absence of one or more chromosomal alterations for the sample according to the comparison in (d); A method comprising: (Item 61) 1. A method for determining the presence or absence of one or more chromosomal alterations in a sample nucleic acid, comprising: loading a sequencing device with circulating cell-free nucleic acid from a test sample, or loading an engineered variant of the nucleic acid onto the sequencing device, wherein the sequencing device generates signals corresponding to the nucleotide bases of the nucleic acid; Optionally, generating paired-end sequence reads from the signals of the nucleic acids by a system comprising one or more computing devices after transferring the signals to the system, the one or more computing devices comprising a memory and one or more processors; determining the presence or absence of one or more chromosomal alterations in the sample nucleic acid by the system, wherein a computing device or combination of computing devices in the system aligns the sequence reads to a reference genome; (a) identifying mismatched read pairs from the paired-end sequence reads, thereby identifying mismatched read mates; (b) characterizing the mappability of multiple sequence read subsequences of each mismatched readmate aligned to the reference genome, wherein each of the sequence read subsequences of each mismatched readmate differs in length; (c) selecting a subset of the mismatched readmates according to change in mappability, wherein the subset includes reads that include the candidate breakpoint; (d) comparing (i) the number of mismatched readmates from the sample associated with the candidate breakpoint, and optionally associated with one or more substantially similar breakpoints, with (ii) the number of mismatched readmates from a reference associated with the candidate breakpoint, and optionally associated with the one or more substantially similar breakpoints, for the mismatched readmates in the subset selected in (c), thereby generating a comparison; (e) determining the presence or absence of one or more chromosomal alterations for the sample according to the comparison in (d); and a step configured as follows: A method comprising: (Item 62) 62. The method of item 60 or 61, wherein the one or more chromosomal alterations comprise a chromosomal translocation. (Item 63) 62. The method of item 60 or 61, wherein the one or more chromosomal alterations comprise a chromosomal deletion. (Item 64) 62. The method of item 60 or 61, wherein the one or more chromosomal alterations comprise a chromosomal inversion. (Item 65) 62. The method of item 60 or 61, wherein the one or more chromosomal alterations comprise a heterologous insertion. (Item 66) 66. The method of any one of items 60 to 65, comprising determining the location of one or more candidate breakpoints. (Item 67) 67. The method of any one of items 60 to 66, wherein the characterizing step in (b) comprises generating a fit relationship between the mappability and the length for each of the sequence read subsequences of each mismatched readmate. (Item 68) 68. The method of any one of items 60 to 67, wherein each of the sequence read subsequences for each mismatched readmate is about 5 bases or less shorter than the next largest fragment or readmate. (Item 69) 69. The method of claim 68, wherein each of the sequence read subsequences for each mismatched readmate is one or two bases shorter than the next largest fragment or readmate. (Item 70) 69. The method of claim 68, wherein each of the sequence read subsequences of each mismatched readmate is progressively shorter than the next largest fragment or readmate. (Item 71) 71. The method of claim 70, wherein each of the sequence read subsequences for each mismatched readmate is progressively shorter than the next largest fragment or readmate by about one base. (Item 72) 72. The method of any one of items 67 to 71, wherein the change in mappability is determined from the slope of the fitting relationship. (Item 73) 73. The method of any one of items 60 to 72, wherein the selecting step in (c) is according to a mappability threshold. (Item 74) 74. The method of any one of items 60 to 73, comprising filtering the non-matching readmates. (Item 75) Item 75. The method of item 74, wherein the filtering step includes excluding one or both of the mismatched readmates. (Item 76) 77. The method of claim 74 or 75, wherein the filtering step is selected from one or more of: (i) filtering out low-quality reads, (ii) filtering out matched reads, (iii) filtering out PCR-duplicated reads, (iv) filtering out reads that map to mitochondrial DNA, (v) filtering out reads that map to repetitive elements, (vi) filtering out unmappable reads, (vii) filtering out reads that contain stepwise multiple alignments, and (viii) filtering out reads that map to centromeres. 77. The method of any one of items 74 to 76, wherein the filtering step comprises excluding one or more singleton events. (Item 78) 77. The method of any one of items 74 to 76, wherein the filtering step comprises filtering out mismatched readmates if the substantially similar breakpoints are within the criteria. (Item 79) 79. The method of any one of items 60 to 78, wherein the location of the breakpoint is identified with single base resolution. (Item 80) 80. The method of any one of paragraphs 60 to 79, wherein in (e) the presence of a balanced translocation is determined. (Item 81) 81. The method of any one of items 60 to 80, wherein in (e) the presence of an unbalanced translocation is determined. (Item 82) 82. The method of any one of items 60 to 81, wherein determining the presence of the chromosomal alteration in (e) comprises identifying a substantially greater number of sequence reads from the sample compared to the reference in the comparison of (d). (Item 83) 83. The method of any one of items 60 to 82, wherein the first breakpoint and the second breakpoint are identified according to the comparison in (d). (Item 84) 84. The method of item 83, wherein in (e), the presence of the chromosomal alteration is identified according to the first breakpoint and the second breakpoint. (Item 85) 85. The method of any one of items 60 to 84, wherein the selecting step in (c) or the comparing step in (d), or the selecting step in (c) and the comparing step in (d), does not comprise performing a clustering analysis. (Item 86) 86. The method of any one of items 60 to 85, wherein the comparing in (d) comprises determining a confidence level. (Item 87) Item 88. The method of item 86, wherein determining the confidence level comprises determining a p-value. Item 87. The method of item 86, wherein determining the confidence level comprises determining a Z-score. (Item 89) 89. The method of any one of items 60 to 88, comprising the use of one or more machines. (Item 90) 90. The method of claim 89, comprising use of a sequencing machine configured to generate the sequence reads. (Item 91) 91. The method according to item 89 or 90, embodied in one machine. (Item 92) 92. The method of any one of items 60 to 91, comprising obtaining the sequence reads, obtaining the mismatched read pairs, obtaining the subset of mismatched read mates, obtaining mappability changes, obtaining the breakpoints, or a combination thereof. (Item 93) 93. The method of any one of items 1 to 92, wherein the sample nucleic acid is circulating cell-free nucleic acid from a pregnant woman carrying a fetus. (Item 94) 93. The method of any one of items 1 to 92, wherein the sample nucleic acid is circulating cell-free nucleic acid from a subject having or suspected of having a cell proliferative disorder. (Item 95) 95. The method of item 94, wherein the cell proliferative disorder is cancer. (Item 96) 96. The method of any one of items 1 to 95, wherein the presence or absence of one or more chromosomal alterations is determined for minority nucleic acid species. (Item 97) 97. The method of claim 96, wherein the minority nucleic acid species comprises fetal nucleic acids. (Item 98) 97. The method of claim 96, wherein the minority nucleic acid species comprises nucleic acids derived from cancer cells. Certain aspects and embodiments of the technology are further described in the following description, examples, claims, and figures.

[0011] The drawings are illustrative of embodiments of the technology, not limiting. For clarity and ease of illustration, the drawings may not be made to scale and, in some cases, various aspects may be shown exaggerated or enlarged to facilitate an understanding of particular embodiments. [Brief explanation of the drawings]

[0012] [Figure 1A]Figures 1A-C show the identification of fetal balanced translocations in maternal plasma. Figure 1A shows a Circos plot (Krzywinski M. et al. (2009) Genome Res 19:1639-45) detailing the fetal balanced translocation identified between chromosomes 8 and 11. Diagonal lines represent the start and end points of the sequenced fragments. Chromosomes are highlighted to highlight banding patterns and centromeres. Figure 1B shows a Circos plot highlighting the identified areas of translocation for each affected chromosome. Repetitive regions within each of these regions are highlighted in black. Each line represents the start and end positions of the sequenced fragments. Figure 1C shows base-level descriptions of individual sequencing reads spanning the translocation breakpoints for each reciprocal translocation event. Sequences derived from chromosome 8 are indicated by "CHR8" and are located to the right of the designator. Sequences derived from chromosome 11 are designated "CHR11" and are located to the right of the designator. Vertical dashed lines indicate the location of the chromosome breakpoint. The designator "CHR8 / CHR11" indicates the chromosome 8 sequence to the left of the breakpoint location and the chromosome 11 sequence to the right of the breakpoint location. The designator "CHR11 / CHR8" indicates the chromosome 11 sequence to the left of the breakpoint location and the chromosome 8 sequence to the right of the breakpoint location. Horizontal dashed lines indicate nucleotide deletions. [Figure 1B]Figures 1A-C show the identification of fetal balanced translocations in maternal plasma. Figure 1A shows a Circos plot (Krzywinski M. et al. (2009) Genome Res 19:1639-45) detailing the fetal balanced translocation identified between chromosomes 8 and 11. Diagonal lines represent the start and end points of the sequenced fragments. Chromosomes are highlighted to highlight banding patterns and centromeres. Figure 1B shows a Circos plot highlighting the identified areas of translocation for each affected chromosome. Repetitive regions within each of these regions are highlighted in black. Each line represents the start and end positions of the sequenced fragments. Figure 1C shows base-level descriptions of individual sequencing reads spanning the translocation breakpoints for each reciprocal translocation event. Sequences derived from chromosome 8 are indicated by "CHR8" and are located to the right of the designator. Sequences derived from chromosome 11 are designated "CHR11" and are located to the right of the designator. Vertical dashed lines indicate the location of the chromosome breakpoint. The designator "CHR8 / CHR11" indicates the chromosome 8 sequence to the left of the breakpoint location and the chromosome 11 sequence to the right of the breakpoint location. The designator "CHR11 / CHR8" indicates the chromosome 11 sequence to the left of the breakpoint location and the chromosome 8 sequence to the right of the breakpoint location. Horizontal dashed lines indicate nucleotide deletions. [Figure 1C]Figures 1A-C show the identification of fetal balanced translocations in maternal plasma. Figure 1A shows a Circos plot (Krzywinski M. et al. (2009) Genome Res 19:1639-45) detailing the fetal balanced translocation identified between chromosomes 8 and 11. Diagonal lines represent the start and end points of the sequenced fragments. Chromosomes are highlighted to highlight banding patterns and centromeres. Figure 1B shows a Circos plot highlighting the identified areas of translocation for each affected chromosome. Repetitive regions within each of these regions are highlighted in black. Each line represents the start and end positions of the sequenced fragments. Figure 1C shows base-level descriptions of individual sequencing reads spanning the translocation breakpoints for each reciprocal translocation event. Sequences derived from chromosome 8 are indicated by "CHR8" and are located to the right of the designator. Sequences derived from chromosome 11 are designated "CHR11" and are located to the right of the designator. Vertical dashed lines indicate the location of the chromosome breakpoint. The designator "CHR8 / CHR11" indicates the chromosome 8 sequence to the left of the breakpoint location and the chromosome 11 sequence to the right of the breakpoint location. The designator "CHR11 / CHR8" indicates the chromosome 11 sequence to the left of the breakpoint location and the chromosome 8 sequence to the right of the breakpoint location. Horizontal dashed lines indicate nucleotide deletions. [Figure 2A]Figures 2A-D show the average MAPQ scores for sequence read subsequences of simulated reads containing structural rearrangement breakpoints (vertical black lines) at various positions. Sequence read subsequences were generated as single-base increments. Figures 2A-2D show the overall mapping confidence of each sequence read subsequence (pseudoread) for mate pair 1 (R1) and mate pair 2 (R2), where the breakpoints are at positions 10 (Figure 2A), 40 (Figure 2B), 70 (Figure 2C), or 120 (Figure 2D) for a given target fragment length of approximately 140 bp. For R1, the average MAPQ scores for pseudoread lengths from 32 to 100 bp are plotted as gray boxes, starting from the leftmost start position of the fragment. For R2, the average MAPQ scores for pseudoread lengths between 32 and 100 bp are plotted in reverse order, starting from the rightmost position of the fragment, as black boxes. Figure 2C demonstrates the change in average MAPQ as highly mappable pseudoreads become unmappable due to the expansion of sequences from different genomic regions. [Figure 2B]Figures 2A-D show the average MAPQ scores for sequence read subsequences of simulated reads containing structural rearrangement breakpoints (vertical black lines) at various positions. Sequence read subsequences were generated as single-base increments. Figures 2A-2D show the overall mapping confidence of each sequence read subsequence (pseudoread) for mate pair 1 (R1) and mate pair 2 (R2), where the breakpoints are at positions 10 (Figure 2A), 40 (Figure 2B), 70 (Figure 2C), or 120 (Figure 2D) for a given target fragment length of approximately 140 bp. For R1, the average MAPQ scores for pseudoread lengths from 32 to 100 bp are plotted as gray boxes, starting from the leftmost start position of the fragment. For R2, the average MAPQ scores for pseudoread lengths between 32 and 100 bp are plotted in reverse order, starting from the rightmost position of the fragment, as black boxes. Figure 2C demonstrates the change in average MAPQ as highly mappable pseudoreads become unmappable due to the expansion of sequences from different genomic regions. [Figure 2C]Figures 2A-D show the average MAPQ scores for sequence read subsequences of simulated reads containing structural rearrangement breakpoints (vertical black lines) at various positions. Sequence read subsequences were generated as single-base increments. Figures 2A-2D show the overall mapping confidence of each sequence read subsequence (pseudoread) for mate pair 1 (R1) and mate pair 2 (R2), where the breakpoints are at positions 10 (Figure 2A), 40 (Figure 2B), 70 (Figure 2C), or 120 (Figure 2D) for a given target fragment length of approximately 140 bp. For R1, the average MAPQ scores for pseudoread lengths from 32 to 100 bp are plotted as gray boxes, starting from the leftmost start position of the fragment. For R2, the average MAPQ scores for pseudoread lengths between 32 and 100 bp are plotted in reverse order, starting from the rightmost position of the fragment, as black boxes. Figure 2C demonstrates the change in average MAPQ as highly mappable pseudoreads become unmappable due to the expansion of sequences from different genomic regions. [Figure 2D]Figures 2A-D show the average MAPQ scores for sequence read subsequences of simulated reads containing structural rearrangement breakpoints (vertical black lines) at various positions. Sequence read subsequences were generated as single-base increments. Figures 2A-2D show the overall mapping confidence of each sequence read subsequence (pseudoread) for mate pair 1 (R1) and mate pair 2 (R2), where the breakpoints are at positions 10 (Figure 2A), 40 (Figure 2B), 70 (Figure 2C), or 120 (Figure 2D) for a given target fragment length of approximately 140 bp. For R1, the average MAPQ scores for pseudoread lengths from 32 to 100 bp are plotted as gray boxes, starting from the leftmost start position of the fragment. For R2, the average MAPQ scores for pseudoread lengths between 32 and 100 bp are plotted in reverse order, starting from the rightmost position of the fragment, as black boxes. Figure 2C demonstrates the change in average MAPQ as highly mappable pseudoreads become unmappable due to the expansion of sequences from different genomic regions. [Figure 3] Figure 3 shows a simulated translocation where both regions contain highly unique sequences. For each simulated translocation event, the mean slope of the mapping quality score (y-axis) is plotted across all simulated breakpoint locations (x-axis). [Figure 4] Figure 4 shows a simulated translocation where the left region contains a highly unique sequence and the right region contains a repetitive element. For each simulated translocation event, the mean slope of the mapping quality score (y-axis) is plotted at all simulated breakpoint positions (x-axis). [Figure 5A-5B] Figures 5A-B show the likely false-positive translocations observed between chromosomes 2 and 5 for Mix B (Figure 5A) and the "pooled" control set (Figure 5B). The gray bars indicate the regions of the repetitive elements. The left and right coordinates correspond to chromosomes 2 and 5 (hg19), respectively. [Figure 6] FIG. 6 illustrates an exemplary embodiment of a system in which certain embodiments of the technology may be implemented. [Figure 7] FIG. 7 illustrates an exemplary embodiment of a filter. [Figure 8] FIG. 8 illustrates an exemplary embodiment of a system in which certain embodiments of the technology may be implemented. DETAILED DESCRIPTION OF THE INVENTION

[0013] Detailed Description Provided herein are systems and methods for analyzing polynucleotides in a nucleic acid mixture, including, for example, methods for determining the presence or absence of chromosomal alterations (e.g., translocations, deletions, inversions, insertions). Chromosomal alterations are widespread within a population and contribute to phenotypic variation within a population. Certain chromosomal alterations may play a role in the onset and progression of various diseases (e.g., cancer), disorders (e.g., structural malformations, fertility disorders), and functional disorders (e.g., mental dysfunction). Provided herein are systems, methods, and products useful for locating and / or identifying chromosomal alterations and useful for diagnosing and treating diseases, disorders, and functional disorders associated with certain chromosomal alterations.

[0014] Next-generation sequencing allows for genome-wide sequencing of nucleic acids in a manner that is faster and more affordable than traditional sequencing methods. The methods, systems, and products provided herein utilize advanced sequencing technologies to locate and identify chromosomal alterations and / or associated diseases and disorders. The methods, systems, and products provided herein often use a blood sample or a portion thereof to provide a non-invasive assessment of a subject's genome (e.g., a fetal genome), which is often safer, faster, and / or less expensive than invasive techniques (e.g., amniocentesis, biopsy). Some embodiments herein provide a method that includes, in part, obtaining sequence reads (also referred to herein as "sequencing reads") present in a sample, which often map to a reference sequence; identifying certain mapping features of a selected subset of the sequence reads; and determining the presence or absence of chromosomal alterations. Some embodiments herein also provide systems, machines, devices, products, and modules that perform the methods described herein.

[0015] Chromosomal changes Provided herein are methods and systems for identifying the presence or absence of chromosomal alterations of one or more species.As used herein, "chromosomal alterations" refers to any insertion, deletion (e.g., loss), translocation, inversion, and / or fusion of genetic material in one or more human chromosomes.As used herein, the term "genetic material" refers to one or more polynucleotides. Chromosomal alterations can include or are polynucleotide insertions, deletions, and / or translocations of any length, including, non-limiting examples of which include polynucleotides of at least 10 bp, at least 20 bp, at least 50 bp, at least 100 bp, at least 500 bp, at least 1000 bp, at least 2500 bp, at least 5000 bp, at least 10,000 bp, at least 50,000 bp, at least 100,000 bp, at least 500,000 bp, at least 1 megabase pair (Mbp), at least 5 Mbp, at least 10 Mbp, at least 20 Mbp, at least 50 Mbp, at least 100 Mbp, and at least 150 Mbp. In some embodiments, the chromosomal alteration is from about 10 bp to about 200 Mbp, from about 20 bp to about 200 Mbp, from about 50 bp to about 200 Mbp, from about 100 bp to about 200 Mbp, from about 500 bp to about 200 Mbp, from about 1000 bp to about 200 Mbp, from about 2500 bp to about 200 Mbp, from about 5000 bp to about 200 Mbp, from about 10,000 bp to about 200 Mbp, or from about 50,000 bp to about 200 Mbp. p, about 100,000 bp to about 200 Mbp, about 500,000 bp to about 200 Mbp, about 1 Mbp to about 200 Mbp, about 5 Mbp to about 200 Mbp, about 10 Mbp to about 200 Mbp, about 20 Mbp to about 200 Mbp, about 50 Mbp to about 200 Mbp, about 100 Mbp to about 200 Mbp, or about 150 Mbp to about 200 Mbp.In some embodiments, the chromosomal alterations comprise insertions, deletions, and / or translocations in about 1% or more of the chromosomes, about 2% or more of the chromosomes, about 3% or more of the chromosomes, about 4% or more of the chromosomes, about 5% or more of the chromosomes, about 10% or more of the chromosomes, about 15% or more of the chromosomes, about 20% or more of the chromosomes, about 25% or more of the chromosomes, or about 30% or more of the chromosomes. Non-limiting examples of chromosomal alterations that may be detected by the methods and / or systems described herein are described in more detail herein and presented in Table 1 (provided below).

[0016] The chromosomal alteration optionally includes or is an insertion, deletion, and / or translocation of homologous genetic material. Homologous genetic material often includes any suitable polynucleotide that is homologous to a human reference genome or portion thereof. In some embodiments, the chromosomal alteration includes an insertion, deletion, and / or translocation of heterologous genetic material. As used herein, "heterologous genetic material" refers to genetic material derived from any non-human species. Heterologous genetic material optionally includes polynucleotides that are highly homologous to the genome of any non-human species or portion thereof. Examples of heterologous genetic material include viral genomes or portions thereof. Non-limiting examples of viral genera, families, groups, and species that can contribute heterologous genetic material include Herpesviridae, Adenoviridae, Papovaviridae, Anelloviridae, Circoviridae, Parvoviridae, Reoviridae, alpharetroviruses, betaretroviruses, gammaretroviruses, deltaretroviruses, epsilonretroviruses, lentiviruses, pumaviruses, parvoviruses, bornaviruses, circoviruses, and polyomaviruses.

[0017] In some embodiments, the genetic change includes or is a translocation. As used herein, the term "translocation" refers to a chromosomal mutation in which a chromosomal segment changes position. A translocation can be a non-reciprocal translocation or a reciprocal translocation. A non-reciprocal translocation involves the movement of genetic material from one segment of the genome, where genetic material is deleted or copied, to another segment of the genome, where genetic material is inserted. A reciprocal translocation involves the exchange of genetic material from one segment of the genome with another segment of the genome. A translocation can occur within a chromosome (e.g., an intrachromosomal translocation) or between chromosomes (e.g., an interchromosomal translocation). If the exchange of genetic material does not involve the loss or gain of genetic material, the translocation can be a balanced translocation. For example, a balanced translocation is often an exchange of segment x with segment y, where the length and integrity (e.g., sequence) of segments x and y are maintained and no genetic material is added or removed from segments x and / or y. In certain embodiments, a balanced translocation is a non-reciprocal translocation in which a segment of a genome is inserted, but no additional genetic material other than the insert is added or removed at the insertion site. In some embodiments, one or both of the exchanged polynucleotides in a balanced translocation contain one or more genetic variations (e.g., SNPs, microinsertions, microdeletions) as determined by comparison with a reference genome. Such genetic variations are often located internally to the translocated polynucleotide (e.g., not located at or near the breakpoint), and such genetic variations are often not the result of a translocation event. In some embodiments, a translocation is an unbalanced translocation in which the exchange of segment x with segment y involves the loss of genetic material from x and / or y. In some embodiments, an unbalanced translocation involves the exchange of segment x with segment y in which genetic material is added to x and / or y (e.g., duplication, insertion). An unbalanced translocation often involves the gain or loss of genetic material at the ends of the translocated polynucleotide (e.g., at or near the breakpoint of either segment).The presence or absence of a translocation and the location of the translocation breakpoint can be determined by the methods or systems described herein. Determining the presence or absence of a translocation often involves determining the presence or absence of inserted, deleted, and / or exchanged genetic material (e.g., polynucleotides). In some embodiments, the presence or absence of a translocation is determined by the methods and / or systems described herein.

[0018] In certain embodiments, a translocation comprises or is an inversion. Inversions are sometimes referred to herein as "chromosomal inversions." In certain embodiments, an inversion is when a segment of a chromosome is removed and rejoined to the same chromosome in the opposite orientation (e.g., relative to the 5' to 3' DNA strand). In some embodiments of an inversion, the segment rejoins the chromosome at approximately the same site from which it was removed. In certain embodiments of an inversion, the segment rejoins the chromosome at a different site from which it was removed. In certain embodiments, when an inversion occurs, no genetic material is lost or added, although in some cases, phenotypic consequences can be observed if the inversion breakpoint occurs within a gene or within a region that controls the expression of a gene. In some embodiments of an inversion, genetic material is lost or added and can be detected by the methods described herein.

[0019] In some embodiments, the chromosomal change comprises or is an insertion. An insertion is sometimes referred to herein as a "chromosomal insertion." An insertion is sometimes the addition of one or more nucleotide base pairs or polynucleotides to a genome or a segment thereof (e.g., a chromosome). In some embodiments, an insertion refers to the insertion of a large sequence into a chromosome, often resulting from unequal crossing over during meiosis. An insertion sometimes refers to a translocation or part thereof. For example, in a non-reciprocal translocation, a polynucleotide is deleted (deleted) at one site and inserted (e.g., inserted) at another site. In some embodiments, an insertion is independent of a translocation (e.g., an insertion involving the insertion of viral DNA). In some embodiments, an insertion is not a translocation. An insertion often accompanies a translocation. For example, an insertion sometimes includes additional genetic material added during an unbalanced translocation. An insertion associated with an unbalanced translocation is often added between the chromosomal breakpoint and one or both ends of the translocated polynucleotide. An insertion associated with an unbalanced translocation is referred to herein as a "microinsert." The microinsert, or portions thereof, may contain homologous and / or heterologous genetic material. In some embodiments, the microinsert, or portions thereof, contains a nucleic acid or polynucleotide of unknown origin and / or homology. Determining the presence or absence of an insertion often includes determining the presence or absence of a microinsert and / or a translocation (e.g., the presence of a translocated polynucleotide).

[0020] In some embodiments, the microinsert is from about 1 bp to about 10,000 bp, from about 1 bp to about 5000 bp, from about 1 bp to about 1000 bp, from about 1 bp to about 500 bp, from about 1 bp to about 250 bp, from about 1 bp to about 100 bp, from about 1 bp to about 50 bp, or from about 1 bp to about 30 bp. In some embodiments, the polynucleotide insert is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 bp in length.

[0021] In some embodiments, the chromosomal change includes or is a deletion. Herein, a deletion is sometimes referred to as a chromosomal deletion. As used herein, a deletion refers to the absence and / or loss of genetic material (e.g., one or more nucleotide sequences, polynucleotide sequences) at a particular location (e.g., site) of a genome or for a particular sequence of a genome, according to a reference genome. In some embodiments, a deletion refers to the loss of a continuous nucleic acid (e.g., polynucleotide) strand. In some embodiments, a deletion results in the loss of genetic material from a genome. A deletion sometimes refers to a non-reciprocal translocation or a portion thereof. For example, in a non-reciprocal translocation, a polynucleotide is deleted (deleted) at one site and inserted at another site. In some embodiments, a deletion is not a translocation. In some cases, a deletion is independent of a translocation. A deletion is often associated with a translocation. For example, in some cases, a deletion includes genetic material lost during an unbalanced translocation. As used herein, genetic material determined to be absent and / or missing due to a deletion associated with an unbalanced translocation is referred to as a microdeletion. A microdeletion associated with an unbalanced translocation may be lost from one or both ends of a translocated polynucleotide. In some embodiments, a microdeletion associated with an unbalanced translocation is lost from one or both ends of an insertion site. A microdeletion may include loss of genetic material at one or both ends of a breakpoint. In some embodiments, determining the presence of a microdeletion often includes determining the absence of genetic material and / or the presence of a translocation (e.g., the presence of a translocated polynucleotide).

[0022] In some embodiments, the length of the microdeletion is from about 1 bp to about 10,000 bp, from about 1 bp to about 5000 bp, from about 1 bp to about 1000 bp, from about 1 bp to about 500 bp, from about 1 bp to about 250 bp, from about 1 bp to about 100 bp, from about 1 bp to about 50 bp, or from about 1 bp to about 30 bp. In some embodiments, the microdeletion comprises a loss of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 bp.

[0023] sample Provided herein are systems, methods and products for analyzing nucleic acid.In some embodiments, nucleic acid fragments in a mixture of nucleic acid fragments are analyzed.The mixture of nucleic acid can comprise two or more nucleic acid fragment species, and the nucleic acid fragment species have different nucleotide sequences, different fragment lengths, different origins (for example, genomic origin, fetal origin versus maternal origin, cell origin or tissue origin, cancer origin versus non-cancer origin, tumor origin versus non-tumor origin, sample origin, subject origin, etc.), or combinations thereof.

[0024] The nucleic acids or nucleic acid mixtures utilized in the systems, methods, and products described herein are often isolated from samples obtained from subjects. Subjects can be any living or non-living organisms, including, but not limited to, humans, non-human animals, plants, bacteria, fungi, or protists. Any human or non-human animal can be selected, including, but not limited to, mammals, reptiles, birds, amphibians, fish, ungulates, ruminants, bovines (e.g., cows), equines (e.g., horses), caprines and ovines (e.g., sheep, goats), porcines (e.g., pigs), camelids (e.g., camels, llamas, alpacas), monkeys, apes (e.g., gorillas, chimpanzees), ursinians (e.g., bears), poultry, dogs, cats, mice, rats, fish, dolphins, whales, and sharks. Subjects can be male or female (e.g., women, pregnant women). The subject can be of any age (eg, embryo, fetus, infant, child, adult).

[0025] Nucleic acids can be isolated from any type of suitable biological specimen or biological sample (e.g., a test sample). A sample or test sample can be any specimen isolated or obtained from a subject (e.g., a human subject, a pregnant woman, a fetus) or part thereof. Non-limiting examples of specimens include bodily fluids or tissues from a subject, including, but not limited to, blood or blood products (e.g., serum, plasma, etc.), umbilical cord blood, chorionic villi, amniotic fluid, cerebrospinal fluid, spinal fluid, lavage fluid (e.g., bronchoalveolar lavage fluid, gastric lavage fluid, peritoneal lavage fluid, mammary duct lavage fluid, ear lavage fluid, arthroscopic lavage fluid), biopsy samples (e.g., biopsies from preimplantation embryos; cancer biopsies), peritoneal aspirate samples, cells (blood cells, placental cells, embryonic or fetal cells, nucleated fetal cells or remnants of fetal cells) or portions thereof (e.g., mitochondria, nuclei, extracts, etc.), washings of the female reproductive tract, urine, feces, sputum, saliva, nasal mucus, prostatic fluid, lavage fluid, semen, lymph, bile, tears, sweat, breast milk, mammary fluid, etc., or combinations thereof. In some embodiments, the biological sample is a cervical swab from a subject. In some embodiments, the biological sample can be blood, and optionally plasma or serum. As used herein, the term "blood" refers to a blood sample or preparation derived from a pregnant woman or woman being tested for possible pregnancy. The term encompasses whole blood, blood products, or any blood fraction, as conventionally defined, such as serum, plasma, or buffy coat. Blood or a fraction thereof often contains nucleosomes (e.g., maternal and / or fetal nucleosomes). Nucleosomes contain nucleic acids, optionally cell-free or intracellular nucleic acids. Blood also includes buffy coats. Buffy coats are optionally isolated using a Ficoll gradient. Buffy coats may contain white blood cells (e.g., leukocytes, T cells, B cells, platelets, etc.). In certain embodiments, the buffy coat contains maternal and / or fetal nucleic acids. Plasma refers to a fraction of whole blood resulting from centrifugation of anticoagulant-treated blood. Serum refers to the aqueous portion of the fluid that remains after a blood sample has been allowed to clot.Fluid or tissue samples are often collected according to standard protocols commonly followed by hospitals or clinics. For blood, a suitable volume of peripheral blood (e.g., between 3 and 40 milliliters) can often be collected and stored according to standard procedures, either before or after preparation. Fluid or tissue samples from which nucleic acids are extracted can be acellular (e.g., cell-free) samples. In some embodiments, fluid or tissue samples can contain cellular components or cellular remnants. In some embodiments, fetal or cancer cells can be included in the sample.

[0026] The sample may be a liquid sample. The liquid sample may contain extracellular nucleic acids (e.g., circulating cell-free DNA). Non-limiting examples of liquid samples include blood or blood products (e.g., serum, plasma, etc.), umbilical cord blood, amniotic fluid, cerebrospinal fluid, spinal fluid, lavage fluid (e.g., bronchoalveolar, gastric, peritoneal, mammary duct, ear, arthroscope), biopsy samples (e.g., liquid biopsy for detecting cancer), abdominal paracentesis samples, washings of the female genital tract, urine, sputum, saliva, nasal mucus, prostatic fluid, lavage fluid, semen, lymph, bile, tears, sweat, breast milk, mammary fluid, etc., or combinations thereof. In certain embodiments, the sample is a liquid biopsy, which generally refers to the evaluation of a liquid sample from a subject for the presence, absence, progression, or remission of a disease (e.g., cancer). Liquid biopsies can be used in conjunction with or as an alternative to solid biopsies (e.g., tumor biopsies). In certain cases, extracellular nucleic acids are analyzed in liquid biopsies.

[0027] Samples are often heterogeneous, meaning that more than one type of nucleic acid species is present in the sample. For example, heterogeneous nucleic acids can include, but are not limited to, (i) cancer nucleic acids and non-cancer nucleic acids, (ii) pathogen nucleic acids and host nucleic acids, (iii) fetal-derived nucleic acids and maternal-derived nucleic acids, and / or, more generally, (iv) mutant nucleic acids and wild-type nucleic acids. A sample can be heterogeneous due to the presence of more than one cell type, such as fetal cells and maternal cells, cancer cells and non-cancer cells, or pathogenic cells and host cells. In some embodiments, minority and majority nucleic acid species are present.

[0028] In prenatal applications of the technology described herein, bodily fluid or tissue samples can be collected from women of a gestational age suitable for testing, or can be collected from women being tested for possible pregnancy. The appropriate gestational age can vary depending on the prenatal test being performed. In certain embodiments, pregnant subjects are optionally in the first trimester of pregnancy. In the middle of pregnancy, or sometimes in the second trimester of pregnancy or, in some cases, in the third trimester of pregnancy. In certain embodiments, bodily fluids or tissues are collected from a pregnant woman between about 1 and about 45 weeks of gestation (e.g., between 1 and 4, 4 and 8, 8 and 12, 12 and 16, 16 and 20, 20 and 24, 24 and 28, 28 and 32, 32 and 36, 36 and 40, or 40 and 44 weeks of gestation), and optionally between about 5 and about 28 weeks of gestation (e.g., at 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, or 27 weeks of gestation). In certain embodiments, bodily fluid or tissue samples are collected from a pregnant woman at or shortly after delivery (e.g., vaginal or non-vaginal delivery (e.g., operative delivery)).

[0029] Blood sample collection and DNA extraction In some embodiments, the methods herein involve isolating, concentrating, sequencing, and / or analyzing DNA found in the blood of a subject as a non-invasive means of detecting the presence or absence of chromosomal alterations in the subject's genome and / or monitoring the subject's health.

[0030] Blood sample collection Blood samples can be obtained from subjects of any age (e.g., male or female subjects) using the methods of the present technology. Blood samples can be obtained from pregnant women at a gestational age suitable for testing using the methods of the present technology. The appropriate gestational age may vary depending on the disorder being tested, as discussed below. Collection of blood from subjects (e.g., pregnant women) is often performed according to standard protocols commonly followed by hospitals or clinics. For example, an appropriate amount of peripheral blood, typically between 5 and 50 ml, can often be collected and stored according to standard procedures before further preparation. Blood samples can be collected, stored, or transported in a manner that minimizes degradation of nucleic acids present in the sample or their quality.

[0031] Blood sample preparation Analysis of DNA found in a subject's blood can be performed using, for example, whole blood, serum, or plasma. Analysis of fetal DNA found in maternal blood can be performed using, for example, whole blood, serum, or plasma. Methods for preparing serum or plasma from blood obtained from a subject (e.g., a maternal subject) are known. For example, the subject's blood (e.g., a pregnant woman's blood) can be placed in a test tube containing EDTA or in a dedicated commercially available product such as Vacutainer SST (Becton Dickinson, Franklin Lakes, NJ) to prevent blood clotting, and then plasma can be obtained from the whole blood by centrifugation. Serum can be obtained with or without centrifugation (after the blood has been allowed to clot). When centrifugation is used, it is typically, but not necessarily, performed at an appropriate speed, e.g., 1,500 to 3,000 × g. The plasma or serum can be subjected to an additional centrifugation step before being transferred to a fresh test tube to extract DNA.

[0032] In addition to the cell-free portion of whole blood, DNA can also be recovered from the cell-enriched fraction of the buffy coat, which can be obtained after centrifuging a whole blood sample from a woman and removing the plasma.

[0033] DNA extraction Numerous methods are known for extracting DNA from biological samples, including blood. General methods for preparing DNA (e.g., as described by Sambrook and Russell, "Molecular Cloning: A Laboratory Manual," 3rd ed., 2001) can be followed. QIAamp Circulating Nucleic Acid from Qiagen Various commercially available reagents or kits, such as the QiaAmp DNA Mini Kit or QiaAmp DNA Blood Mini Kit (Qiagen, Hilden, Germany), GenomicPrep™ Blood DNA Isolation Kit (Promega, Madison, Wis.), and GFX™ Genomic Blood DNA Purification Kit (Amersham, Piscataway, NJ), can also be used to obtain DNA from a blood sample derived from a subject. A combination of more than one of these methods can also be used.

[0034] In some embodiments, the sample obtained from pregnant woman subject can first be enriched or enriched to some extent for fetal nucleic acid by one or more methods.For example, the differentiation between fetal DNA and maternal DNA can be carried out by using the composition and process of the present technology alone, or can be carried out in combination with other differentiation factors.Examples of these factors include but are not limited to the single nucleotide difference between X chromosome and Y chromosome, Y chromosome specific sequence, polymorphism located in other places in genome, the size difference between fetal DNA and maternal DNA, and the difference in methylation pattern between maternal tissue and fetal tissue.

[0035] Other methods for enriching a sample for specific nucleic acid species are described in PCT Patent Application No. PCT / US07 / 69991, filed May 30, 2007, PCT Patent Application No. PCT / US2007 / 071232, filed June 15, 2007, U.S. Provisional Application No. 60 / 968,876, and U.S. Provisional Application No. 60 / 968,878 (assigned to the present applicant) (PCT Patent Application No. PCT / EP05 / 012707, filed November 28, 2005), all of which are incorporated herein by reference. In certain embodiments, parental nucleic acids are selectively removed (partially, substantially, nearly completely, or completely) from the sample.

[0036] The terms "nucleic acid" and "nucleic acid molecule" may be used interchangeably throughout this disclosure. The terms refer to nucleic acids of any composition, including DNA (e.g., complementary DNA (cDNA), genomic DNA (gDNA)), RNA (e.g., messenger RNA (mRNA), short inhibitory RNA (siRNA), ribosomal RNA (rRNA), tRNA, microRNA, RNA highly expressed by the fetus or placenta, etc.), all of which may be in single- or double-stranded form and, unless otherwise limited, may include known analogs of natural nucleotides that can function in a manner similar to naturally occurring nucleotides, and / or DNA or RNA analogs (e.g., containing base analogs, sugar analogs, and / or non-natural backbones, etc.), RNA / DNA hybrids, and peptide nucleic acids (PNAs). In certain embodiments, a nucleic acid may be or be derived from a plasmid, phage, virus, autonomously replicating sequence (ARS), centromere, artificial chromosome, chromosome, or other nucleic acid capable of replicating in vitro or within a host cell, cell, cell nucleus, or cell cytoplasm. In some embodiments, the template nucleic acid may be derived from a single chromosome (e.g., a nucleic acid sample may be derived from one chromosome of a sample obtained from a diploid organism). Unless specifically limited, the term encompasses nucleic acids containing known analogs of natural nucleotides that have similar binding properties as the reference nucleic acid and are metabolized in a manner similar to naturally occurring nucleotides. Unless otherwise indicated, a particular nucleic acid sequence also implicitly encompasses conservatively modified variants thereof (e.g., degenerate codon substitutions), alleles, orthologs, single nucleotide polymorphisms (SNPs), and complementary sequences, as well as the explicitly indicated sequence. Specifically, degenerate codon substitutions can be achieved by creating sequences in which the third position of one or more selected (or all) codons is substituted with mixed-base and / or deoxyinosine residues. The term nucleic acid is used interchangeably with genetically encoded locus, gene, cDNA, and mRNA.The term may also include, as equivalents, nucleotide analogs, single-stranded ("sense" or "antisense," "plus" or "minus" strand, "forward" or "reverse" reading frame) polynucleotides, and RNA or DNA derivatives, variants, and analogs synthesized from double-stranded polynucleotides. The term "gene" means a segment of DNA involved in producing a polypeptide chain, and includes regions preceding and following the coding region (leader and trailer) that are involved in the transcription / translation of the gene product and the regulation of transcription / translation, and intervening sequences (introns) between individual coding segments (exons).

[0037] Deoxyribonucleotides include deoxyadenosine, deoxycytidine, deoxyguanosine, and deoxythymidine. In RNA, cytosine bases are replaced with uracil. Template nucleic acids can be prepared using nucleic acids obtained from a subject as templates.

[0038] Nucleic Acid Isolation and Processing Nucleic acids can be derived from one or more sources (e.g., cells, serum, plasma, buffy coat, lymph, skin, soil, etc.) by methods known in the art, non-limiting examples of which include DNA preparation methods (e.g., Sambrook and Russell, "Molecular (described in "Cloning: A Laboratory Manual," 3rd ed., 2001), Any suitable method can be used to isolate, extract, and / or purify DNA from a biological sample (e.g., from blood or blood products), including commercially available reagents or kits such as Qiagen's QIAamp Circulating Nucleic Acid Kit, QiaAmp DNA Mini Kit, or QiaAmp DNA Blood Mini Kit (Qiagen, Hilden, Germany), GenomicPrep™ Blood DNA Isolation Kit (Promega, Madison, Wis.), and GFX™ Genomic Blood DNA Purification Kit (Amersham, Piscataway, NJ), or combinations thereof.

[0039] Cell lysis procedures and cell lysis reagents are known in the art and generally can be performed by chemical (e.g., detergents, hypotonic solutions, enzymatic procedures, etc., or a combination thereof), physical (e.g., French press, sonication, etc.), or electrolytic lysis. Any suitable lysis procedure can be utilized. For example, chemical methods generally employ a lysis agent to disrupt cells and extract nucleic acids from the cells, followed by treatment with a chaotropic salt. Physical methods such as freeze / thaw followed by crushing or the use of a cell press are also useful. High-salt lysis procedures are also commonly used. For example, alkaline lysis procedures can be utilized. The latter procedures traditionally incorporate the use of phenol-chloroform solutions, although alternative phenol-chloroform-free procedures involving three solutions can also be utilized. In the latter procedure, one solution may contain 15 mM Tris, pH 8.0, 10 mM EDTA, and 100 μg / ml Rnase A; a second solution may contain 0.2 N NaOH and 1% SDS, and a third solution may contain 3 M KOAc, pH 5.5. These procedures are described in "Current Protocols in Molecular Biology," John Wiley & Sons, NY, which are incorporated herein in their entirety. , Sections 6.3.1 to 6.3.6 (1989).

[0040] The nucleic acid can be isolated at different times compared to another nucleic acid, where each sample is derived from the same source or from different sources. The nucleic acid can be derived from a nucleic acid library, such as a cDNA library or an RNA library. The nucleic acid can be the result of purifying or isolating nucleic acids from a sample and / or amplifying nucleic acid molecules. Nucleic acids provided for the processes described herein can contain nucleic acids from one sample or from two or more samples (e.g., from one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more samples).

[0041] In certain embodiments, nucleic acids can include extracellular nucleic acids. As used herein, the term "extracellular nucleic acid" can refer to nucleic acids isolated from a source substantially free of cells, and is also referred to as "cell-free" nucleic acid, "circulating cell-free nucleic acid" (e.g., CCF fragments, ccf DNA), and / or "cell-free circulating nucleic acid." Extracellular nucleic acids can be present in blood and can be obtained from blood (e.g., human blood, e.g., from the blood of a pregnant woman). Extracellular nucleic acids often do not contain detectable cells and can contain cellular components or remnants of cells. Non-limiting examples of cell-free sources for extracellular nucleic acids are blood, plasma, serum, and urine. As used herein, the term "obtaining cell-free circulating sample nucleic acids" includes obtaining a sample directly (e.g., collecting a sample, e.g., a test sample) or obtaining a sample from another party who collected the sample. Without being limited by theory, extracellular nucleic acids can be products of cellular apoptosis and cell destruction, providing a basis for extracellular nucleic acids that often have a series of lengths (e.g., a "ladder") across a spectrum.

[0042] Extracellular nucleic acids can contain different nucleic acid species, and are therefore referred to herein as "heterogeneous" in certain embodiments. For example, serum or plasma from a patient with cancer can contain nucleic acids from cancer cells and nucleic acids from non-cancer cells. In another example, serum or plasma from a pregnant woman can contain maternal nucleic acids and fetal nucleic acids. In some cases, fetal nucleic acids are about 5% to about 50% of the total nucleic acids (e.g., about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, or 49% of the total nucleic acids are fetal nucleic acids). In some embodiments, the majority of the fetal nucleic acids in the nucleic acid are about 500 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the fetal nucleic acids are about 500 base pairs or less in length). In some embodiments, the majority of the fetal nucleic acids in the nucleic acid are about 250 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the fetal nucleic acids are about 250 base pairs or less in length). In some embodiments, the majority of the fetal nucleic acids in the nucleic acid are about 200 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the fetal nucleic acids are about 200 base pairs or less in length). In some embodiments, the majority of the fetal nucleic acids in the nucleic acid are about 150 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the fetal nucleic acids are about 150 base pairs or less in length). In some embodiments, the majority of the fetal nucleic acids in the nucleic acid are about 100 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the fetal nucleic acids are about 100 base pairs or less in length).In some embodiments, the majority of the fetal nucleic acids in the nucleic acid are about 50 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the fetal nucleic acids are about 50 base pairs or less in length). In some embodiments, the majority of the fetal nucleic acids in the nucleic acid are about 25 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the fetal nucleic acids are about 25 base pairs or less in length).

[0043] In certain embodiments, nucleic acids can be prepared by performing the methods described herein without processing a sample containing the nucleic acid. In some embodiments, nucleic acids are prepared by performing the methods described herein after processing a sample containing the nucleic acid. For example, nucleic acids can be extracted, isolated, purified, partially purified, or amplified from a sample. As used herein, the term "isolated" refers to nucleic acid that has been removed from its original environment (e.g., from a natural environment if naturally occurring, or from a host cell if exogenously expressed) and thus has been altered from its original environment by human intervention (e.g., "artificial"). As used herein, the term "isolated nucleic acid" can refer to nucleic acid that has been removed from a subject (e.g., a human subject). Isolated nucleic acids can be prepared with less non-nucleic acid components (e.g., proteins, lipids) than the amount of those components present in the source sample. A composition comprising isolated nucleic acids can be about 50% to greater than 99% free of non-nucleic acid components. A composition comprising an isolated nucleic acid can be about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or greater than 99% free of non-nucleic acid components. As used herein, the term "purified" can refer to a nucleic acid that has been prepared to contain fewer non-nucleic acid components (e.g., proteins, lipids, carbohydrates) than the amount of non-nucleic acid components present before the nucleic acid is subjected to a purification procedure. A composition comprising a purified nucleic acid can be about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or greater than 99% free of other non-nucleic acid components. As used herein, the term "purified" can refer to a nucleic acid that has been prepared to contain fewer nucleic acid species than in the sample source from which the nucleic acid is derived. A composition containing purified nucleic acid can be about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or greater than 99% free of other nucleic acid species. For example, fetal nucleic acid can be purified from a mixture containing maternal and fetal nucleic acids. In certain instances, small fragments of fetal nucleic acid (e.g., fragments of 30-500 bp) can be purified or partially purified from a mixture containing both fetal and maternal nucleic acid fragments.In certain instances, nucleosomes containing small fragments of fetal nucleic acid can be purified from a mixture of large nucleosome complexes containing large fragments of maternal nucleic acid. In certain instances, cancer cell nucleic acid can be purified from a mixture containing cancer cell nucleic acid and non-cancer cell nucleic acid. In certain instances, nucleosomes containing small fragments of cancer cell nucleic acid can be purified from a mixture of large nucleosome complexes containing large fragments of non-cancer nucleic acid.

[0044] In some embodiments, nucleic acids are sheared or cleaved before, during, or after the methods described herein. The nominal, average, or mean length of the sheared or cleaved nucleic acids can be about 5 to about 10,000 base pairs, about 100 to about 1,000 base pairs, about 100 to about 500 base pairs, or about 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, or 9000 base pairs. Sheared or cleaved nucleic acids can be produced by any suitable method known in the art, and the average, mean, or nominal length of the resulting nucleic acid fragments can be controlled by selecting the appropriate fragment generation method.

[0045] In some embodiments, nucleic acids are sheared or cleaved by a suitable method, non-limiting examples of which include physical methods (e.g., shearing methods, e.g., sonication, French press, heating, UV irradiation, etc.), enzymatic processes (e.g., enzymatic cleavage agents (e.g., suitable nucleases, suitable restriction enzymes, suitable methylation-sensitive restriction enzymes)), chemical methods (e.g., alkylation, DMS, piperidine, acid hydrolysis, base hydrolysis, heating, etc., or combinations thereof), processes described in U.S. Patent Application Publication No. 20050112590, etc., or combinations thereof.

[0046] As used herein, "shearing" or "cleavage" refers to a procedure or condition that can severe a nucleic acid molecule, such as a nucleic acid template gene molecule or its amplification product, into two or more smaller nucleic acid molecules. Such shearing or cleavage can be sequence-specific, base-specific, or non-specific, and can be achieved by any of a variety of methods, reagents, or conditions, including, for example, chemical shearing, enzymatic shearing, or physical shearing (e.g., physical fragmentation). As used herein, "cleavage product," "cleaved product," or grammatical variations thereof, refers to a nucleic acid molecule that results from shearing or cleaving a nucleic acid or its amplification product.

[0047] As used herein, the term "amplification" refers to subjecting a target nucleic acid in a sample to a process that linearly or exponentially produces amplicon nucleic acids having the same or substantially the same nucleotide sequence as the target nucleic acid or a segment thereof. In certain embodiments, the term "amplification" refers to a method that includes the polymerase chain reaction (PCR). For example, an amplification product may contain one or more nucleotides beyond the nucleotide region of the nucleic acid template sequence that is being amplified (e.g., a primer may contain "additional" nucleotides, such as a transcription initiation sequence, in addition to nucleotides complementary to the nucleic acid template gene molecule, resulting in an amplification product that contains "additional" nucleotides or nucleotides that do not correspond to the nucleotide region of the nucleic acid template gene molecule that is being amplified).

[0048] As used herein, the term "complementary cleavage reactions" refers to cleavage reactions carried out on the same nucleic acid using different cleavage reagents or by varying the cleavage specificity of the same cleavage reagent to generate alternating cleavage patterns of the same target or reference nucleic acid or target or reference protein. In certain embodiments, a nucleic acid can be treated with one or more specific cleavage agents (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more specific cleavage agents) in one or more reaction vessels (e.g., the nucleic acid is treated with each specific cleavage agent in a separate vessel). As used herein, the term "specific cleavage agent" refers to an agent, optionally a chemical or enzyme, that can cleave a nucleic acid at one or more specific sites.

[0049] Nucleic acids can also be exposed to a process that modifies certain nucleotides within the nucleic acid before preparing the nucleic acid for the methods described herein. For example, a process that selectively modifies nucleic acids based on the methylation state of the nucleotides therein can be applied to the nucleic acid. In addition, conditions such as high temperature, ultraviolet light, and x-rays can also induce changes in the sequence of nucleic acid molecules. Nucleic acids can be prepared in any suitable form that is useful for performing appropriate sequence analysis.

[0050] Nucleic acids can be single-stranded or double-stranded. For example, single-stranded DNA can be created by denaturing double-stranded DNA, for example, through heating or alkali treatment. In certain embodiments, the nucleic acid is a D-loop structure formed through strand invasion of a double-stranded DNA molecule by a DNA-like molecule, such as an oligonucleotide or peptide nucleic acid (PNA). The formation of a D-loop can be facilitated by adding the RecA protein of E. coli and / or by changing the salt concentration, for example, using methods known in the art.

[0051] Contrast between few species and many species At least two different nucleic acid species can be present in different amounts within extracellular (e.g., circulating cell-free) nucleic acids, sometimes referred to as minor and major species. In certain instances, the minor nucleic acid species originates from a diseased cell type (e.g., cancer cells, wasted cells, cells attacked by the immune system). In certain embodiments, chromosomal alterations are determined for the minority nucleic acid species. In certain embodiments, chromosomal alterations are determined for the majority nucleic acid species. As used herein, the terms "minority" and "majority" are not intended to be rigidly defined in any way. In one aspect, the abundance of a nucleic acid considered "minor" can be, for example, at least about 0.1% of the total nucleic acids in the sample to less than 50% of the total nucleic acids in the sample. In some embodiments, the abundance of a minority nucleic acid can be at least about 1% of the total nucleic acids in the sample to about 40% of the total nucleic acids in the sample. In some embodiments, the abundance of a minority nucleic acid can be at least about 2% of the total nucleic acids in the sample to about 30% of the total nucleic acids in the sample. In some embodiments, the abundance of minority nucleic acids can be at least about 3% of the total nucleic acids in the sample to about 25% of the total nucleic acids in the sample. For example, the abundance of minority nucleic acids can be about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, or 30% of the total nucleic acids in the sample. In some cases, the minority species of extracellular nucleic acids is, optionally, about 1% to about 40% of the total nucleic acids (e.g., about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40% of the nucleic acids are minority species of nucleic acids). In some embodiments, the minority nucleic acid is extracellular DNA. In some embodiments, the minority nucleic acid is extracellular DNA derived from apoptotic tissue. In some embodiments, the minority nucleic acid is extracellular DNA derived from tissue affected by a cell proliferative disorder. In some embodiments, the minority nucleic acid is extracellular DNA derived from tumor cells.In some embodiments, the minority nucleic acid is extracellular fetal DNA.

[0052] In another aspect, the abundance of a nucleic acid considered "major" can be, for example, greater than 50% of all nucleic acids in the sample to about 99.9% of all nucleic acids in the sample. In some embodiments, the abundance of the majority nucleic acid can be at least about 60% of all nucleic acids in the sample to about 99% of all nucleic acids in the sample. In some embodiments, the abundance of the majority nucleic acid can be at least about 70% of all nucleic acids in the sample to about 98% of all nucleic acids in the sample. In some embodiments, the abundance of the majority nucleic acid can be at least about 75% of all nucleic acids in the sample to about 97% of all nucleic acids in the sample. For example, the abundance of the majority nucleic acid can be at least about 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the total nucleic acids in the sample. In some embodiments, the majority nucleic acid is extracellular DNA. In some embodiments, the majority nucleic acid is extracellular maternal DNA. In some embodiments, the majority nucleic acid is DNA derived from healthy tissue. In some embodiments, the majority nucleic acid is DNA derived from non-tumor cells.

[0053] In some embodiments, a minority of the extracellular nucleic acids are about 500 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the minority of nucleic acids are about 500 base pairs or less in length). In some embodiments, a minority of the extracellular nucleic acids are about 300 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the minority of nucleic acids are about 300 base pairs or less in length). In some embodiments, a minority of the extracellular nucleic acids are about 200 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the minority of nucleic acids are about 200 base pairs or less in length). In some embodiments, a minority of the extracellular nucleic acids are about 150 base pairs or less in length (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the minority of nucleic acids are about 150 base pairs or less in length).

[0054] cell type As used herein, "cell type" refers to a type of cell that can be distinguished from another cell type. Extracellular nucleic acid can include nucleic acids from multiple different cell types. Non-limiting examples of cell types that can contribute nucleic acids to circulating cell-free nucleic acid include liver cells (e.g., hepatocytes), lung cells, spleen cells, pancreatic cells, colon cells, skin cells, bladder cells, eye cells, brain cells, esophageal cells, head cells, cervical cells, ovarian cells, testicular cells, prostate cells, placental cells, epithelial cells, endothelial cells, adipocytes, kidney / renal cells, cardiac cells, muscle cells, blood cells (e.g., leukocytes), central nervous system (CNS) cells, etc., and combinations of the foregoing. In some embodiments, cell types that contribute nucleic acids to the circulating cell-free nucleic acid being analyzed include leukocytes, endothelial cells, and hepatocytes. The different cell types can be screened as part of identifying and selecting nucleic acid loci for which the marker status is the same, or substantially the same, in cell types in subjects with and without a medical condition, as described in further detail herein.

[0055] A particular cell type may be the same or remain substantially the same in a subject with a medical condition as in a subject without the medical condition. In a non-limiting example, the number of viable or living cells of a particular cell type may be reduced in a cytopathic condition, but the viable cells are not altered or significantly altered in a subject with a medical condition.

[0056] A particular cell type may be altered as part of a medical condition, sometimes possessing one or more characteristics that differ from its original state. In non-limiting examples, a particular cell type may proliferate at a rate greater than normal, may transform into a cell having a different shape, may transform into a cell that expresses one or more different cell surface markers, and / or may become part of a tumor as part of a cancerous condition. In embodiments in which a particular cell type (i.e., a progenitor cell) is altered as part of a medical condition, the marker state is often the same, or substantially the same, for each of one or more markers assayed between a particular cell type in a subject with a medical condition and a particular cell type in a subject without the medical condition. Thus, the term "cell type" may refer to both the type of cell in a subject without a medical condition and the modified version of the cell in a subject with a medical condition. In some embodiments, a "cell type" refers only to progenitor cells, not modified versions that arise from progenitor cells. "Cell type" sometimes refers to progenitor cells and modified cells that arise from progenitor cells. In such embodiments, the marker state for the marker being analyzed is often the same or substantially the same in a cell type in a subject with a medical condition and a cell type in a subject without a medical condition.

[0057] In certain embodiments, the cell type is a cancer cell. Certain cancer cell types include, for example, leukemia cells (e.g., acute myeloid leukemia, acute lymphoblastic leukemia, chronic myeloid leukemia, chronic lymphoblastic leukemia); cancerous kidney / renal cells (e.g., renal cell carcinoma (clear cell renal cell carcinoma, papillary 1 renal cell carcinoma, papillary 2 renal cell carcinoma, chromophobe renal cell carcinoma, oncocytoma, collecting duct carcinoma), renal adenocarcinoma, adrenal gland tumor, Wilms' tumor, transitional cell carcinoma); brain tumor cells (e.g., acoustic neuroma, astrocytoma (grade I: pilocytic astrocytoma), Grade II: low-grade astrocytoma, Grade III: anaplastic astrocytoma, Grade IV: glioblastoma (GBM), chordoma, CNS lymphoma, craniopharyngioma, glioma (brain stem glioma, ependymoma, mixed glioma, optic nerve glioma, subependymoma), medulloblastoma, meningioma, metastatic brain tumor, oligodendroglioma, pituitary tumor, primitive neuroectodermal tumor (PNET), schwannoma, juvenile pilocytic astrocytoma (JPA), pineal tumor, rhabdoid tumor).

[0058] Different cell types can be distinguished by any suitable characteristics, including, but not limited to, one or more different cell surface markers, one or more different morphological features, one or more different functions, one or more protein (e.g., histone) modifications, and one or more different nucleic acid markers. Non-limiting examples of nucleic acid markers include single nucleotide polymorphisms (SNPs), methylation status of nucleic acid loci, short tandem repeats, insertions (e.g., microinserts), deletions (microdeletions), and combinations thereof. Non-limiting examples of protein (e.g., histone) modifications include acetylation, methylation, ubiquitination, phosphorylation, sumoylation, and combinations thereof.

[0059] As used herein, the term "related cell type" refers to a cell type that shares multiple characteristics with another cell type, where related cell types optionally share 75% or more of their cell surface markers (e.g., about 80%, 85%, 90%, or 95% or more of their cell surface markers with the related cell type).

[0060] Enrichment and separation of nucleic acid subpopulations In some embodiments, nucleic acids (e.g., extracellular nucleic acids) are enriched or partially enriched for a subpopulation or nucleic acid species. Subpopulations of nucleic acids can include, for example, fetal nucleic acids, maternal nucleic acids, nucleic acids comprising fragments of a particular length or length range, or nucleic acids derived from a particular genomic region (e.g., a single chromosome, a set of chromosomes, and / or a particular chromosomal region). Such enriched samples can be used with the methods provided herein. The methods herein optionally include isolating, enriching, and analyzing fetal DNA found in maternal blood as a non-invasive means of detecting the presence or absence of chromosomal alterations in the mother and / or fetus. Thus, in certain embodiments, the methods of the present technology include further enriching for a nucleic acid subpopulation in the sample, e.g., fetal nucleic acids. In certain embodiments, the methods described herein for determining the fetal fraction can also be used to enrich for fetal nucleic acids. In certain embodiments, maternal nucleic acids are selectively removed (partially, substantially, nearly completely, or completely) from the sample. In certain embodiments, by enriching for specific low copy number nucleic acid (for example, fetal nucleic acid), can improve quantitative sensitivity.The method for enriching sample for specific nucleic acid species is described in, for example, United States Patent No. 6,927,028, International Patent Application Publication No. WO2007 / 140417, International Patent Application Publication No. WO2007 / 147063, International Patent Application Publication No. WO2009 / 032779, International Patent Application Publication No. WO2009 / 032781, International Patent Application Publication No. WO2010 / 033639, International Patent Application Publication No. WO2011 / 034631, International Patent Application Publication No. WO2006 / 056480 and International Patent Application Publication No. WO2011 / 143659, all of which are incorporated herein by reference.

[0061] In certain embodiments, a subset of nucleic acid fragments is selected prior to sequencing. In certain embodiments, hybridization-based techniques (e.g., using oligonucleotide arrays) can be used to first select for nucleic acid sequences derived from certain chromosomes (e.g., sex chromosomes and / or chromosomes suspected of containing chromosomal alterations). In some embodiments, nucleic acids can be fractionated by size (e.g., by gel electrophoresis, size exclusion chromatography, or microfluidics-based techniques), and in certain cases, fetal nucleic acids can be enriched by selecting for nucleic acids with low molecular weights (e.g., less than 300 base pairs, less than 200 base pairs, less than 150 base pairs, less than 100 base pairs). In some embodiments, fetal nucleic acids can be enriched by suppressing maternal background nucleic acids, such as through the addition of formaldehyde. In some embodiments, a portion or subset of a preselected set of nucleic acid fragments is randomly sequenced. In some embodiments, the nucleic acids are amplified prior to sequencing. In some embodiments, a portion or subset of the nucleic acids is amplified prior to sequencing.

[0062] Nucleic Acid Library In some embodiments, a nucleic acid library is a plurality of polynucleotide molecules (e.g., a sample of nucleic acids) that are prepared, assembled, and / or modified for a specific process, non-limiting examples of which include immobilization on a solid phase (e.g., a solid support, e.g., a flow cell, a bead), enrichment, amplification, cloning, detection, and / or nucleic acid sequencing. In certain embodiments, the nucleic acid library is prepared before or during the sequencing process. The nucleic acid library (e.g., a sequencing library) can be prepared by any suitable method known in the art. The nucleic acid library can be prepared by a targeted preparation process or a non-targeted preparation process.

[0063] In some embodiments, nucleic acid libraries are modified to include chemical moieties (e.g., functional groups) configured for immobilizing the nucleic acids to a solid support. In some embodiments, nucleic acid libraries are modified to include biomolecules (e.g., functional groups) and / or binding pair members configured for immobilizing the library to a solid support, non-limiting examples of which include thyroxin-binding globulin, steroid-binding proteins, antibodies, antigens, haptens, enzymes, lectins, nucleic acids, inhibitors, protein A, protein G, avidin, streptavidin, biotin, complement component C1q, nucleic acid-binding proteins, receptors, carbohydrates, oligonucleotides, polynucleotides, complementary nucleic acid sequences, etc., and combinations thereof. Some examples of specific binding pairs include, but are not limited to, an avidin moiety and a biotin moiety; an antigenic epitope and an antibody or immunoreactive fragment thereof; an antibody and a hapten; a digoxigenin moiety and an anti-digoxigenin antibody; a fluorescein moiety and an anti-fluorescein antibody; an agonist and an inhibitor; a nuclease and a nucleotide; a lectin and a polysaccharide; a steroid and a steroid-binding protein; an active compound and an active compound receptor; a hormone and a hormone receptor; an enzyme and a substrate; an immunoglobulin and protein A; an oligonucleotide or a polynucleotide and its corresponding complement;

[0064] In some embodiments, nucleic acid libraries are modified to contain one or more species of polynucleotides of known composition, non-limiting examples of which include identifiers (e.g., tags, indicator tags), capture sequences, labels, adapters, restriction enzyme sites, promoters, enhancers, origins of replication, stem-loops, complementary sequences (e.g., primer binding sites, annealing sites), suitable integration sites (e.g., transposons, viral integration sites), modified nucleotides, etc., or combinations thereof. Polynucleotides of known sequence can be added at any appropriate position, for example, on the 5' end, the 3' end, or within the nucleic acid sequence. Polynucleotides of known sequence can have the same sequence or different sequences. In some embodiments, polynucleotides of known sequence are configured to hybridize with one or more species of oligonucleotides immobilized on a surface (e.g., a surface within a flow cell). For example, nucleic acid molecules containing a known 5' sequence can be hybridized with a first plurality of oligonucleotides, while the known 3' sequence can be hybridized with a second plurality of oligonucleotides. In some embodiments, the nucleic acid library may include chromosome-specific tags, capture sequences, labels, and / or adapters. In some embodiments, the nucleic acid library includes one or more types of detection labels. In some embodiments, one or more types of detection labels can be incorporated into the nucleic acid library at the 5' end, the 3' end, and / or at any nucleotide position within the nucleic acids in the library. In some embodiments, the nucleic acid library includes hybridized oligonucleotides. In certain embodiments, the hybridized oligonucleotides are labeled probes. In some embodiments, the nucleic acid library includes hybridized oligonucleotide probes prior to immobilization on a solid phase.

[0065] In some embodiments, the polynucleotide whose sequence is known comprises a universal sequence. A universal sequence is a specific nucleic acid sequence incorporated into two or more nucleic acid molecules, or two or more subsets of nucleic acid molecules, where the universal sequence is the same for all molecules or subsets of molecules into which it is incorporated. Universal sequences are often designed to hybridize with and / or amplify multiple different sequences using a single universal primer complementary to the universal sequence. In some embodiments, two (e.g., pairs) or more universal sequences and / or universal primers are used. Universal primers often comprise a universal sequence. In some embodiments, an adapter (e.g., a universal adapter) comprises a universal sequence. In some embodiments, the universal sequences of one or more species are used to capture, identify, and / or detect multiple species or subsets of nucleic acids.

[0066] In certain embodiments, nucleic acid libraries are prepared (e.g., in certain sequencing via synthesis procedures), the nucleic acids are size selected and / or fragmented (e.g., in preparation to create the library) to lengths of a few hundred base pairs or less. In some embodiments, library preparation is performed without fragmentation (e.g., when using ccfDNA).

[0067] In certain embodiments, ligation-based library preparation method is used.Non-limiting examples of ligation-based library preparation method and kit include TRUSEQ or ScriptMiner, Illumina, San Diego CA; KAPA Library Preparation Kit, KAPA Biosystems, Inc., Woburn, MA; NEBNext, New England Biolabs, Ipswich, MA; MuSeek, Thermo Fisher Scientific, Waltham, MA; NxSeq® DNA Sample Prep Kits, Lucigen Corp., Middleton, WI; PureGenome, EMD Millipore, Billerica, MA etc.).Ligation-based library preparation method often uses adapter design, which can incorporate index sequence in early ligation step, and can often be used to prepare sample for single-read sequencing, paired-end sequencing and multiplexed sequencing. For example, in some cases, nucleic acids (e.g., fragmented nucleic acids or ccfDNA) are end-repaired by a fill-in reaction, an exonuclease reaction, or a combination thereof. In some embodiments, the resulting blunt-end-repaired nucleic acid is then extended with a single nucleotide complementary to the single nucleotide overhang on the 3' end of the adapter / primer. Any nucleotide can be used for the extension / overhang nucleotide. In some embodiments, preparing a nucleic acid library includes ligating an adapter oligonucleotide. The adapter oligonucleotide is often complementary to a flow cell anchor and is sometimes used to immobilize the nucleic acid library to a solid support, such as the inner surface of a flow cell.In some embodiments, the adapter oligonucleotide comprises an identifier, a hybridization site for one or more sequencing primers (e.g., a sequence complementary to a universal sequencing primer, a single-end sequencing primer, a paired-end sequencing primer, a multiplexed sequencing primer, etc.), or a combination thereof (e.g., adapter / sequencing, adapter / identifier, adapter / identifier / sequencing).

[0068] The identifier can be a suitable detection label incorporated into or attached to a nucleic acid (e.g., a polynucleotide) that allows for detection and / or identification of the nucleic acid containing the identifier. In some embodiments, the identifier is incorporated into or attached to a nucleic acid in a sequencing method (e.g., polymerase-mediated). Non-limiting examples of identifiers include nucleic acid tags, nucleic acid indexes, or barcodes, radioactive labels (e.g., isotopes), metal labels, fluorescent labels, chemiluminescent labels, phosphorescent labels, fluorophore quenchers, dyes, proteins (e.g., enzymes, antibodies or portions thereof, linkers, members of binding pairs), etc., or combinations thereof. In some embodiments, the identifier (e.g., nucleic acid index or barcode) is a unique, known, and / or distinguishable sequence of nucleotides or nucleotide analogs. In some embodiments, the identifier is six or more consecutive nucleotides. Numerous fluorophores are available with a variety of different excitation and emission spectra. Any suitable type and / or number of fluorophores can be used as identifiers. In some embodiments, one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, twenty or more, thirty or more, or fifty or more different identifiers are utilized in the methods described herein (e.g., nucleic acid detection and / or nucleic acid sequencing methods). In some embodiments, one or two types of identifiers (e.g., fluorescent labels) are linked to each nucleic acid in the library.Detection and / or quantification of the identifiers may be performed by any suitable method, device, or machine, non-limiting examples of which include flow cytometry, quantitative polymerase chain reaction (qPCR), gel electrophoresis, luminometer, fluorometer, spectrophotometer, suitable gene chip or microarray analysis, Western blot, mass spectrometry, chromatography, cytofluorimetric analysis, fluorescence microscopy, suitable fluorescence or digital imaging methods, confocal laser scanning microscopy, laser scanning cytometry, affinity chromatography, manual batch separation, electrosuspension, suitable nucleic acid sequencing methods and / or nucleic acid sequencing devices (e.g., sequencing machines, e.g., sequencers), and the like, and combinations thereof.

[0069] In some embodiments, transposon-based library preparation methods (e.g., EPICENTRE NEXTERA, Epicentre, Madison WI) are used. Transposon-based methods typically use simultaneous in vitro transposition of fragment DNA and tag DNA in a single-tube reaction (often allowing for the incorporation of platform-specific tags and optional barcodes) to prepare libraries for sequencing.

[0070] In some embodiments, the nucleic acid library or a portion thereof is amplified (e.g., amplified by a PCR-based method). In some embodiments, the sequencing method includes amplification of the nucleic acid library. The nucleic acid library can be amplified before or after immobilization on a solid support (e.g., a solid support in a flow cell). Nucleic acid amplification includes a process that amplifies or increases the number of nucleic acid templates and / or their complements present (e.g., in a nucleic acid library) by providing one or more copies of the templates and / or their complements. Amplification can be carried out by any suitable method. The nucleic acid library can be amplified by thermocycling or isothermal amplification. In some embodiments, rolling circle amplification is used. In some embodiments, amplification is carried out on a solid support (e.g., in a flow cell) to which the nucleic acid library or a portion thereof is immobilized. In certain sequencing methods, the nucleic acid library is added to a flow cell and immobilized to anchors by hybridization under appropriate conditions. This type of nucleic acid amplification is often referred to as solid-phase amplification. In some embodiments of solid-phase amplification, all or part of the amplification product is synthesized by extension starting from an immobilized primer. Solid-phase amplification reactions are similar to standard liquid-phase amplification, except that at least one of the amplification oligonucleotides (e.g., primers) is immobilized on a solid support.

[0071] In some embodiments, solid-phase amplification includes a nucleic acid amplification reaction that includes only one oligonucleotide primer species immobilized to a surface. In certain embodiments, solid-phase amplification includes multiple different immobilized oligonucleotide primer species. In some embodiments, solid-phase amplification can include a nucleic acid amplification reaction that includes one oligonucleotide primer species immobilized on a solid surface and a second, different oligonucleotide primer species in solution. Multiple different species of immobilized primers or solution-based primers can be used. Non-limiting examples of solid-phase nucleic acid amplification reactions include interface amplification, bridge amplification, emulsion PCR, WildFire amplification (e.g., U.S. Patent Publication No. US20130012399), etc., or combinations thereof.

[0072] Sequencing In some embodiments, nucleic acids (e.g., nucleic acid fragments, sample nucleic acids, cell-free nucleic acids) can be sequenced. In some embodiments, full-length sequences or substantially full-length sequences are obtained, and in some cases, partial sequences are obtained. Methods for sequencing, mapping, and related analysis are described herein and known in the art (e.g., U.S. Patent Application Publication No. US2009 / 0029377, which is incorporated by reference). Certain aspects of such processes are described herein below.

[0073] Any suitable method for sequencing nucleic acids can be used, including, but not limited to, Maxim-Gilbert, chain termination, sequencing by synthesis, sequencing by ligation, sequencing by mass spectrometry, microscopy-based techniques, or a combination thereof. In some embodiments, first-generation technologies, such as Sanger sequencing, including automated Sanger sequencing, including microfluidic Sanger sequencing, can be used in the methods provided herein. In some embodiments, sequencing techniques can be used, including the use of nucleic acid imaging techniques (e.g., transmission electron microscopy (TEM) and atomic force microscopy (AFM)). In some embodiments, high-throughput sequencing methods are used. High-throughput sequencing methods generally involve clonally amplified DNA templates or single DNA molecules, which are optionally sequenced in a massively parallel manner in a flow cell. The methods described herein can use next-generation (e.g., second- and third-generation) sequencing methods capable of sequencing DNA in a massively parallel manner, collectively referred to herein as "massively parallel sequencing" (MPS). In some embodiments, MPS sequencing methods utilize targeting methods that generate sequence reads from specific chromosomes, genes, or regions of interest. In some cases, specific chromosomes, genes, or regions of interest are referred to herein as targeted genomic regions. In certain embodiments, non-targeting methods are used that randomly sequence, amplify, and / or capture most or all nucleic acid fragments (e.g., ccf fragments, ccf DNA, polynucleotides) in a sample.

[0074] MPS sequencing may use sequencing by synthesis and certain imaging processes.The nucleic acid sequencing technology that can be used in the methods described herein is sequencing by synthesis and reversible terminator-based sequencing (for example, Illumina's Genome Analyzer; Genome Analyzer II; HISEQ 2000; HISEQ 2500 (Illumina, San Diego CA)).This technology can sequence millions of nucleic acid (for example, DNA) fragments in parallel.One example of this type of sequencing technology uses a flow cell that contains an optically transparent slide with eight individual lanes, and has oligonucleotide anchors (for example, adapter primers) attached to its surface.

[0075] In some embodiments, sequencing by synthesis involves the iterative addition (e.g., by covalent addition) of nucleotides to a primer or an existing nucleic acid strand in a template-directed manner. Each iterative addition of nucleotides is detected, and the process is repeated multiple times until the sequence of the nucleic acid strand is obtained. The length of the resulting sequence depends, in part, on the number of addition and detection steps performed. In some sequencing-by-synthesis embodiments, one, two, three, or more nucleotides of the same type (e.g., A, G, C, or T) are added and detected in a single round of nucleotide addition. Nucleotides can be added by any suitable method (e.g., enzymatically or chemically). For example, in some embodiments, nucleotides are added to a primer or an existing nucleic acid strand in a template-directed manner by a polymerase or ligase. Some sequencing-by-synthesis embodiments use different types of nucleotides, nucleotide analogs, and / or identifiers. In some embodiments, reversible terminators and / or removable (e.g., cleavable) identifiers are used. In some embodiments, fluorescently labeled nucleotides and / or nucleotide analogs are used. In certain embodiments, sequencing by synthesis includes a cleavage step (e.g., cleavage and removal of identifiers) and / or a washing step. In some embodiments, the addition of one or more nucleotides is detected by a suitable method described herein or known in the art, including, but not limited to, any suitable imaging device or imaging machine, suitable camera, digital camera, CCD (charge-coupled device)-based imaging device (e.g., CCD camera), CMOS (complementary metal oxide silicon)-based imaging device (e.g., CMOS camera), photodiode (e.g., photomultiplier tube), electron microscopy, field-effect transistor (e.g., DNA field-effect transistor), ISFET ion sensor (e.g., CHEMFET sensor), etc., or a combination thereof. Other sequencing methods that can be used to perform the methods herein include digital PCR and sequencing by hybridization.

[0076] Nucleic acid sequencing reads can be obtained using any MPS method, MPS system, or MPS technology platform suitable for carrying out the methods described herein. Non-limiting examples of MPS platforms include Illumina / Solex / HiSeq (e.g., Illumina Genome Analyzer; Genome Analyzer II; HISEQ 2000; HISEQ), SOLiD, Roche / 454, PACBIO, and / or SMRT, Helicos True Single Molecule Sequencing, Ion Torrent, and Ion These include semiconductor-based sequencing (e.g., developed by Life Technologies), WildFire, 5500, 5500xl W, and / or 5500xl W Genetic Analyzer-based technologies (e.g., developed and sold by Life Technologies; U.S. Patent Publication No. US20130012399); polony sequencing, pyrosequencing, massively parallel signature sequencing (MPSS), RNA polymerase (RNAP) sequencing, LaserGen systems and methods, nanopore-based platforms, chemical-sensitive field-effect transistor (CHEMFET) arrays, electron microscopy-based sequencing (e.g., developed by ZS Genetics, Halcyon Molecular), nanoball sequencing, and the like.

[0077] Other sequencing methods that can be used to implement the methods herein include digital PCR and hybridization sequencing. Digital polymerase chain reaction (digital PCR or dPCR) can be used to directly identify and quantify nucleic acids in a sample. In some embodiments, digital PCR can be performed in an emulsion. For example, individual nucleic acids can be separated, for example, in a microfluidic chamber device, and each nucleic acid can be individually amplified by PCR. Nucleic acids can be separated so that there is no more than one nucleic acid per well. In some embodiments, different probes can be used to distinguish various alleles (e.g., fetal alleles and maternal alleles). Alleles can be enumerated to determine copy number.

[0078] In certain embodiments, sequencing by hybridization can be used. The method involves contacting a plurality of polynucleotide sequences with a plurality of polynucleotide probes, where each of the plurality of polynucleotide probes can optionally be tethered to a substrate. In some embodiments, the substrate can be a flat surface with an array of known nucleotide sequences. The pattern of hybridization to the array can be used to determine the polynucleotide sequences present in the sample. In some embodiments, each probe is tethered to a bead, such as a magnetic bead. Hybridization to the bead can be identified and used to identify multiple polynucleotide sequences in the sample.

[0079] In some embodiments, nanopore sequencing can be used in the methods described herein. Nanopore sequencing is a single-molecule sequencing technique in which a single nucleic acid molecule (e.g., DNA) is directly sequenced as it passes through a nanopore.

[0080] In some embodiments, chromosome-specific sequencing is performed. In some embodiments, chromosome-specific sequencing is performed using DANSR (digital analysis of selected regions). Digital analysis of selected regions allows for simultaneous quantification of hundreds of loci by cfDNA-dependent concatenation of two locus-specific oligonucleotides via an intervening "bridge" oligonucleotide to form a PCR template. In some embodiments, chromosome-specific sequencing is performed by creating a library enriched in chromosome-specific sequences. In some embodiments, sequence reads are obtained only for a selected set of chromosomes. In some embodiments, sequence reads are obtained only for chromosomes 21, 18, and 13.

[0081] In some embodiments, the sequence module obtains, generates, collects, assembles, manipulates, converts, processes, converts, and / or transfers sequence reads. The sequence module can utilize sequencing technologies known in the art to determine the sequence of nucleic acids. In some embodiments, the sequence module can also align, assemble, fragment, create complements, create reverse complements, check for errors, and correct errors of sequence reads. In some embodiments, the sequence module provides sequence reads to a mapping module or any other suitable module.

[0082] Sequencing reads As used herein, a "read" (i.e., a "read," "sequence read") is a short nucleotide sequence generated by any sequencing process described herein or known in the art. A read can be generated from one end of a polynucleotide fragment (a "single-end read"), or in some cases, from both ends of a polynucleotide fragment (e.g., a paired-end read, a double-end read).

[0083] The length of a sequence read is often associated with a particular sequencing technology. High-throughput methods, for example, produce sequence reads that can vary in size from tens to hundreds of base pairs (bp). Nanopore sequencing, for example, can produce sequence reads that can vary in size from tens to hundreds to thousands of base pairs. In some embodiments, the mean, median, average, or absolute length of the sequence reads is about 15 bp to about 900 bp in length. In certain embodiments, the mean, median, average, or absolute length of the sequence reads is about 1000 bp or greater.

[0084] Single-end reads can be of any suitable length. In some embodiments, the nominal, average, mean, or absolute length of a single-end read is, as the case may be, from about 10 nucleotides to about 1000 contiguous nucleotides, from about 10 nucleotides to about 500 contiguous nucleotides, from about 10 nucleotides to about 250 contiguous nucleotides, from about 10 nucleotides to about 200 contiguous nucleotides, from about 10 nucleotides to about 150 contiguous nucleotides, from about 15 contiguous nucleotides to about 100 contiguous nucleotides, from about 20 contiguous nucleotides to about 75 contiguous nucleotides, or about 30 or about 50 contiguous nucleotides. In certain embodiments, the nominal, average, mean, or absolute length of the single-end reads is or is more than about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 nucleotides in length.

[0085] Paired-end reads can be any suitable length. In certain embodiments, both ends are sequenced with a suitable read length sufficient to map each read (e.g., reads from both ends of a fragment template) to a reference genome. In certain embodiments, the nominal, average, mean, or absolute length of a paired-end read is between about 10 contiguous nucleotides and about 100 contiguous nucleotides, between about 10 contiguous nucleotides and about 75 contiguous nucleotides, between about 10 contiguous nucleotides and about 50 contiguous nucleotides, between about 15 contiguous nucleotides and about 50 contiguous nucleotides, between about 15 contiguous nucleotides and about 40 contiguous nucleotides, between about 15 contiguous nucleotides and about 30 contiguous nucleotides, or between about 15 contiguous nucleotides and about 20 contiguous nucleotides. In certain embodiments, the nominal, average, mean, or absolute length of the paired-end reads is about or greater than 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80 nucleotides.

[0086] A read is generally a representation of a nucleotide sequence in a physical nucleic acid. For example, in a read containing the designation ATGC for a sequence, "A" represents an adenine nucleotide in the physical nucleic acid, "T" represents a thymine nucleotide, "G" represents a guanine nucleotide, and "C" represents a cytosine nucleotide. Sequence reads are often obtained from a nucleic acid sample derived from a pregnant woman carrying a fetus. Sequence reads obtained from a nucleic acid sample derived from a pregnant woman carrying a fetus are often sequence reads that represent the fetus and / or the mother of the fetus (e.g., a pregnant woman subject).

[0087] Sequence reads obtained from the blood of a pregnant woman may be reads derived from a mixture of fetal and maternal nucleic acids. A mixture of relatively short reads can be converted into an indication of genomic nucleic acids present in the pregnant woman and / or fetus by the processes described herein. A mixture of relatively short reads can be converted into an indication of, for example, chromosomal alterations. Reads of a mixture of maternal and fetal nucleic acids can be converted into an indication of a composite chromosome or segment thereof containing features of one or both maternal and fetal chromosomes. In certain embodiments, "obtaining" nucleic acid sequence reads for a sample derived from a subject and / or "obtaining" nucleic acid sequence reads for a biological specimen derived from one or more reference patients can involve directly sequencing the nucleic acid to obtain sequence information. In some embodiments, "obtaining" can involve receiving sequence information obtained directly from the nucleic acid by another analyst.

[0088] It has been observed that circulating cell-free nucleic acid fragments (CCF fragments) obtained from a pregnant woman generally contain nucleic acid fragments derived from fetal cells (i.e., fetal fragments) and nucleic acid fragments derived from maternal cells (i.e., maternal fragments). Herein, sequence reads derived from CCF fragments derived from a fetus are referred to as "fetal reads." Herein, sequence reads derived from CCF fragments derived from the genome of a pregnant woman (e.g., mother) carrying a fetus are referred to as "maternal reads." Herein, the CCF fragments from which fetal reads are derived are referred to as fetal templates, and the CCF fragments from which maternal reads are derived are referred to as maternal templates.

[0089] In some embodiments, the length of a polynucleotide fragment (e.g., a polynucleotide template) is determined. The length of the polynucleotide fragments in a sample, or the average or mean length of the polynucleotide fragments, can be estimated and / or determined by a suitable method. In some embodiments, the length of the polynucleotide fragments in a sample, or the average or mean length of the polynucleotide fragments, is determined using a sequencing method. In some embodiments, the fragment length is determined using a paired-end sequencing platform. In some cases, the length of the fragment template is determined by calculating the difference between the genomic coordinates assigned to each mapped read of the paired-end read. In some embodiments, the fragment length can be determined using a sequencing process that obtains a complete or substantially complete nucleotide sequence for the fragment. Such sequencing processes include platforms that generate relatively long read lengths (e.g., Roche 454, Ion Torrent technology, One Molecule (Pacific Biosciences) technology, real-time SMRT technology, etc.).

[0090] In some embodiments, a subset of reads is selected for analysis, and optionally, a certain portion of the reads are excluded from the analysis. In certain cases, the selection of a subset of reads can enrich for a nucleic acid species (e.g., fetal nucleic acid). Enriching reads derived from fetal nucleic acid often increases the accuracy of, for example, the methods described herein (e.g., detecting chromosomal alterations). However, selecting and excluding reads from analysis often decreases the accuracy of the methods described herein (e.g., due to increased variability). Thus, without being limited by theory, there is generally a trade-off between the increased accuracy associated with enriching fetal reads and the decreased accuracy associated with reducing the amount of reads in the method, including selecting and / or excluding reads (e.g., derived from fragments within a particular size range). In some embodiments, a method includes selecting a subset of reads enriched for reads derived from fetal nucleic acid without significantly decreasing the accuracy of the method. Despite this apparent trade-off, it has been determined that, as described herein, utilizing a subset of nucleotide sequence reads (e.g., reads derived from relatively short fragments) can improve or maintain the accuracy of fetal genetic analysis. For example, in certain embodiments, about 80% or more of the nucleotide sequence reads can be rejected while maintaining sensitivity and specificity values ​​similar to those for comparable methods that do not reject such nucleotide sequence reads.

[0091] In some embodiments, some or all of the nucleic acids in a sample are enriched and / or amplified (e.g., non-specifically, e.g., by PCR-based methods) before or during sequencing. In certain embodiments, specific nucleic acid portions or nucleic acid subsets in a sample are enriched and / or amplified before or during sequencing. In some embodiments, portions or subsets of a preselected pool of nucleic acids are randomly sequenced. In some embodiments, nucleic acids in a sample are not enriched and / or amplified before or during sequencing.

[0092] In some embodiments, targeted enrichment, targeted amplification, and / or targeted sequencing methods are used. Targeting methods often involve isolating, selecting, and / or enriching a subset of nucleic acids (e.g., targeted genomic regions) in a sample for further processing by using sequence-specific oligonucleotides. In some embodiments, the targeted genomic regions are associated with chromosomal alterations, including but not limited to translocations, insertions, additions, deletions, and / or inversions. In some embodiments, nucleic acid fragments derived from multiple targeted genomic regions are sequenced and / or assayed. Polynucleotides (e.g., ccf DNA) derived from any suitable chromosome, part thereof, or combination of chromosomes can be sequenced and / or analyzed by targeted methods or non-targeted methods using the methods or systems described herein. Non-limiting examples of chromosomes that can be analyzed by the methods or systems described herein include chromosome 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, and Y. In some embodiments, a library of sequence-specific oligonucleotides is utilized to target (e.g., hybridize with) one or more sets of nucleic acids in a sample. The sequence-specific oligonucleotides and / or primers are often selective for specific sequences (e.g., unique nucleic acid sequences) present within one or more chromosomes, genes, exons, introns, and / or regulatory regions of interest. Any suitable method or combination of methods can be used to enrich, amplify, and / or sequence one or more species subsets of targeted nucleic acids.In some embodiments, one or more sequence-specific anchors are used to isolate and / or enrich the targeted sequences by capturing them on a solid phase (e.g., flow cells, beads). In some embodiments, sequence-specific primers and / or sequence-specific primer sets are used to enrich and / or amplify the targeted sequences by polymerase-based methods (e.g., PCR-based methods via any suitable polymerase-based extension). Sequence-specific anchors can often be used as sequence-specific primers.

[0093] In some embodiments, a fraction of the genome is sequenced, and this fraction is sometimes represented by the amount of genome covered by the determined nucleotide sequence (e.g., a coverage "fold" less than 1). When a genome is sequenced at a coverage of about 1, nearly 100% of the nucleotide sequence of the genome is represented by the read. A genome can also be sequenced with redundancy, in which case a given region of the genome can be covered by two or more reads or overlapping reads (e.g., a coverage "fold" greater than 1). In some embodiments, the genome is sequenced at about 0.01x to about 100x coverage, about 0.2x to 20x coverage, or about 0.2x to about 1x coverage (e.g., about 0.02x, 0.03x, 0.04x, 0.05x, 0.06x, 0.07x, 0.08, 0.09x, 0.1x, 0.2x, 0.3x, 0.4x, 0.5x, 0.6x, 0.7x, 0.8x, 0.9x, 1x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x, 15x, 20x, 30x, 40x, 50x, 60x, 70x, 80x, 90x coverage).

[0094] In some embodiments, the sequence coverage is reduced without significantly resulting in a decrease in the accuracy (e.g., sensitivity and / or specificity) of the methods described herein. A significant decrease in accuracy can be about a 1% to about a 20% decrease in accuracy compared to a method not using reduced sequence read count. For example, a significant decrease in accuracy can be about a 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, or more decrease. In some embodiments, the sequence coverage and / or sequence read count is reduced by about 50% or more. For example, the sequence coverage and / or sequence read count can be reduced by about 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or more. In some embodiments, the sequence coverage and / or sequence read count is reduced by about 60% to about 85%. For example, the sequence coverage and / or sequence read count can be reduced by about 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, or 84%. In some embodiments, the sequence coverage and / or sequence read count can be reduced by excluding certain sequence reads. In some cases, sequence reads derived from fragments longer than a certain length (e.g., fragments longer than about 160 bases) are excluded.

[0095] In some embodiments, one or more samples are sequenced in one sequencing run.The nucleic acid from different samples is often identified by one or more unique identifiers or identification tags.Sequencing method often utilizes identifiers, which allow the sequencing reaction in sequencing process to be multiplexed.Sequencing process can be carried out using any suitable number of samples and / or unique identifiers (for example, 4, 8, 12, 24, 48, 96 or more).

[0096] The sequencing process optionally uses a solid phase, which optionally includes a flow cell onto which nucleic acids from a library can be attached, allowing reagents to flow and contact the attached nucleic acids. The flow cell optionally includes flow cell lanes, and the use of identifiers can facilitate the analysis of multiple samples within each lane. Flow cells are often solid supports that can be configured to support and / or enable the ordered passage of reagent solutions over bound analytes. Flow cells are often planar, often optically transparent, generally millimeter or submillimeter scale, and often have channels or lanes through which analyte / reagent interactions occur. In some embodiments, the number of samples analyzed in a given flow cell lane depends on the number of unique identifiers utilized during library preparation and / or probe design. For example, multiplexing using 12 identifiers allows for the simultaneous analysis of 96 samples (e.g., equivalent to the number of wells in a 96-well microwell plate) within an 8-lane flow cell. Similarly, multiplexing using, for example, 48 identifiers allows for simultaneous analysis of 384 samples (e.g., equivalent to the number of wells in a 384-well microwell plate) in an 8-lane flow cell. Non-limiting examples of commercially available multiplex sequencing kits include Illumina's multiplexed sample preparation oligonucleotide kit and multiplexed sequencing primers, as well as the PhiX control kit (e.g., Illumina's product numbers PE-400-1001 and PE-400-1002, respectively).

[0097] Lead Mapping Sequence reads or portions thereof (e.g., sequence read subsequences) can be mapped and / or aligned to a reference sequence (e.g., a reference genome) by an appropriate method. Herein, the process of aligning one or more reads to a reference genome is referred to as "mapping." In some cases, the number of sequence reads mapped to a specified nucleic acid region (e.g., a chromosome, a portion thereof, or a segment thereof) can be quantified. Any appropriate mapping method (e.g., a process, an algorithm, a program, software, a module, etc., or a combination thereof) can be used. Certain aspects of the mapping process are described herein below.

[0098] Mapping of nucleotide sequence reads (i.e., sequence information derived from fragments whose physical location in a genome is unknown) can be performed in a number of ways and often involves aligning the resulting sequence reads with matching sequences in a reference genome. In such alignments, sequence reads are generally aligned to a reference sequence, and aligned reads are referred to as "mapped," "mapped sequence reads," or "mapped reads."

[0099] As used herein, the terms "aligned," "alignment," or "aligning" refer to two or more nucleic acid sequences that can be identified as a match (e.g., 100% identity) or a partial match. Alignment can be performed manually (e.g., by visual inspection) or by computer (e.g., software, program, module, or algorithm), non-limiting examples of which include the Efficient Local Alignment of Nucleotide Data (ELAND) computer program, sold as part of the Illumina Genomics Analysis pipeline. The alignment of sequence reads can be a 100% sequence match. In some cases, the alignment is less than a 100% sequence match (i.e., a non-perfect match, a partial match, or a partial alignment). In some embodiments, the alignment is about 99%, 98%, 97%, 96%, 95%, 94%, 93%, 92%, 91%, 90%, 89%, 88%, 87%, 86%, 85%, 84%, 83%, 82%, 81%, 80%, 79%, 78%, 77%, 76%, or 75% match. In some embodiments, the alignment includes mismatches. In some embodiments, the alignment includes 1, 2, 3, 4, or 5 mismatches. Two or more sequences can be aligned using either strand. In certain embodiments, a nucleic acid sequence is aligned with the reverse complement of another nucleic acid sequence.

[0100] A variety of computational methods can be used to map each sequence read to a portion. Non-limiting examples of computer algorithms that can be used to align sequences include, without limitation, BLAST, BLITZ, FASTA, BOWTIE 1, BOWTIE 2, ELAND, MAQ, PROBEMATCH, SOAP, or SEQMAP, or variations or combinations thereof. In some embodiments, sequence reads or portions thereof can be aligned to sequences in a reference genome. In some embodiments, sequence reads can be accessed from nucleic acid databases known in the art, such as GenBank, dbEST, dbSTS, EMBL (European Molecular Biology Laboratory), and DDBJ (DNA The sequences can be found and / or aligned with sequences in nucleic acid databases, including the Nucleic Acid Databank of Japan. BLAST or similar tools can be used to search the identified sequences against sequence databases.

[0101] In some embodiments, a read may be uniquely or non-uniquely mapped to a reference genome. A read is considered "uniquely mapped" if it aligns with a sequence in a single reference genome. A read is considered "non-uniquely mapped" if it aligns with sequences in two or more reference genomes. In some embodiments, non-uniquely mapped reads are excluded from further analysis (e.g., filtered out). In certain embodiments, certain low-level mismatches (0-1) may be allowed to account for single nucleotide polymorphisms that may exist between the reference genome and the reads derived from the individual samples being mapped. In some embodiments, no mismatches are allowed for reads mapped to a reference sequence.

[0102] The term "reference genome," as used herein, may refer to any particular known, partially or completely sequenced, or characterized genome or portion thereof of any organism or virus that can be used as a reference for identified sequences derived from a subject. For example, reference genomes of human subjects, bacteria, parasites, viruses, and many other organisms can be found at the National Center for Biotechnology Information on the World Wide Web at the URL: ncbi.nlm.nih.gov. In some embodiments, the reference genome is obtained from a reference sample or set of reference samples. "Genome" refers to the complete genetic information of an organism or virus, as expressed in nucleic acid sequences. As used herein, a "reference sequence" may refer to a reference genome or a portion thereof (e.g., a chromosome, a gene, a conserved region, a highly mappable region). A reference sequence is sometimes a reference genome or a portion thereof. As used herein, a reference genome is often an assembled or partially assembled genome sequence from one or more individuals. In some embodiments, the reference genome is an assembled or partially assembled genome sequence from one or more human individuals. In some embodiments, the reference genome includes sequences assigned to chromosomes. In some embodiments, the reference genome is a viral genome or portion thereof. In some embodiments, the reference genomes of one or more viruses are used to align and / or map nucleic acids (e.g., sequence reads) obtained from a human subject (e.g., a human sample).

[0103] In certain embodiments, when the sample nucleic acid is derived from a pregnant woman, the reference sequence is not derived from the fetus, the mother of the fetus, or the father of the fetus, as the case may be, and is referred to herein as an "external standard." In some embodiments, a maternal standard can be prepared and used. When a standard derived from a pregnant woman (a "maternal reference sequence") is prepared based on an external standard, reads derived from the pregnant woman's DNA, which is substantially free of fetal DNA, are often mapped and assembled against the external standard sequence. In certain embodiments, the external standard is derived from the DNA of an individual of substantially the same race as the pregnant woman. The maternal standard sequence may not completely cover the maternal genomic DNA (e.g., it may cover about 50%, 60%, 70%, 80%, 90%, or more of the maternal genomic DNA), and the maternal standard may not completely match the maternal genomic DNA sequence (e.g., the maternal reference sequence may contain multiple mismatches).

[0104] Sequence reads can be mapped by a mapping module or a machine that includes a mapping module, and generally, this mapping module maps the reads to a reference genome or a segment thereof.The mapping module can map sequencing reads through a suitable method known in the art or as described herein.In some embodiments, a mapping module or a machine that includes a mapping module is required to provide mapped sequence reads.The mapping module often includes a suitable mapping program or mapping algorithm and / or alignment program or alignment algorithm.

[0105] Unmatched Leads This paper provides a method for determining the presence or absence of chromosomal alterations, and in some embodiments, provides a method for identifying breakpoints associated with chromosomal alterations.In certain embodiments, the method for identifying breakpoints and / or chromosomal alterations comprises identifying mismatched sequence reads.In some embodiments, the method comprises identifying mismatched states for sequences, sequencing reads, and / or sequencing read pairs (e.g., readmate pairs).As used herein, the term "mismatched" refers to the following situation: (i) a first read or a portion thereof is mapped to a first location of a reference genome; (ii) a second read, a portion thereof, or the second portion of the first read is unmappable, has a low mappability score, and / or is mapped to a second location of a reference genome, and the first and second locations of the reference genome are non-contiguous and / or are separated by a distance greater than the size of the template polynucleotide fragment from which one or more of the sequence reads are obtained.In some embodiments, mismatched refers to the situation in which both reads of a sequencing read pair (e.g., readmate pair) are unmappable. Mismatched sequence reads and / or mismatched sequence read pairs often contain mismatches. Mismatches can be determined for polynucleotide sequences, sequence reads, single-end reads, and double-end reads (e.g., paired-end reads). In some embodiments, the method includes identifying mismatched reads and / or mismatched read pairs. Mismatched reads and / or mismatched read pairs can be identified using any suitable sequencing method. In some embodiments, mismatched read pairs are identified from paired-end sequencing reads. In this specification, the terms "paired-end sequencing read" and "paired-end read" are used interchangeably and refer to a sequencing read pair, where each member of the pair is derived from sequencing complementary strands of a polynucleotide fragment. In this specification, each read of a paired-end read is referred to as a "readmate."

[0106] Sequence reads and / or paired-end reads can be generated using a variety of technologies, including, but not limited to, BWA (Li H. and Durbin R. (2009), Bioinformatics, 25:1754-60), Novoalign [Novocraft (2010)], Bowtie (Langmead B et al. (2009), Genome Biol, 10:R25), SOAP2 (Li R et al. (2009), , Bioinformatics, Vol. 25, pp. 1966-67), BFAST (Homer N et al. (2009), PLoS ONE, Vol. 4, p. e7767), GASSST (Rizk, G. and Lavenier, D. (2010), Bioinformatics, Vol. 26, pp. 2534-2540), and MPscan (Rivals E. et al. (2009), Lecture Notes in Computer Science, 5724 Sequence reads and / or paired-end reads can be mapped and / or aligned using a suitable short read alignment program. Non-limiting examples of short read alignment programs include BarraCUDA, BFAST, BLASTN, BLAT, Bowtie, BWA, CASHX, CUDA-EC, CUSHAW, CUSHAW2, drFAST, ELAND, ERNE, GNUMAP, GEM, GensearchNGS, GMAP, and Geneious. Assembler, iSAAC, LAST, MAQ, mrFAST, mrsFAST, MOSAIK, MPscan, Novoalign, NovoalignCS, Novocraft, NextGENe, Omixon, PALMapper, Partek, PASS, PerM, QPalma, RazerS, REAL, cREAL, RMAP, rNA, RTG, Segemehl, SeqMap, Shrec, SHRiMP, SLIDER, SOAP, SOAP2, SOAP3, SOCS, SSAHA, SSAHA2, Stampy, SToRM, Subread, Subjunc, Taipan, UGENE, VelociMapper, TimeLogic, XpressAlign, ZOOM, etc., or combinations thereof. Paired-end reads are often mapped to opposite ends of the same polynucleotide fragment in relation to a reference genome. In some embodiments, sequence reads are independently mapped. In some embodiments, readmates are independently mapped. In some embodiments, information from both sequence reads (i.e., from each end) is interwoven in the mapping process. A reference genome is often used to determine and / or infer the sequence of nucleic acids located between paired-end read mates.As used herein, the term "mismatched read pair" refers to a paired-end read that includes a readmate pair, and one or both readmates do not uniquely map to the same region of a reference genome that is partially defined by a segment of consecutive nucleotides. In some embodiments, the mismatched read pair is a paired-end readmate that maps to an unexpected location in the reference genome. Non-limiting examples of unexpected locations in the reference genome include (i) two different chromosomes, (ii) locations separated by more than a predetermined fragment size (e.g., more than 300bp, more than 500bp, more than 1000bp, more than 5000bp, or more than 10,000bp), (iii) orientation that does not match the reference sequence (e.g., opposite orientation), etc., or combinations thereof. In some embodiments, readmates that are mapped to and / or aligned with two different chromosomes are identified as mismatched readmates. In this specification, readmates that are mapped to and / or aligned with two different chromosomes are referred to as "chimeric read pairs". In some embodiments, the mismatched read pair does not include a read mate pair in which the first read mate is mapped to a first chromosome, the second read mate is mapped to a second chromosome, and the first chromosome is a different chromosome from the second chromosome. In some embodiments, the mismatched read pair includes a first read mate that is mapped to a first segment of a reference genome (e.g., a first chromosome) and a second read mate that is partially mapped to the first segment of a reference genome (e.g., a first chromosome). As used herein, the term "partially" in the term "partially mapped" refers to 90% or less, 80% or less, 60% or less, 50% or less, 40% or less, 30% or less, 25% or less, 20% or less, 15% or less, 10% or less, or 5% or less of the nucleotides of the read.In some embodiments, the mismatched read pair includes a first readmate that maps to a first segment (e.g., a first chromosome) of the reference genome and a second readmate that is unmappable and / or has a low mappability (e.g., a low mappability score). In some embodiments, the mismatched read pair includes a first readmate that maps to a first segment (e.g., a first chromosome) of the reference genome and a second readmate, where the mappability of the second readmate or a portion thereof is not determined. In some embodiments, the mismatched read pair includes a first readmate that maps to a first segment (e.g., a first chromosome) of the reference genome and a second readmate that partially maps to a second segment (e.g., a second chromosome) of the reference genome, where the first segment and the second segment are different segments (e.g., different chromosomes). In some embodiments, a subset (e.g., collection) of mismatched read pairs includes a first readmate and a second readmate that map to different chromosomes, and a first readmate and a second readmate where one or both readmates are unmappable and / or partially map to the same or different chromosomes. In some embodiments, mismatched readmates are identified according to the length (e.g., average length, predetermined fragment size) or expected length of the template polynucleotide fragments in the sample. For example, readmates that map to locations that are separated by more than the average or expected length of the polynucleotide fragments in the sample are optionally identified as mismatched read pairs. Read pairs that map in reverse orientation are optionally determined by taking the reverse complement of one of the reads and comparing the alignment of both reads using the same strand of the reference sequence. Mismatched read pairs can be identified by any suitable method and / or algorithm known in the art or described herein.Mismatched read pairs can be identified by a mismatched read identification module or a machine that includes a mismatched read identification module, and a mismatched read identification module is generally a module that identifies mismatched read pairs.Non-limiting examples of mismatched read identification modules include SVDetect, Lumpy, BreakDancer, BreakDancerMax, CREST, DELLY, etc., or a combination thereof.In some embodiments, mismatched read pairs are not identified by an algorithm that only identifies readmates that are mapped to or aligned with different chromosomes.In certain embodiments, mismatched read pairs are identified by an algorithm that identifies a collection that includes paired-end readmates that are mapped to or aligned with different chromosomes, and paired-end readmates where one or both readmates are unmappable and / or partially mapped to the same chromosome or different chromosomes.In some embodiments, a mismatched read identification module or a machine that includes a mismatched read identification module is required to provide mismatched read pairs.

[0107] In some embodiments, the mismatched read pairs are not clustered or subjected to clustering analysis.Clustering analysis referred to herein refers to the process of grouping paired-end reads according to the location in genome where one or both readmates are mapped.In some embodiments, the clustering analysis comprises generating a subset of paired reads, where one readmate of each read pair is mapped to a first chromosome, and the other readmate of each read pair is mapped to a second chromosome, and the first chromosome is a chromosome different from the second chromosome.

[0108] The term "mismatched read" as used herein refers to a sequence read in which a first portion of the read does not uniquely map to the same region of a reference genome as a second portion of the read, where the same region of the reference genome is defined, in part, as a segment of consecutive nucleotides. In some embodiments, the mismatched read comprises a first portion and a second portion that map to an unexpected location in the reference genome. In some embodiments, the mismatched read comprises a first portion that maps to a first chromosome and a second portion that maps to a second chromosome, where the first chromosome is a different chromosome from the second chromosome. In some embodiments, the mismatched read comprises a portion that partially maps to a first segment of the reference genome (e.g., a first chromosome). In some embodiments, the mismatched read comprises a first portion that maps to a first segment of the reference genome (e.g., a first chromosome) and a second portion that is unmappable, and / or has a low mappability (e.g., a low mappability score). In some embodiments, the mismatched reads include a first portion that maps to a first segment (e.g., a first chromosome) of the reference genome and a second portion where the mappability of the second portion or part thereof is not determined. In some embodiments, the mismatched reads include a first portion that maps to a first segment (e.g., a first chromosome) of the reference genome and a second portion that partially maps to a second segment (e.g., a second chromosome) of the reference genome, where the first segment and the second segment are different segments (e.g., different chromosomes). Mismatched reads can be identified by suitable methods and / or algorithms known in the art or described herein. Mismatched reads are sometimes identified by a process of characterizing reads. Mismatched reads can be identified by a mismatched read identification module or a machine that includes a mismatched read identification module, and the mismatched read identification module is generally a module that identifies mismatched reads. In certain embodiments, mismatched reads are identified by an algorithm that identifies a subset or collection of mismatched reads.In some embodiments, a mismatched read identification module or a machine including a mismatched read identification module is required to provide the mismatched read.

[0109] Mappability changes In some embodiments, the mappability of one or more reads is characterized. In some embodiments, characterizing the mappability of a read includes characterizing the mappability of a plurality of sequence read subsequences. As used herein, the term "characterizing the mappability of a read of a plurality of sequence read subsequences" sometimes refers to "characterizing a read." Characterizing a read sometimes includes generating a plurality of sequence read subsequences of a read and mapping each of the sequence read subsequences to a reference genome. In some embodiments, sequence read subsequences are generated for both readmates of a mismatched read pair. As used herein, sequence read subsequences are sometimes referred to as pseudoreads. Sequence read subsequences can be generated by any suitable method. Sequence read subsequences are often generated via an in silico process. In some embodiments, sequence read subsequences are generated and mapped by the following method: (i) mapping the reads, (ii) excluding one or more bases from the end of the mapped reads via an in silico process, (iii) mapping the resulting truncated reads (i.e., sequence read subsequences), and (v) repeating (ii) and (iii). In some embodiments, steps (ii) and (iii) are repeated until the end of the read is reached. In certain embodiments, steps (ii) and (iii) are repeated until the resulting truncated reads are no longer mappable. Sequence read subsequences can be generated by progressively and / or incrementally excluding one or more bases from the 3' end of the read or the 5' end of the read. Sequence read subsequences can be generated by excluding bases from the end of the read one at a time, two at a time, three at a time, four at a time, five at a time (e.g., in step (ii)), or a combination thereof. In some embodiments, each sequence read subsequence generated for a read is a different length, hi some embodiments, each sequence read subsequence for a read is a subsequence of contiguous nucleotides derived from the read.In some embodiments, each of the sequence read subsequences of a read is shorter than the full length read, and optionally the longest sequence read subsequence is about 1 base, about 2 bases or less, about 3 bases or less, about 4 bases or less, about 5 bases or less, about 6 bases or less, about 7 bases or less, about 8 bases or less, about 9 bases or less, or about 10 bases or less shorter than the read. In some embodiments, each of the sequence read subsequences of a read is about 1 base, about 2 bases or less, about 3 bases or less, about 4 bases or less, about 5 bases or less, about 6 bases or less, about 7 bases or less, about 8 bases or less, about 9 bases or less, or about 10 bases or less shorter than the next-longest sequence read subsequence or read. In some embodiments, the sequence read subsequence of each mismatched readmate is progressively shorter than the next largest subsequence or readmate by about 1 base, about 2 bases or less, about 3 bases or less, about 4 bases or less, about 5 bases or less, about 6 bases or less, about 7 bases or less, about 8 bases or less, about 9 bases or less, about 10 bases or less, or a combination thereof. As used herein, the term "sequence read subsequence" refers to a set of polynucleotide fragments generated in silico for a read by the methods described herein. As used herein, the term "sequence read subsequence" can also refer to one or more sets of polynucleotide fragments generated in silico for one or more reads. As used herein, the term "sequence read subsequence" also refers to and / or includes a full-length read from which a set of sequence read subsequences is generated. As used herein, a sequence read subsequence is sometimes referred to as a "subsequence." As used herein, the terms "sequence read subsequence" and / or "subsequence" in the singular refer to a member of a set of polynucleotide fragments generated in silico for a read.

[0110] The sequence read subsequences can be generated by any suitable module, program, or method. In certain embodiments, the sequence read subsequences are generated by a fragmentation module. The sequence read subsequences can be generated by any suitable module, program, or method, non-limiting examples of which include BWA (Li H. and Durbin R. (2009) Bioinformatics, 25, 1754-60), Novoalign [Novocraft (2010)], Bowtie (Langmead B et al. (2009) Genome Biol, 10:R25), SOAP2 (Li R et al. (2009) Bioinformatics , Vol. 25, pp. 1966-67), BFAST (Homer N et al. (2009), PLoS ONE, 4, p. e7767), GASSST (Rizk, G. and Lavenier, D. (2010), Bioinformatics, vol. 26, pp. 2534-2540), and MPscan (Rivals E. et al. (2009), Lecture Notes in Computer Science, vol. 5724, pp. 246-26 The mapping can be performed by any suitable mapping module, program, or method, including, for example, the methods described in the above sections (see, for example, "Mapping Modules," "Mapping Programs," "Mapping Method ...

[0111] In some embodiments, some or all sequence read subsequences generated for a sample are mapped to a reference genome. In some embodiments, sequence read subsequences of a read are mapped to a combination of one or more reference genomes. Subsequences of one or more reads are often mapped to a human reference genome. In some embodiments, subsequences are mapped to a human genome and / or a viral genome. In some embodiments, the mappability of sequence read subsequences of some or all reads is determined. The term "mappability" refers to a measure of how well a polynucleotide fragment maps to a reference genome. In some cases, mappability includes a mappability score or mappability value. In some cases, a mappability score or mappability value is determined for each sequence read subsequence (e.g., readmate) of a read. Any suitable mappability score or mappability value can be determined for a sequence read subsequence. The mappability score can be any suitable score generated by a mapping module, mapping program, or mapping method, or a combination thereof, known or described herein. For example, in some embodiments, the mappability score can be a MAPQ score. In some embodiments, the mappability score includes an alignment score. For example, the alignment score can be generated by a suitable local alignment algorithm (such as the Smith-Waterman algorithm), and the alignment score can be generated according to the Euclidean distance between two sequences, weighted by the number of occurrences in the reference genome. The alignment score can be generated by any suitable metric that qualifies or quantifies the uniqueness of a polynucleotide sequence. Standards for determining high, good, acceptable, low, unacceptable, and / or poor mappability scores are known in the art and are often specific to the mapping or alignment program used.

[0112] In some embodiments, determining the presence or absence of a chromosomal alteration includes determining and / or identifying a change in mappability of a sequence read subsequence of a read (e.g., a mismatched read, a readmate of a mismatched read pair). In some embodiments, characterizing a read includes determining and / or identifying a change in mappability of a sequence read subsequence of a read. The change in mappability is optionally determined between one or more sequence read subsequences. For example, optionally, a change in mappability is indicated when one or more subsequences comprising one or more nucleotides located on a first side of the read comprise a first subset with mappability scores that are substantially the same or similar, and one or more subsequences comprising nucleotides on a second side of the read comprise a second subset with mappability scores that are substantially different from the first subset. As used herein, a change in mappability identified within a sequence read subsequence of a read is sometimes referred to as a change in mappability of the read. For example, a read may comprise a change in mappability where a sequence read subsequence of the read comprises the change in mappability. In certain embodiments, a mismatch can be identified within a read for which a change in mappability is determined. In certain embodiments, the mismatched read comprises a change in mappability. In some embodiments, a change in mappability can be determined for a read for which a mismatch is identified. In some embodiments, a change in mappability is not identified and / or determined for a read, and the read does not comprise a mismatch. In some cases, one or both readmates of a mismatched read pair comprise a change in mappability. In certain embodiments, a mismatched read pair is identified in which one or both readmates of a paired-end read pair comprise a change in mappability. In some embodiments, a change in mappability can be determined for one or both readmates of a mismatched read pair. In some embodiments, one or both readmates of a chimeric read pair do not comprise a change in mappability.

[0113] In some cases, determining and / or identifying changes in mappability of sequence read subsequences of a read includes determining a relationship between the mappability of each sequence read subsequence of a read and a suitable feature of each sequence read subsequence of a read (e.g., a mismatched read, a mismatched read pair, a readmate of a mismatched read pair). The relationship can be determined for one or more reads. For example, in some embodiments, the relationship is determined for both readmates of a mismatched read pair.

[0114] As used herein, the term "relationship" refers to a mathematical and / or graphical relationship between two or more variables or values. Non-limiting examples of relationships include mathematical or graphical representations of functions, correlations, distributions, linear or nonlinear equations, lines, regressions, fitted regressions, etc., or combinations thereof. In some embodiments, determining a relationship includes generating a linear relationship, a nonlinear relationship, or a fitted relationship. In some embodiments, the relationship is plotted or graphically displayed.

[0115] Non-limiting examples of suitable features of sequence read subsequences that can be used to determine the relationship include fragment length, their relative order, molecular weight, an identifier for each fragment indicating GC content, etc., or a combination thereof. In certain embodiments, determining and / or identifying changes in mappability of sequence read subsequences of a read comprises determining the relationship between mappability and length of each sequence read subsequence of a read (e.g., one or both readmates).

[0116] In some embodiments, determining and / or identifying a change in mappability includes determining the presence or absence of a change in one or more coefficients, variables, values, constants, etc., or combinations thereof, that describe and / or quantify the relationship. As used herein, "change" sometimes refers to "difference." Non-limiting examples of coefficients, constants, values, and variables that may be used to determine a change in mappability include slope (e.g., the slope of a linear, non-linear, or fitted relationship), the sum, average, median, or mean of coordinates (e.g., x- or y-coordinate values), intercept (e.g., y-intercept), maximum value (e.g., maximum peak height), minimum value (e.g., smallest value), integral of a curve (e.g., area under the curve), etc., or combinations thereof. In some embodiments, a change in mappability is determined mathematically. The change can be determined by an appropriate statistical test for significance (e.g., significant difference), non-limiting examples of which include a Wilcoxon test (e.g., a Wilcoxon signed-rank test), a t-test, a chi-squared test, etc. In some embodiments, the change in mappability is identified and / or determined visually (e.g., from a plot or graph). In some embodiments, the change in mappability comprises the mean, median, or average of the change in mappability determined for one or more reads. For example, in some cases, the change in mappability comprises the mean, median, or average of the change in mappability determined for both readmates of a mismatched read pair. In some embodiments, the change in mappability comprises the mean, median, or average of the slope between a first relationship generated for a partial sequence of a first read and a second relationship generated for a partial sequence of a second read (e.g., for the readmates of the first and second mismatched read pairs). The change in mappability can be generated, determined, and / or identified by any suitable module, system, or software. Changes in mappability are often identified and / or determined by a mapping characterization module.The mapping characterization module can generate subsequences, generate relationships, characterize the mappability of the subsequences, determine a change in mappability, receive or generate a mappability threshold, and / or compare the change in mappability to a mappability threshold.

[0117] In some embodiments, the mapping characterization module includes instructions (e.g., algorithms) for a microprocessor in the form of code and / or source code (e.g., a collection of standard or custom scripts) and / or one or more software packages (e.g., statistical software packages) that perform the functions of the mapping characterization module. In some embodiments, the mapping characterization module includes code (e.g., scripts) written in S or R that utilizes an appropriate package (e.g., S package, R package). For example, the slope of change of a mapping characterization can be calculated in R using the following script: lm(y~x)[["coefficient"]][2] where y is the MAPQ score for each stepwise alignment and x is the length of the stepwise alignment. In some embodiments, the mapping characterization module includes and / or uses a suitable statistical software package. Non-limiting examples of statistical software packages include S-plus, stata, SAS, MATLAB, statistical packages in R, etc., or combinations thereof.

[0118] In some embodiments, the change in mappability can be determined for reads and / or a subset of reads. In some embodiments, the change in mappability can be determined for reads and / or a subset of reads in which mismatches are identified. In some embodiments, one or more reads (e.g., a subset of reads, a subset of mismatched read pairs) are selected and / or identified according to a change in mappability of sequence read subsequences. In some embodiments, identifying and / or determining a change in mappability comprises identifying and / or determining a substantial difference (e.g., a statistical difference) in mappability between two or more subsequences of reads or subsets of subsequences. In some embodiments, one or more reads (e.g., a subset of reads) are identified and / or selected according to a change in mappability and / or a mappability threshold. In some embodiments, mismatched read pairs are selected according to a change in mappability and / or a mappability threshold of one or both reads. In some embodiments, selecting reads comprises comparing the change in mappability to a mappability threshold. In some embodiments, selected reads are selected that include a change in mappability that is above, below, within, outside, significantly different from, or substantially the same as a mappability threshold. Often, one or more reads are selected in which the change in mappability for one or more reads is significantly different from a predetermined mappability threshold or includes a numerical value (e.g., quantitative value) that falls outside a predetermined range for the value defined by the mappability threshold. Reads (e.g., a subset of reads) can be identified and / or selected according to a lead selection module (e.g., 120).In some embodiments, the lead selection module comprises instructions (e.g., algorithms) for a microprocessor in the form of code and / or source code (e.g., a collection of standard or custom scripts) and / or one or more software packages (e.g., statistical software packages) that perform the functions of the mapping characterization module. In some embodiments, the lead selection module comprises code (e.g., scripts) written in S or R that utilizes an appropriate package (e.g., S package, R package). For example, selecting reads for which the average slope between Mate 1 and Mate 2 is below a threshold of 0 can be done in R. Data [Data < 0] where the data contains the slope of the average values ​​of Mate 1 and Mate 2. In some embodiments, the lead selection module includes and / or uses an appropriate statistical software package. Non-limiting examples of statistical software packages include S-plus, stata, SAS, MATLAB, statistical packages in R, Prism (GraphPad Software, Inc., La Jolla, CA), SigmaPlot (Systat Software, Inc., San Jose, CA), Microsoft Excel (Redmond, WA, USA), etc., or combinations thereof.

[0119] Mappability thresholds often include one or more predetermined values, value limits, and / or value ranges. As used herein, the terms "threshold" and "threshold value" refer to any number calculated using a qualified data set and used as a selection limit. Mappability thresholds are often calculated by mathematically and / or statistically manipulating changes in mappability.

[0120] In some embodiments, a subset of reads is identified and / or selected, wherein the reads in the subset comprise a change in the slope of the relationship determined between mappability and fragment length for a plurality of subsequences of the reads in each subset. In some embodiments, the change in slope indicates the presence of a candidate breakpoint. In some embodiments, a slope of the relationship (e.g., between mappability and fragment length for a plurality of subsequences of the reads) substantially greater than 1 or substantially less than 1 (e.g., a mappability threshold of 1) often indicates a read that comprises a candidate breakpoint and / or identifies a subset of reads in which a change in mappability exists. In some embodiments, reads (e.g., a subset of reads) are identified and / or selected that comprise a change in mappability (e.g., a slope) that exceeds a mappability threshold of about 0, about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, about 0.9, or greater than about 1.0. In some embodiments, reads are identified and / or selected that contain a change in mappability (e.g., slope) outside a mappability threshold range of about -0.1 to about 0.1, about -0.2 to about 0.2, about -0.3 to about 0.3, about -0.4 to about 0.4, about -0.5 to about 0.5, about -0.6 to about 0.6, about -0.7 to about 0.7, about -0.8 to about 0.8, about -0.9 to about 0.9, or about -1.0 to about 1.0. In some embodiments, the change in mappability (e.g., mean slope, average slope, or median slope) and / or the mappability threshold are expressed as an absolute value. The threshold can be any suitable parameter indicative of a change in mappability (e.g., the variance, standard deviation, or MAD of one or more mappability scores).

[0121] Characterizing the read and / or evaluating the change in mappability of the read (e.g., a partial sequence of the read) can provide one or more breakpoints and / or candidate breakpoints. As used herein, the term "breakpoint" refers to a position between two adjacent base calls of a readmate, where the base on the first side of the breakpoint is mapped to a first chromosomal region, and the base on the second side of the breakpoint is mapped to a second chromosomal region, and the first chromosomal region and the second chromosomal region are not adjacent in the reference genome. In some embodiments, the first chromosomal region and the second chromosomal region are on different chromosomes. In some embodiments, the first chromosomal region and the second chromosomal region are on the same chromosome, in which case the first chromosomal region and the second chromosomal region are not adjacent in the reference genome. In some embodiments, the term "breakpoint," as used herein, refers to a position between two adjacent base calls in a readmate where the bases on a first side of the position are mapped to a reference genome and the bases on a second side of the position are unmappable (e.g., cannot be mapped with a certain level of certainty). In some embodiments, the term "breakpoint," as used herein, refers to a position between two adjacent base calls in a readmate where the bases on a first side of the position are mapped to the human genome and the bases on the second side of the position are mapped to heterologous genetic material (e.g., a viral genome). In some embodiments, the breakpoint indicates the location and / or position of a chromosomal variation or portion thereof. In some embodiments, the breakpoint identifies the position of a nucleic acid relative to a reference genome where genetic material is inserted, deleted, and / or exchanged. In some embodiments, if the chromosomal variation includes an insertion or translocation, the breakpoint may indicate the position and / or location on one side of the insertion or translocation. In some embodiments, a first breakpoint of the insertion or translocation is identified within a first read or a subset of the first reads, and a second breakpoint of the insertion or translocation is identified within a second read or a subset of the second reads.As used herein, the term "candidate breakpoint" refers to a read and / or a position within a read that is likely to contain a breakpoint. In some embodiments, the candidate breakpoint includes the breakpoint. In certain embodiments, the candidate breakpoint does not include the breakpoint. Reads and / or subsets of reads identified and / or selected according to changes in mappability and / or according to a mappability threshold often contain the candidate breakpoint.

[0122] In certain embodiments, characterizing the mappability of multiple sequence read subsequences generated from reads comprises identifying and / or determining locations and / or positions of candidate breakpoints. In some embodiments, the reads are representations of a genome (e.g., a maternal genome, a fetal genome, or a portion thereof). In certain embodiments, the mapped reads comprising candidate breakpoints are representations of candidate breakpoints located within a genome (e.g., a maternal genome, a fetal genome, or a portion thereof). In some embodiments, the locations and / or positions of the candidate breakpoints within the reads are determined according to a relationship (e.g., the relationship between mappability and length of the sequence read subsequences). In some embodiments, the locations and / or positions of the candidate breakpoints within the reads are determined according to changes in mappability. In some embodiments, identifying and / or determining the locations and / or positions of the candidate breakpoints within the reads comprises identifying a substantial difference (e.g., a statistical difference) in mappability between the sequence read subsequences of two or more reads. In some embodiments, the location of the candidate breakpoint is determined at position x, where a sequence read subsequence on a first side of position x comprises a mappability value that is substantially different from a mappability value for a sequence read subsequence located on a second side of position x, thereby indicating a candidate breakpoint at position x. In some embodiments, the location of the candidate breakpoint is determined according to a slope analysis. For example, a relationship is often determined between mappability and fragment length for multiple subsequences, where the relationship is defined in part by a line, and the line or portion thereof is defined in part by a slope. In the foregoing example, a substantial change in slope often indicates the location of the candidate breakpoint (e.g., at position x, where a subsequence on a first side of position x comprises a slope that is substantially different from the slope for a subsequence located on the second side of position x). In some embodiments, all reads containing the putative breakpoint (e.g., determined according to a change in mappability and / or a threshold) are de novo assembled to determine the breakpoint. In some cases, the breakpoints are determined by aligning the reads containing the mappability changes to a reference genome.In some embodiments, the candidate breakpoint and / or the location of the breakpoint are identified at the resolution of read length.For example, the mapped read can comprise a mappability change, which indicates the candidate breakpoint located at the position in the reference genome to which the read is mapped.In some embodiments, the candidate breakpoint and / or the location of the breakpoint are identified at the resolution of 150 bases or less, 100 bases or less, 75 bases or less, 50 bases or less, 10 bases or less, 9 bases or less, 8 bases or less, 7 bases or less, 6 bases or less, 5 bases or less, 4 bases or less, 3 bases or less, 2 bases or less, or at the resolution of single base.

[0123] In some embodiments, candidate breakpoints are identified by a breakpoint module. The breakpoint module is often configured to identify breakpoints according to the methods described herein. In some embodiments, the breakpoint module includes code (e.g., a script) written in S or R that utilizes an appropriate package (e.g., an S package, an R package). In some embodiments, the breakpoint module includes and / or uses an appropriate statistical software package. SOAP de-novo, or a de novo assembly listed in "Wikipedia" (e.g., "Wikipedia," "Sequence Assembly," [online] [retrieved 25 Sep 2013], retrieved from the internet on the world wide web at URL: en.wikipedia.org / wiki / Sequence_assembly). Any suitable de novo assembler, such as assembler, can be used independently or with custom scripts to identify the location of the breakpoint. In some embodiments, given the positions of mates 1 and 2, R and / or one or more bioconductor packages in R (World Wide Web URL: bioconductor.org) are used to assess the similarity of each read to the human reference genome to determine the breakpoint. Any suitable statistical software package or custom script can be used to use the slope to determine the exact location of the breakpoint.

[0124] In some embodiments, one or both mismatched readmates of a mismatched read pair contain substantially similar candidate breakpoints and / or the same candidate breakpoint. In some embodiments, one readmate of a mismatched read pair contains a candidate breakpoint, and the other readmate of the pair does not contain a candidate breakpoint. In some embodiments, the sequence of a first readmate of a mismatched read pair overlaps with the sequence of a second readmate of the pair, and both readmates contain the same candidate breakpoint or a substantially similar candidate breakpoint. As used herein, the term "substantially similar breakpoints" (e.g., "substantially similar candidate breakpoints") refers to breakpoints that are located at the same or substantially the same location relative to a reference genome. Substantially similar breakpoints are sometimes located at different relative positions (e.g., often determined relative to the ends of the reads) on different reads, where the positions of each breakpoint on each read are substantially the same relative to the reference genome. In some cases, two or more reads (e.g., mismatched readmates) contain the same breakpoint and / or substantially similar breakpoint, in which case the position of the breakpoint on each read may be the same or different. In some embodiments, the substantially similar breakpoint is located at the same position on different reads. In some embodiments, the sequence (e.g., base determination) of 1, 2, 3, 4, 5, 6, 7, or 8 nucleotides or more flanking each side of the substantially similar breakpoint is substantially the same sequence. In some embodiments, the substantially similar breakpoint is located on a first read and a second read, in which the first read is the reverse complement of the second read.

[0125] In some embodiments, a subset of reads is selected according to variation in mappability, where each read in the selected subset comprises a minimum length of 20 contiguous bases, 21 contiguous bases, 22 contiguous bases, 23 contiguous bases, 24 contiguous bases, 25 contiguous bases, 26 contiguous bases, 27 contiguous bases, 28 contiguous bases, 29 contiguous bases, 30 contiguous bases, 31 contiguous bases, 32 contiguous bases, 33 contiguous bases, 34 contiguous bases, 35 contiguous bases, 36 contiguous bases, 37 contiguous bases, 38 contiguous bases, 39 contiguous bases, 40 contiguous bases, 50 contiguous bases, 60 contiguous bases, 70 contiguous bases, 80 contiguous bases, 90 contiguous bases, or 100 contiguous bases.

[0126] In some embodiments, a subset of reads is selected according to varying mappability, where each read in the selected subset comprises at least about 10 to about 60, 15 to about 50, 15 to about 40, 15 to about 30, 15 to about 25, or about 15 to about 20 contiguous bases on each side of a candidate breakpoint.

[0127] In some embodiments, two or more reads (e.g., mismatched readmates) in a sample contain substantially similar candidate breakpoints. In some embodiments, two or more, five or more, ten or more, twenty or more, fifty or more, one hundred or more, or one thousand or more reads obtained from a sample contain the same or substantially similar candidate breakpoints. In certain embodiments, reads, mismatched readmates, and / or mismatched read pairs containing substantially similar candidate breakpoints are grouped into subsets. In some embodiments, two or more subsets are identified and / or selected, each subset containing reads containing substantially similar candidate breakpoints. In some embodiments, the first subset of reads and the second subset of reads contain different breakpoints. In some cases, the first subset of reads containing substantially similar candidate breakpoints contains a different breakpoint from the second subset of reads containing substantially similar candidate breakpoints. Any one subset or read will often contain different candidate breakpoints than the breakpoints in another subset of reads.

[0128] In some embodiments, using the systems or methods described herein, one or more subsets of reads containing substantially similar candidate breakpoints are obtained and / or generated from a reference. A subset of mismatched readmates containing substantially similar candidate breakpoints is often obtained and / or generated from a reference sample and a test sample (e.g., a test subject) using the same or substantially the same method. As used herein, a "reference" refers to one or more reference subjects or reference samples. As used herein, a "reference" often refers to data (e.g., reads, a subset of mismatched readmates, a selected set of reads) obtained from one or more reference subjects or reference samples. The reference subjects and / or reference samples are often known or assumed to lack chromosomal alterations. For example, the reference subjects and / or reference samples often do not contain chromosomal alterations. In some embodiments, the reference includes polynucleotides and / or polynucleotide reads derived from a specific genomic region or multiple genomic regions that are not associated with chromosomal alterations.

[0129] Generate a comparison One or more subsets of reads generated and / or obtained from the test sample and the reference can be compared. Often, a subset of reads derived from the sample that contains substantially similar candidate breakpoints is compared with a subset of reads derived from the reference that contains substantially similar candidate breakpoints. In some embodiments, a subset of reads derived from the sample is compared with a subset of reads derived from the reference, where the reads derived from both subsets (i.e., the subset derived from the sample and the subset derived from the reference) contain substantially similar candidate breakpoints. In some embodiments, a subset of reads derived from the sample is compared with a subset of reads derived from the reference, where the reads derived from both subsets (i.e., the subset derived from the sample and the subset derived from the reference) map to the same or substantially the same location in the reference genome. The reads that map to "substantially the same" location in the reference genome refer to the reads that map within a distance of 100,000 kilobases (kb) or less, 50,000 kb or less, 25,000 kb or less, 10,000 kb or less, 5000 kb or less, 1000 kb or less, 500 kb or less, 100 kb or less, 50 kb or less, 25 kb or less, 10 kb or less, 5 kb or less, 1000 base pairs (bp) or less, 500 bp or less, or 100 bp or less. The reads that map to "substantially the same" location in the reference genome refer to the reads that map within a distance of 50, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, or 0 bases from each other, as the case may be. For example, in some cases, a subset of reads derived from the sample is compared to a subset of reads derived from the reference, where both subsets (i.e., the subset derived from the sample and the subset derived from the reference) are selected according to changes in mappability, and where reads from both subsets map to the same or substantially the same location in the reference genome.In some embodiments, selected subsets of reads from the sample and the reference are identified according to the change in mappability, and are mapped to the same bin or part of the genome and compared.The bin or preselected part of the genome to which the reads are mapped can be any suitable length.In some embodiments, the length of the bin or preselected part of the genome to which the reads are mapped is about 100,000 kilobases (kb) or less, 50,000 kb or less, 25,000 kb or less, 10,000 kb or less, 5000 kb or less, 1000 kb or less, 500 kb or less, 100 kb or less, 50 kb or less, 25 kb or less, 10 kb or less, 5 kb or less, 1000 base pairs (bp) or less, or about 500 bp or less.In some embodiments, the number of reads in one or more subsets is quantified and compared. In some embodiments, the number of reads in one or more subsets of the test sample is compared to the number of reads in one or more subsets of the reference.

[0130] Subsets of reads that contain substantially similar candidate breakpoints can be compared by appropriate statistical, graphical, or mathematical methods. In some embodiments, the comparison comprises determining whether the subsets of reads derived from the test sample and the reference are the same or different. In some embodiments, determining whether the subsets of reads derived from the test sample and the reference are the same or different comprises statistical analysis. In some embodiments, the subsets of reads are compared, and a determination is made that the subsets are substantially the same or substantially different. In some embodiments, the number of reads in the subsets is compared, and a determination is made that the number of reads derived from the first subset and the number of reads derived from the second subset are statistically different or not statistically different. As used herein, the terms "statistically different" and "statistical difference" refer to a statistically significant difference. A statistically significant difference can be evaluated by an appropriate method. Non-limiting examples of methods for determining statistically significant differences include determining and / or comparing Z-scores, distributions, correlations (e.g., correlation coefficients, t-tests, k-tests, etc.), uncertainty values, measures of reliability (e.g., confidence intervals, confidence levels, confidence coefficients), etc., or combinations thereof. Calculating and / or comparing distributions may include calculating and / or comparing probability distribution functions (e.g., kernel density estimates). Calculating and / or comparing distributions may include calculating and / or comparing uncertainty values ​​of two or more distributions. The uncertainty value is generally a measure of variance or error and may be any suitable measure of variance or error. Non-limiting examples of uncertainty values ​​include standard deviation, standard error, variance, p-value, mean absolute deviation (MAD), etc., or combinations thereof.

[0131] In some embodiments, comparing (e.g., determining a statistical difference) comprises comparing a subset of reads (e.g., number of reads) to a threshold or range. The terms "threshold" and "threshold value" herein refer to any number calculated using a qualified dataset (e.g., one or more criteria) and used as a limit for a decision (e.g., determining the presence or absence of a breakpoint and / or chromosomal alteration). In certain embodiments, above the threshold, two or more subsets are determined to be statistically different. In certain embodiments, above the threshold, a test sample (e.g., a subject, e.g., a fetus) is determined to contain a chromosomal alteration. In certain embodiments, above the threshold, a subset of reads is determined to contain a breakpoint. In some embodiments, a quantitative value determined for a subset of reads (e.g., read count, read distribution, Z-score, uncertainty value, confidence measure, etc., or a combination thereof) falls within or outside a threshold range, and a decision is made as to the presence or absence of a breakpoint and / or chromosomal alteration. In some embodiments, the threshold or range of values ​​is often calculated by mathematically and / or statistically manipulating the read data (e.g., the number of reads in one or more subsets from the reference and / or test subjects). In some embodiments, the threshold includes an uncertainty value.

[0132] Any suitable threshold or range can be used to determine that two read subsets are significantly different. Optionally, two read subsets that differ by about 0.01 percent (e.g., 0.01 percent of one or both of the subset values) or more are significantly different. Optionally, two subsets that differ by about 0.1 percent or more are significantly different. Optionally, two subsets that differ by about 0.5 percent or more are significantly different. Optionally, two subsets that differ by more than about 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, or about 10% are significantly different. Optionally, two subsets that are significantly different do not overlap within the subsets and / or do not overlap within a range defined by the uncertainty values ​​calculated for one or both subsets. In some cases, the uncertainty value (e.g., standard deviation) is expressed as a sigma. In some cases, two subsets are significantly different when they differ by about 1 or more times the uncertainty value (e.g., 1 sigma). In some cases, two subsets are significantly different when they differ by about 2 or more times the uncertainty value (e.g., deviation, standard deviation, MAD, sigma), about 3 or more times the uncertainty value, about 4 or more times the uncertainty value, about 5 or more times the uncertainty value, about 6 or more times the uncertainty value, about 7 or more times the uncertainty value, about 8 or more times the uncertainty value, about 9 or more times the uncertainty value, or about 10 or more times the uncertainty value. In some cases, two subsets are significantly different when the uncertainty value differs by about 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, or 4.0 times or more. In some embodiments, the confidence level increases as the difference between the two subsets increases. In some cases, the confidence level decreases as the difference between the two subsets decreases and / or the uncertainty value increases.

[0133] In some embodiments, the absence of a statistical difference is determined when the deviation (e.g., standard deviation, mean absolute deviation) between the number of reads of the test sample and the number of reads of the reference is less than about 3.5, less than about 3.4, less than about 3.3, less than about 3.2, less than about 3.1, less than about 3.0, less than about 2.9, less than about 2.8, less than about 2.7, less than about 2.6, less than about 2.5, less than about 2.0, less than about 1.5, or less than about 1.0. For example, in some cases, the absence of a statistical difference is determined when the number of reads of the test sample differs from the number of reads of the reference by less than 3 on a deviation scale (e.g., 3 sigma, 3 MAD). In some embodiments, the absence of a statistical difference is determined when the number of reads of the test sample obtained from a pregnant woman differs from the number of reads of the reference by less than 3 on a deviation scale (e.g., 3 sigma, 3 MAD). In some embodiments, a deviation of less than 3 (e.g., 3 sigma standard deviations) between the number of reads in the test sample and the number of reads in the reference often indicates the absence of a chromosomal alteration. The measure of deviation between the number of reads in the test sample and the number of reads in the reference for one or more reference subjects can be plotted and visualized (e.g., a z-score plot).

[0134] In some embodiments, the comparison comprises comparing Z-scores. In certain embodiments, the comparison comprises comparing Z-scores for a subset of reads from a test sample to one or more Z-scores (e.g., a range of Z-scores) derived from a predetermined threshold, threshold range, and / or standard. In some embodiments, a Z-score and / or threshold determined according to the Z-score is used to determine whether a subset of reads is significantly different from another subset and / or standard. In some embodiments, a subset of reads comprising Z-scores below the threshold range and / or Z-scores within the threshold range (e.g., Z-scores within a certain level of uncertainty, e.g., Z-scores below 3, 2, or 1 sigma, Z-scores within a predetermined range) are not significantly different. In some embodiments, a subset of reads comprising Z-scores above the threshold range and / or Z-scores outside the threshold range (e.g., Z-scores above a predetermined level of uncertainty, e.g., Z-scores above 2, 2.5, 3, 3.5, 4, 5, or 6 sigma, Z-scores outside a predetermined range) are significantly different. In some embodiments, the threshold or predetermined value used for Z-score comparison is at least 2.5, at least 2.75, at least 3.0, at least 3.25, at least 3.5, at least 3.75, at least 4.0, at least 4.25, at least 4.5, at least 4.75, at least 5.0, at least 5.25, at least 5.5, at least 5.75, at least 6.0, at least 6.25, at least 6.5, at least 6.75, at least 7.0, at least 7.25, at least 7.5, at least 7.75, at least 8, at least 8.5, at least 9, at least 9.5, or at least 10.

[0135] The comparing often includes multivariate analysis. In some embodiments, the multivariate analysis includes generating and / or comparing heat maps. In some embodiments, the heat maps can be visually compared, and the presence and / or absence of breakpoints and / or chromosomal alterations can be visually determined. The multivariate analysis sometimes includes mathematical manipulation of two or more data sets (e.g., two or more subsets of reads). For example, sometimes the two or more data sets (e.g., the number of reads, Z-scores, uncertainty values, and / or coefficients derived for two or more subsets of reads) are added, subtracted, divided, multiplied, and / or normalized.

[0136] The comparisons described herein can be performed by a comparison module (e.g., 130) or a machine including a comparison module. In some embodiments, the comparison module includes code (e.g., scripts) written in S or R that utilizes an appropriate package (e.g., S package, R package). For example, heat maps can be generated using "gplots" ("gplots" retrieved from the internet at the world wide web URL: cran.r-project.org / web / packages / gplots / gplots.pdf [online], [2 [Retrieved 25 September 2013]) and available for download at "gplots" (search "gplots" from the Internet at the World Wide Web URL: cran.r-project.org / web / packages / gplots [online], [Retrieved 25 September 2013]). For example, heatmaps can be generated using heatmap.2 and the script: heatmap.2(x) where x is a matrix of Z scores (calculated directly using R) comparing the sample against the reference set for chromosomes A and B. In some embodiments, the comparison module comprises and / or uses a suitable statistical software package.

[0137] Identification of chromosomal alterations In some embodiments, the presence or absence of a chromosomal alteration is determined. Herein, determining the presence or absence of a chromosomal alteration is sometimes referred to as determining or generating an "outcome" or "making a decision." In some embodiments, the presence or absence of a chromosomal alteration is determined according to a comparison. The presence or absence of a chromosomal alteration is often determined by comparing one or more selected subsets of mismatched reads obtained from the sample with one or more selected subsets of mismatched reads obtained from the reference. In some embodiments, the number of mismatched readmates containing substantially similar candidate breakpoints determined for the test sample is compared with the number of mismatched readmates containing substantially similar candidate breakpoints determined for the reference.

[0138] The absence of chromosomal alterations in a test subject (e.g., a fetus) can be determined according to the comparison. In some embodiments, the absence of chromosomal alterations is determined when a selected subset of mismatched readmates from the test sample contains the same candidate breakpoints or substantially similar candidate breakpoints as a selected subset of mismatched readmates obtained from the reference. In some cases, the absence of chromosomal alterations in a test subject is determined when one or more, or all, subsets of reads from the test sample are not different (e.g., not statistically different) from a subset of reads from the reference. In certain embodiments, determining the absence of chromosomal alterations comprises determining the absence of one or more breakpoints in the test sample (e.g., in the test subject) according to the comparison.

[0139] The presence of a chromosomal alteration in one or more test subjects (e.g., fetuses) can be determined according to the comparison. In certain embodiments, the presence of a chromosomal alteration in a test subject is determined when one or more subsets of reads from the test sample (e.g., a selected subset of reads) are different (e.g., statistically different) from one or more subsets of reads from the reference. In some cases, the presence of a chromosomal alteration in a test subject is determined by identifying a substantially greater number of reads from the test sample that contain a candidate breakpoint or a substantially similar breakpoint compared to the number of reads from the reference that contain a candidate breakpoint or a substantially similar breakpoint, where the candidate breakpoints and / or substantially similar breakpoints of the test sample and the reference are substantially similar. In some embodiments, the presence of a chromosomal alteration is determined when a selected subset of mismatched readmates from the test sample contain candidate breakpoints and / or breakpoints that are substantially different (e.g., statistically different) from the candidate breakpoints in the selected subset of mismatched readmates obtained from the reference. In certain embodiments, determining the presence of a chromosomal alteration comprises identifying one or more breakpoints in the test sample (e.g., in the test subject) by comparing the candidate breakpoints of the test sample with the candidate breakpoints of the reference. In some embodiments, determining the presence of a chromosomal alteration comprises identifying a subset of reads from the test sample that contain substantially similar breakpoints, thereby determining that the reference (e.g., a subset of reads from the reference sample) does not contain a candidate breakpoint that is substantially similar to the breakpoint identified in the test sample. In some embodiments, determining the presence of a chromosomal alteration comprises identifying a first breakpoint and a second breakpoint of a chromosomal alteration (e.g., a translocation or insertion) in the test subject. In some embodiments, determining the presence of a chromosomal deletion comprises identifying a single breakpoint in the test subject. In some embodiments, determining the presence of a chromosomal alteration comprises providing one or more breakpoints associated with the chromosomal alteration identified in the test subject.

[0140] In some cases, the candidate breakpoint includes the true breakpoint, but in some cases, the candidate breakpoint does not include the true breakpoint. Without being limited by theory, in some cases, the candidate breakpoint is identified according to a mapping artifact caused by misalignment of two regions of the read with respect to two different chromosomes or non-adjacent positions on a chromosome, in which case the candidate breakpoint does not include the true breakpoint. Mapping artifacts and / or misalignments often occur in the test sample and the reference sample (e.g., a sample known not to contain chromosomal alterations), resulting in a candidate breakpoint that does not actually contain the true breakpoint. In some embodiments, the presence or absence of a breakpoint is determined according to a comparison. For example, a candidate breakpoint that does not contain a breakpoint and a candidate breakpoint that contains a true breakpoint can often be identified and / or distinguished from each other by comparing the candidate breakpoint of the test sample with the candidate breakpoint of the reference. For example, in some cases, the breakpoint is identified by comparing a subset of reads from the test sample with a subset of reads from the reference, in which both subsets contain substantially similar candidate breakpoints. A subset of reads from the test sample that contain a substantially similar candidate breakpoint is often determined to contain the true breakpoint following comparison, thereby determining that the reference (e.g., a subset of reads from the reference sample) does not contain a subset of reads that contain a candidate breakpoint that is substantially similar to the breakpoint identified in the test sample.

[0141] In some embodiments, the location and / or position of the breakpoint is determined according to the location and / or position of the candidate breakpoint (e.g., the candidate breakpoint includes a breakpoint). In certain embodiments, the location and / or position of the breakpoint is determined by a method described herein to determine the location and / or position of the candidate breakpoint. In some embodiments, the location and / or position of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more breakpoints in the test subject are determined.

[0142] In some embodiments, the location and / or position of a chromosomal change is determined according to the location and / or position of one or more breakpoints. In some embodiments, a first breakpoint is determined in a test sample, and the location and / or position of the first breakpoint indicates the location and / or position of a chromosomal change (e.g., translocation, insertion). For example, if a first translocation is located at the end of a chromosome, a single breakpoint of the first translocation can be identified, thereby indicating the location of the first translocation event. In some embodiments, a first breakpoint and a second breakpoint are determined in a test sample, and the location and / or position of the first and second breakpoints indicate the location and / or position of a chromosomal change (e.g., translocation, insertion). For example, when a chromosome contains an insertion, two breakpoints (e.g., a 5' breakpoint and a 3' breakpoint representing the 5' and 3' sides of the inserted segment) can be determined, and in this case, all sequence reads are mapped to the same strand (e.g., the plus strand) of a reference genome. When a translocation (for example, a translocation involving the insertion of a segment into a chromosome) occurs within a chromosome, two breakpoints can often be identified. In a balanced translocation between a first chromosome and a second chromosome, where a complete segment is exchanged between the two chromosomes, one or two breakpoints can be identified on the first chromosome (for example, the 5' and / or 3' breakpoint on the first chromosome), and one or two breakpoints can be identified on the second chromosome (for example, the 5' and / or 3' breakpoint on the second chromosome), where all sequence reads are mapped to the same strand (for example, the plus strand) of the reference genome. In some embodiments, the location and / or position of the breakpoint is determined in the test sample, where the location and / or position of the breakpoint indicates the location and / or position of the chromosome deletion.

[0143] The methods described herein can provide a determination of the presence or absence of a chromosomal alteration (e.g., a fetal translocation) for a sample, thereby providing an outcome (e.g., providing an outcome that determines the presence or absence of a chromosomal alteration (e.g., a fetal translocation)). The presence or absence of a chromosomal alteration can be determined by converting, analyzing, and / or manipulating sequence reads mapped to a reference genome. In some embodiments, determining the outcome includes analyzing nucleic acids from a pregnant woman.

[0144] Any other suitable criteria can be incorporated with the number of reads of test sample to determine the presence or absence of chromosomal alteration for the test region of test sample.For example, the determination of fetal fraction can be incorporated with the number of reads of test sample to determine the presence or absence of chromosomal alteration.Can utilize suitable process for quantifying fetal fraction, non-limiting examples of which include mass spectrometry process, sequencing process, or a combination thereof.

[0145] In some embodiments, a determination of the presence or absence of a chromosomal alteration (e.g., a translocation) is made according to a decision range. In certain embodiments, a decision (e.g., a decision determining the presence or absence of a chromosomal alteration, e.g., an outcome) is made when a value (e.g., a measurement and / or a level of uncertainty) or collection of values ​​falls within a predefined range (e.g., an area, decision range). In some embodiments, a decision range is defined according to a collection of values ​​obtained from the same patient sample. In certain embodiments, a decision range is defined according to a collection of values ​​derived from the same chromosome or segment thereof. In some embodiments, a decision range based on a ploidy determination is defined according to a confidence level (e.g., a high confidence level, e.g., a low level of uncertainty) and / or a fetal fraction. In some embodiments, the decision range is defined according to a ploidy determination and a fetal fraction of about 2.0% or more, about 2.5% or more, about 3% or more, about 3.25% or more, about 3.5% or more, about 3.75% or more, or about 4.0% or more. For example, in some embodiments, a determination that a fetus contains trisomy 21 is made based on a ploidy determination of greater than 1.25 with a fetal fraction determination of 2% or more or 4% or more for a sample obtained from a pregnant woman carrying a fetus. In certain embodiments, a determination that a fetus is euploid is made based on a ploidy determination of less than 1.25 with a fetal fraction determination of 2% or more or 4% or more for a sample obtained from a pregnant woman carrying a fetus. In some embodiments, the decision range is defined by a confidence level of about 99% or greater, about 99.1% or greater, about 99.2% or greater, about 99.3% or greater, about 99.4% or greater, about 99.5% or greater, about 99.6% or greater, about 99.7% or greater, about 99.8% or greater, or about 99.9% or greater. In some embodiments, the decision range is not used to make the decision.In some embodiments, the determination is made using decision ranges and additional data or information. In some embodiments, the determination is made based on ploidy values ​​without the use of decision ranges. In some embodiments, the determination is made without calculating ploidy values. In some embodiments, the determination is made based on visual inspection of the profile (e.g., visual inspection at the genome segment level). The determination can be made in any suitable manner based, fully or in part, on the determinations, values, and / or data obtained by the methods described herein, non-limiting examples of which include mappability changes, mappability thresholds, relationships, comparisons, uncertainty and / or confidence determinations, z-scores, etc., or combinations thereof.

[0146] In some embodiments, the no-decision zone is an area where no decision is made. In some embodiments, the no-decision zone is defined by a value or collection of values ​​that indicates low precision, high risk, high error, low confidence level, high uncertainty level, etc., or a combination thereof. In some embodiments, the no-decision zone is defined in part by a fetal fraction of about 5% or less, about 4% or less, about 3% or less, about 2.5% or less, about 2.0% or less, about 1.5% or less, or about 1.0% or less.

[0147] In some embodiments, the method for determining the presence or absence of a chromosomal alteration (e.g., a translocation) is performed with an accuracy of at least about 90% to about 100%. For example, the presence or absence of a chromosomal alteration can be determined with an accuracy of at least about 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, or 99.9%. In some embodiments, the presence or absence of a chromosomal alteration is determined with an accuracy that is approximately the same as or greater than the accuracy when using other methods of determining chromosomal alterations (e.g., karyotyping). In some embodiments, the presence or absence of a chromosomal alteration is determined with an accuracy that provides a confidence interval (CI) of about 80% to about 100%. For example, the confidence interval (CI) can be about 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0148] In certain embodiments, one or more of the sensitivity, specificity, and / or confidence level are expressed as a percentage. In some embodiments, the percentage is greater than about 90% (e.g., about 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%), or greater than 99% (e.g., about 99.5% or greater, about 99.9% or greater, about 99.95% or greater, or about 99.99% or greater) for each variable independently. In some embodiments, the coefficient of variation (CV) is expressed as a percentage, and in some cases the percentage is about 10% or less (e.g., about 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1%, or less than 1% (e.g., about 0.5% or less, about 0.1% or less, about 0.05% or less, about 0.01% or less)). In certain embodiments, the probability (e.g., the probability that a particular outcome is not due to chance) is expressed as a Z-score, p-value, or the result of a t-test. In some embodiments, the variance, confidence interval, sensitivity, specificity, etc. (e.g., collectively referred to as reliability parameters) measured for the outcome can be generated using one or more data processing operations described herein. Specific examples of generating outcomes and associated confidence levels are described in the Examples section and in International Patent Application No. PCT / US12 / 59123 (WO2013 / 052913), the entire contents of which are incorporated herein by reference, including all text, tables, formulas, and figures.

[0149] As used herein, the term "sensitivity" refers to the number of true positives divided by the number obtained by adding the number of false negatives to the number of true positives, where sensitivity (sens) may be in the range of 0≦sens≦1. As used herein, the term "specificity" refers to the number of true negatives divided by the number obtained by adding the number of false positives to the number of true negatives, where specificity (spec) may be in the range of 0≦spec≦1. In some embodiments, methods are selected in which the sensitivity and specificity are equal to 1 or 100%, or are close to 1 (e.g., between about 90% and about 99%), as the case may be. In some embodiments, a method is selected that has a sensitivity equal to 1 or 100%, and in certain embodiments, a method is selected that has a sensitivity close to 1 (e.g., about 90% sensitivity, about 91% sensitivity, about 92% sensitivity, about 93% sensitivity, about 94% sensitivity, about 95% sensitivity, about 96% sensitivity, about 97% sensitivity, about 98% sensitivity, or about 99% sensitivity). In some embodiments, a method is selected that has a specificity equal to 1 or 100%, and in certain embodiments, a method is selected that has a specificity close to 1 (e.g., about 90% specificity, about 91% specificity, about 92% specificity, about 93% specificity, about 94% specificity, about 95% specificity, about 96% specificity, about 97% specificity, about 98% specificity, or about 99% specificity).

[0150] Ideally, the number of false negatives is equal to or close to zero, so that when a subject actually has at least one chromosomal alteration, the subject is not mistakenly identified as not having at least one chromosomal alteration.Conversely, the ability of a prediction algorithm to accurately classify negatives is often also evaluated, which is a measure complementary to sensitivity.Ideally, the number of false positives is also equal to or close to zero, so that when a subject does not have the chromosomal alteration being evaluated, the subject is not mistakenly identified as having at least one chromosomal alteration.

[0151] In some embodiments, the outcome can be generated after performing one or more of the processing steps described herein. In certain embodiments, the outcome is generated as a result of one of the processing steps described herein, and in some embodiments, the outcome can be generated after performing each statistical and / or mathematical operation on the data set. The outcome related to determining the presence or absence of a chromosomal alteration can be expressed in any appropriate form, including, but not limited to, a probability (e.g., odds ratio, p-value), likelihood, in-cluster or out-cluster value, above-threshold value or below-threshold value, value within a range (e.g., threshold range), value with a variance or confidence measure, or risk factor associated with the presence or absence of a chromosomal alteration for a subject or sample. In certain embodiments, comparison between samples allows confirmation of sample identity (e.g., allowing identification of repeated samples and / or mixed samples (e.g., mislabeled samples, combined samples, etc.)).

[0152] In some embodiments, the outcome includes a value above or below a predetermined threshold or cutoff value (e.g., a value greater than 1, a value less than 1) and an associated uncertainty or confidence level for the value. In certain embodiments, the predetermined threshold or cutoff value is a predicted level or a range of predicted levels. The outcome may also describe assumptions used in data processing. In certain embodiments, the outcome includes a value that is within or outside a predetermined range of values ​​(e.g., a threshold range) and an associated uncertainty or confidence level for the value being within or outside the range. In some embodiments, the outcome includes a value that is equal to a predetermined value (e.g., equal to 1, equal to zero) or a value within a predetermined range of values ​​and an associated uncertainty or confidence level for the value being equal to, within, or outside the range. The outcome is sometimes displayed graphically as a plot (e.g., a profile plot).

[0153] As noted above, outcomes can be characterized as true positive, true negative, false positive, or false negative. As used herein, the term "true positive" refers to a subject who is correctly diagnosed as having a chromosomal alteration. As used herein, the term "false positive" refers to a subject who is incorrectly identified as having a chromosomal alteration. As used herein, the term "true negative" refers to a subject who is correctly identified as not having a chromosomal alteration. As used herein, the term "false negative" refers to a subject who is incorrectly identified as not having a chromosomal alteration. Two measures of efficacy for any given method can be calculated based on the following incidence ratios: (i) a sensitivity value, which is generally the proportion of predicted positives that are correctly identified as positive; and (ii) a specificity value, which is generally the proportion of predicted negatives that are correctly identified as negative.

[0154] In certain embodiments, the presence or absence of chromosomal alterations is determined from the nucleic acid in the sample.In some embodiments, the mutations that are detected or not detected are present in the sample nucleic acid from one source, but not present in the sample nucleic acid from another source.Non-limiting examples of sources include placental nucleic acid, fetal nucleic acid, maternal nucleic acid, cancer cell nucleic acid, non-cancer cell nucleic acid, etc., and combinations thereof.In non-limiting examples, the specific chromosomal alterations that are detected or not detected are (i) present in placental nucleic acid but not present in fetal nucleic acid and not present in maternal nucleic acid, (ii) present in fetal nucleic acid but not present in maternal nucleic acid, or (iii) present in maternal nucleic acid but not present in fetal nucleic acid.In some embodiments, the presence or absence of chromosomal alterations (e.g., translocations) is determined for the fetus.In such embodiments, the presence or absence of chromosomal alterations (e.g., translocations) is also determined for the mother.

[0155] Some chromosomal alterations (e.g., translocations, insertions, deletions, inversions) that may be detected by the methods and / or systems described herein are associated with disorders or diseases, non-limiting examples of which are shown in Table 1.

[0156]

Table 1-1

Table 1-2

Table 1-3

Table 1-4

Table 1-5

Table 1-6

Table 1-7

Table 1-8

Table 1-9

Table 1-10

Table 1-11

Table 1-12

Table 1-13

Table 1-14

Table 1-15

Table 1-16

Table 1-17

Table 1-18

Table 1-19

Table 1-20

Table 1-21

Table 1-22

Table 1-23

Table 1-24

Table 1-25

Table 1-26

Table 1-27

Table 1-28

Table 1-29

Table 1-30

Table 1-31

Table 1-32

Table 1-33

Table 1-34

Table 1-35

Table 1-36

[0157] Chromosomal alterations are sometimes associated with medical conditions (e.g., Table 1). The outcome determining a chromosomal alteration is sometimes an outcome determining the presence or absence of a condition (e.g., a medical condition), disease, syndrome, or abnormality, or includes the detection of a condition, disease, syndrome, or abnormality (e.g., the non-limiting examples listed in Table 1). In certain embodiments, diagnosis includes evaluation of an outcome. An outcome determining the presence or absence of a condition (e.g., a medical condition), disease, syndrome, or abnormality by the methods described herein can sometimes be independently verified by further investigation (e.g., by karyotyping and / or amniocentesis). Data analysis and processing can provide one or more outcomes. As used herein, the term "outcome" can refer to the result of data processing that facilitates determining the presence or absence of a chromosomal alteration (e.g., translocation, deletion). In certain embodiments, the term "outcome" as used herein refers to a conclusion that predicts and / or determines the presence or absence of a chromosomal alteration (e.g., translocation, deletion). In certain embodiments, the term "outcome" as used herein refers to a conclusion that predicts and / or determines the risk or probability of the presence or absence of a chromosomal alteration (e.g., a translocation) in a subject (e.g., a fetus). Diagnosis optionally involves the use of an outcome. For example, a medical professional can analyze the outcome and provide a diagnosis based, or based in part, on the outcome. In some embodiments, determining, detecting, or diagnosing a condition, syndrome, or abnormality (e.g., listed in Table 1) involves the use of an outcome that determines the presence or absence of a chromosomal alteration. In some embodiments, an outcome based on the identification of discordant read pairs, mapping features, and breakpoints determines the presence or absence of a chromosomal alteration. In certain embodiments, an outcome generated utilizing one or more methods or systems described herein determines the presence or absence of one or more conditions, syndromes, or abnormalities listed in Table 1. In certain embodiments, diagnosis involves determining the presence or absence of a condition, syndrome, or abnormality.A diagnosis often involves determining the nature and / or cause of a chromosomal alteration as the cause of a condition, syndrome, or abnormality. In certain embodiments, the outcome is not a diagnosis. In the context of one or more probability studies, the outcome often includes one or more numerical values ​​generated using the processes described herein. Risk or probability studies may include, but are not limited to, uncertainty values, measures of variability, confidence levels, sensitivity, specificity, standard deviations, coefficients of variation (CV) and / or confidence levels, Z-scores, chi values, phi values, ploidy values, matched fetal fractions, area ratios, median levels, etc., or combinations thereof. Probability studies can facilitate determining whether a subject is at risk of having a chromosomal alteration or whether a subject has a chromosomal alteration, and outcomes determining the presence or absence of a genetic disorder often include such studies.

[0158] The outcome is sometimes a phenotype. The outcome is sometimes a phenotype with an associated confidence level (e.g., an uncertainty value, e.g., the fetus is positive for autism at a 99% confidence level, the pregnant woman is carrying a male fetus at a 95% confidence level, and the test subject is negative for cancer associated with a chromosomal alteration at a 95% confidence level). Different methods of generating outcome values ​​may sometimes result in different types of results. Generally, there are four possible scores or calls that can be made based on the outcome values ​​generated using the methods described herein: true positive, false positive, true negative, and false negative. As used herein, the terms "score," "scores," "call," and "calls" refer to calculating the probability that a particular chromosomal alteration is present or absent in a subject / sample. The score value can be used, for example, to determine the variation, difference, or ratio of mapped sequence reads that may correspond to a chromosomal alteration. For example, calculating a positive score for a selected chromosomal alteration or portion from a dataset relative to a reference genome can result in the identification of the presence or absence of a chromosomal alteration, in some cases associated with a medical condition (e.g., cancer, autism, etc.). In some embodiments, the outcome comprises a level, a profile, and / or a plot (e.g., a plot of a profile). In embodiments where the outcome comprises a profile, an appropriate profile or combination of profiles can be used for the outcome. Non-limiting examples of profiles that can be used for the outcome include z-score profiles, p-value profiles, chi-value profiles, phi-value profiles, etc., and combinations thereof.

[0159] A medical professional or other qualified person who receives a report containing one or more outcomes determining the presence or absence of a chromosomal alteration can use the data provided in the report to make a decision about the condition of the test subject or patient. In some embodiments, the medical professional can make a recommendation based on the provided outcomes. In some embodiments, the medical professional or qualified person can provide the test subject or patient with a decision or score regarding the presence or absence of a chromosomal alteration based on one or more outcome values ​​and associated reliability parameters provided in the report. In certain embodiments, the medical professional or qualified person manually creates or makes a score using visual observation of the provided report. In certain embodiments, the score or decision is optionally created by an automated routine embedded in software, and the medical professional or qualified person reviews the score or decision for accuracy before providing the information to the test subject or patient. As used herein, the term "receiving a report" refers to obtaining, via a communication means, a textual and / or graphical display including an outcome that, upon review, allows a medical professional or other qualified individual to make a determination regarding the presence or absence of a chromosomal alteration in a test subject or patient. The report may be generated by a computer, generated by manual data entry, or communicated using electronic means (e.g., via the Internet, via a computer, via fax, from one network location to another in the same or different physical facilities), or by another method of sending or receiving data (e.g., mail, courier, etc.). In some embodiments, the outcome is transmitted to the medical professional via a suitable medium, including, but not limited to, a non-transitory computer-readable storage medium and / or in audio, written, or file form. The file may be, for example, but not limited to, an audio file, a non-transitory computer-readable file, a paper file, a laboratory file, or a medical record file.

[0160] As used herein, the term "providing an outcome" and its grammatical equivalents may also refer to methods for obtaining such information, including, without limitation, obtaining the information from a laboratory (e.g., a laboratory file). A laboratory file may be created in a laboratory that performed one or more assays or may be created by one or more data processing steps that determine the presence or absence of a medical condition. The laboratory may be in the same location as the medical professional who identifies the presence or absence of a medical condition from the laboratory file, or it may be in a different location (e.g., within another country). For example, a laboratory file may be created in one location and transmitted to another location, where the information therein is transmitted to the pregnant subject. In certain embodiments, the laboratory file may be in a tangible form or in an electronic form (e.g., a computer-readable form).

[0161] In some embodiments, the outcome can be provided from the laboratory to a medical professional, physician, or licensed individual, who can make a diagnosis based on the outcome. In some embodiments, the outcome can be provided from the laboratory to a medical professional, physician, or licensed individual, who can make a diagnosis based in part on the outcome, along with additional data and / or information and other outcomes.

[0162] A medical professional or qualified individual can provide appropriate recommendations based on one or more outcomes provided in the report. Non-limiting examples of recommendations that may be provided based on the provided outcome report include surgery, radiation therapy, chemotherapy, genetic counseling, post-natal treatment solutions (e.g., life planning, long-term nursing care, medication, symptomatic treatment), abortion, organ transplant, blood transfusion, etc., or combinations of the foregoing.

[0163] Laboratory personnel (e.g., laboratory manager) can analyze the values ​​(e.g., number of test sample reads, number of reference reads, level of deviation) that underlie the determination of the presence or absence of a chromosomal alteration. For a delicate or questionable determination regarding the presence or absence of a chromosomal alteration, laboratory personnel can reorder the same test and / or order a different test (e.g., karyotype analysis and / or amniocentesis in the case of some fetal chromosomal alterations) using the same or a different sample nucleic acid from the test subject.

[0164] The outcomes are typically provided to a medical professional (e.g., a laboratory technician or administrator; a physician or assistant). The outcomes are often provided by an outcome module. The outcome module may include a suitable statistical software package. In certain embodiments, the outcomes are provided by a plotting module. Suitable statistical software often includes a suitable plotting module. In some embodiments, the outcome module generates and / or compares Z-scores.

[0165] In some embodiments, the plotting module processes and / or converts the data and / or information into a suitable visual media, non-limiting examples of which include charts, plots, graphs, etc., or combinations thereof. In some embodiments, the plotting module processes, converts, and / or transfers the data and / or information for display on a suitable display (e.g., a monitor, LED, LCD, CRT, etc., or combinations thereof), on a printer (e.g., a printed paper presentation), on a suitable peripheral, or device. In certain embodiments, the plotting module produces a visual display of the relationships and / or outcomes.

[0166] In certain embodiments, the outcome is provided on a peripheral or component of the machine or on the machine itself. For example, in some cases, the outcome is provided by a printer or display. In some embodiments, the outcome determining the presence or absence of a chromosomal alteration and / or an associated disease or disorder is provided to a medical professional in the form of a report, and in certain embodiments, the report includes a presentation of an outcome value and associated reliability parameters. Generally, the outcome can be displayed in an appropriate format that facilitates the determination of the presence or absence of a chromosomal alteration and / or medical condition. Non-limiting examples of formats suitable for use in reporting and / or displaying a dataset or reporting an outcome include digital data, graphs, 2D graphs, 3D graphs, and 4D graphs, photographs, statistical charts, diagrams, bar graphs, pie charts, schematic diagrams, flowcharts, scatter plots, maps, histograms, density plots, function graphs, circuit diagrams, block diagrams, bubble maps, signal space diagrams, contour diagrams, cartograms, radar charts, Venn diagrams, nomograms, etc., and combinations of the foregoing. Various examples of outcome indicators are shown in the figures and described herein.

[0167] Data filtering and processing In some embodiments, one or more processing steps may include one or more filtering steps. As used herein, the term "filtering" refers to excluding portions of data or datasets from consideration and retaining a subset of the data. Sequence reads can be selected for elimination based on any appropriate criteria, including, but not limited to, redundant data (e.g., redundant or overlapping mapped reads), uninformative data, over- or under-represented sequences, noise data, or the like, or combinations of the foregoing. The filtering process often involves excluding one or more reads and / or read pairs (e.g., mismatched read pairs) from consideration. Reducing the number of reads, read pairs, and / or reads containing candidate breakpoints from a dataset analyzed for the presence or absence of chromosomal alterations reduces the complexity and / or dimensionality of the dataset, often increasing the speed at which chromosomal alterations are searched for and / or identified by two or more orders of magnitude, in some cases.

[0168] In some embodiments, the system or method described herein comprises filtering reads, mismatched readmates, and / or mismatched read pairs.The filtering step can be performed before or after the steps of identifying mismatched readmates, characterizing the mappability of a plurality of sequence read subsequences, providing mappability changes, selecting a subset of mismatched readmates, identifying candidate breakpoints, comparing a subset of reads, comparing candidate breakpoints, identifying breakpoints, or comparing the number of mismatched readmates from a sample with a standard.The filtering step is often performed before determining the presence or absence of one or more chromosomal alterations.

[0169] The filtering step is often performed by a system or module. A system or module performing the filtering step is referred to herein as a filter module. In some embodiments, the filtering module includes code (e.g., a script) written in S or R that utilizes an appropriate package (e.g., an S package, an R package). For example, the filter module can include and utilize one or more SAM tools (searchable from the Internet at the World Wide Web URL: samtools.sourceforge.net, "SAMtools" [online] [retrieved September 25, 2013]). For example, the sum of all applicable flags can be used to identify concordant reads, where the selection of concordant or PCR duplicate reads is "if(bitwiseA==83||bitwiseA==163||bitwiseA==99||bitwiseA==147||bitwiseA>=1024)," where bitwiseA is the sum of all applicable flags in the SAM-formatted file.

[0170] A filter often accepts a dataset (e.g., a set of reads) as input and outputs a filtered subset of data (e.g., a subset of reads, e.g., filtered reads). In certain embodiments, reads filtered out in the filtering process are often rejected and / or excluded from further analysis (e.g., statistical analysis). The filtering step often includes excluding a read from the set of reads. In some embodiments, the filtering step includes excluding one or both readmates of a mismatched read pair. In some embodiments, the filtering step includes excluding multiple reads from the set of reads. In some cases, the filtering step does not exclude any reads.

[0171] The filtering step often excludes, discards, or rejects certain leads from a dataset according to a predetermined conditional query. For example, a filter may receive input leads from a system or another module and perform a filtering function on the received leads, accepting only those input leads that satisfy a condition. In some embodiments, a filter may receive input leads from a system or another module and perform a filtering function on the received leads, filtering out, discarding, or rejecting only those input leads that satisfy a condition. In some embodiments, the conditional query comprises a yes / no or true / false decision. For example, a "true" or "yes" value, as the case may be, is assigned to one or more leads for which the query's condition is met, and a "false" or "no" value is assigned to leads for which the query's condition is not met.

[0172] In some embodiments, the filtering step includes filtering out, rejecting, and / or discarding non-mismatched reads (e.g., matching reads). In some embodiments, the conditional query of the filter (e.g., 20) includes determining the presence or absence of mismatched paired reads. In some embodiments, non-mismatched paired reads are filtered out, rejected, and / or rejected. In some embodiments, non-mismatched paired reads are assigned a "false" or "no" value and filtered out, rejected, and / or rejected by the module. In some embodiments, non-mismatched reads are not allowed to pass the filter 20. In some embodiments, non-mismatched reads are deleted, moved to a trash file or temporary file (e.g., 10), or retained in their original data location and / or format. In certain embodiments, mismatched paired reads are identified and / or retained in an output subset of filtered reads. In certain embodiments, mismatched paired reads are identified and sent to another module or filter. In some embodiments, mismatched paired reads are identified and assigned a "true" or "yes" value and retained in the output subset of filtered reads (e.g., mismatched read pairs). In some embodiments, mismatched reads are accepted and passed through filter 20. Reads can be filtered according to the presence or absence of mismatched and / or non-mismatched reads via a mismatched read identification module (e.g., filter 20). The mismatched read identification module optionally includes a filter configured to filter out, reject, or discard non-mismatched reads.

[0173] In some embodiments, the filtering step includes filtering out, rejecting, and / or discarding reads that are exact duplicates. Duplicate reads are referred to herein as PCR duplicates. In some embodiments, the conditional query of a filter (e.g., 30) includes determining the presence or absence of PCR duplicates. In some embodiments, PCR duplicates are assigned a "true" or "yes" value and are filtered, rejected, and / or rejected by the module. In some embodiments, PCR duplicates are not allowed to pass filter 30. In some embodiments, PCR duplicates are deleted, moved to a trash file or temporary file (e.g., 10), or retained in their original data location and / or format. Reads that represent a set of duplicate reads are referred to herein as "represented reads." In some embodiments, represented reads and unique reads are retained in an output subset of filtered reads. Leads and unique reads are often identified and / or sent to another module or filter. In some embodiments, representative reads and unique reads are identified, assigned a "false" or "no" value, and retained in an output subset of filtered reads. In some embodiments, representative reads and unique reads are accepted into and / or passed through a filter (e.g., 30). Reads can be filtered according to PCR replicates through a PCR replicate filter (e.g., filter 30). The filter module optionally includes a PCR replicate filter.

[0174] In some embodiments, the filtering step includes filtering out, rejecting, and / or discarding reads with low sequencing quality. Low sequencing quality reads are often reads with a PHRED score equal to or less than about 40, about 35, about 30, about 25, about 20, about 15, about 10, or about 5. In some embodiments, the conditional query of the filter (e.g., 40) includes determining the presence or absence of low sequencing quality reads. In some embodiments, low sequencing quality reads are assigned a "true" or "yes" value and are filtered, rejected, and / or discarded by the module. In some embodiments, low sequencing quality reads are not allowed to pass the filter 40. In some embodiments, low sequencing quality reads are deleted, moved to a trash file or a temporary file (e.g., 10), or retained in their original data location and / or format. Non-low quality reads (e.g., high quality reads) are optionally retained in the output subset of filtered reads. Reads that are not low quality are often identified and / or sent to another module or filter. In some embodiments, reads that are not low quality are identified, assigned a "false" or "no" value, and retained in an output subset of filtered reads. In some embodiments, reads that are not low quality are accepted into and / or passed through a filter (e.g., 40). Reads can be filtered according to sequencing quality through a sequencing quality filter (e.g., filter 40). The filter module optionally includes a sequencing quality filter.

[0175] In some embodiments, the filtering step includes excluding, rejecting, and / or discarding reads whose sequence read subsequences include mapping discontinuities. Mapping discontinuities often refer to sequence read subsequences of a read, where three or more (e.g., >2) fragments map to different (e.g., unexpected) locations on the reference genome (e.g., reads that include stepwise multiple alignments). Mapping discontinuities sometimes refer to three or more in silico fragments for reads that map to (i) different chromosomes (e.g., three or more different chromosomes), (ii) different locations, each separated by more than a predetermined fragment size (e.g., more than 300 bp, more than 500 bp, more than 1000 bp, more than 5000 bp, or more than 10,000 bp), (iii) different orientations and / or reverse orientations, etc., or a combination thereof. For example, a mapping discontinuity may refer to a sequence read subsequence in which two fragments map in opposite orientations and a third fragment maps to a different chromosome. In some embodiments, the conditional query of a filter (e.g., 60) includes determining the presence or absence of reads containing mapping discontinuities. In some embodiments, reads containing mapping discontinuities are assigned a "true" or "yes" value and filtered out, rejected, and / or discarded by the module. In some embodiments, reads containing mapping discontinuities are not allowed to pass filter 60. In some embodiments, reads containing mapping discontinuities are deleted, moved to a trash file or temporary file (e.g., 10), or retained in their original data location and / or format. Reads that do not contain mapping discontinuities are optionally retained in the output subset of filtered reads. Reads that do not contain mapping discontinuities are often identified and / or sent to another module or filter. In some embodiments, reads that do not contain a mapping discontinuity are identified, assigned a "false" or "no" value, and retained in the output subset of filtered reads.In some embodiments, reads that do not include mapping discontinuities are accepted into and / or passed through a filter (e.g., 60). Reads can be filtered according to mapping discontinuities through a mapping discontinuity filter (e.g., filter 60). The filter module optionally includes a mapping discontinuity filter.

[0176] In some embodiments, the filtering step comprises filtering out, rejecting, and / or discarding reads that contain unmappable sequence read subsequences. In some embodiments, the filtering step comprises filtering out, rejecting, and / or discarding reads that contain one or more, more than two, more than three, more than four, more than five, more than six, more than seven, more than eight, more than nine, more than ten, more than eleven, more than twelve, more than thirteen, more than fourteen, or more than fifteen unmappable sequence read subsequences. "Unmappable" refers to the inability to unambiguously map a polynucleotide to a location in a reference genome (e.g., a human reference genome). In some embodiments, the conditional query of a filter (e.g., 70) comprises determining the presence or absence of reads that contain unmappable sequence read subsequences. In some embodiments, reads that contain unmappable sequence read subsequences are assigned a "true" or "yes" value and filtered out, rejected, and / or rejected by the module. In some embodiments, reads that include unmappable sequence read subsequences are not allowed to pass through the filter 70. In some embodiments, reads that include unmappable sequence read subsequences are deleted, moved to a trash file or temporary file (e.g., 10), or retained in their original data location and / or format. Reads that do not include unmappable sequence read subsequences are optionally retained in the output subset of filtered reads. Reads that do not include unmappable sequence read subsequences are often identified and / or sent to another module or filter. In some embodiments, reads that do not include unmappable sequence read subsequences are identified, assigned a "false" or "no" value, and retained in the output subset of filtered reads. In some embodiments, reads that do not include unmappable sequence read subsequences are accepted into and / or passed through a filter (e.g., 70).Reads can be filtered according to unmappable sequence read subsequences via a mapping filter (e.g., filter 70). The filter module optionally includes a mapping filter.

[0177] In some embodiments, the filtering step includes excluding, rejecting, and / or discarding reads that include sequence read subsequences that map to mitochondrial DNA. In some embodiments, the filtering step includes excluding, rejecting, and / or discarding reads that include one or more, more than two, more than three, more than four, more than five, more than six, more than seven, more than eight, more than nine, more than ten, more than eleven, more than twelve, more than thirteen, more than fourteen, or more than fifteen sequence read subsequences that map to mitochondrial DNA. In some embodiments, the conditional query of the filter (e.g., 80) includes determining the presence or absence of reads that include sequence read subsequences that map to mitochondrial DNA. In some embodiments, reads that include sequence read subsequences that map to mitochondrial DNA are assigned a "true" or "yes" value and are filtered, rejected, and / or rejected by the module. In some embodiments, reads that include sequence read subsequences that map to mitochondrial DNA are not allowed to pass the filter 80. In some embodiments, reads that include sequence read subsequences that map to mitochondrial DNA are deleted, moved to a trash file or a temporary file (e.g., 10), or retained in their original data location and / or format. Reads that do not include sequence read subsequences that map to mitochondrial DNA are optionally retained in the output subset of filtered reads. Reads that do not include sequence read subsequences that map to mitochondrial DNA are often identified and / or sent to another module or filter. In some embodiments, reads that do not include sequence read subsequences that map to mitochondrial DNA are identified, assigned a value of "false" or "no," and retained in the output subset of filtered reads.In some embodiments, reads that do not include sequence read subsequences that map to mitochondrial DNA are accepted into and / or passed through a filter (e.g., 80). Reads can be filtered according to sequence read subsequences that map to mitochondrial DNA through a mitochondrial filter (e.g., filter 80). The filter module optionally includes a mitochondrial filter.

[0178] In some embodiments, the filtering step comprises filtering out, rejecting, and / or discarding reads that include sequence read subsequences that map to centromeric DNA. In some embodiments, the filtering step comprises filtering out, rejecting, and / or discarding reads that include one or more, more than two, more than three, more than four, more than five, more than six, more than seven, more than eight, more than nine, more than ten, more than eleven, more than twelve, more than thirteen, more than fourteen, or more than fifteen sequence read subsequences that map to centromeric DNA. In some embodiments, the filter's conditional query comprises determining the presence or absence of reads that include sequence read subsequences that map to centromeric DNA. In some embodiments, reads that include sequence read subsequences that map to centromeric DNA are assigned a "true" or "yes" value and filtered out, rejected, and / or rejected by the module. In some embodiments, reads that include sequence read subsequences that map to centromeric DNA are not allowed to pass the filter. In some embodiments, reads that include sequence read subsequences that map to centromeric DNA are deleted, moved to a trash file or a temporary file (e.g., 10), or retained in their original data location and / or format. Reads that do not include sequence read subsequences that map to centromeric DNA are optionally retained in the output subset of filtered reads. Reads that do not include sequence read subsequences that map to centromeric DNA are often identified and / or sent to another module or filter. In some embodiments, reads that do not include sequence read subsequences that map to centromeric DNA are identified, assigned a "false" or "no" value, and retained in the output subset of filtered reads. In some embodiments, reads that do not include sequence read subsequences that map to centromeric DNA are accepted into and / or passed through a filter.The reads can be filtered according to sequence read subsequences that map to centromeric DNA via a centromeric DNA filter. The filter module optionally includes a centromeric DNA filter.

[0179] In some embodiments, the filtering step comprises excluding, rejecting, and / or discarding reads comprising sequence read subsequences that map to repetitive elements. In some embodiments, the filtering step comprises excluding, rejecting, and / or discarding reads comprising one or more, more than two, more than three, more than four, more than five, more than six, more than seven, more than eight, more than nine, more than ten, more than eleven, more than twelve, more than thirteen, more than fourteen, or more than fifteen sequence read subsequences that map to repetitive elements. In some embodiments, the conditional query of the filter (e.g., 110) comprises determining the presence or absence of reads comprising sequence read subsequences that map to repetitive elements. In some embodiments, reads comprising sequence read subsequences that map to repetitive elements are assigned a "true" or "yes" value and are filtered, rejected, and / or rejected by the module. In some embodiments, reads comprising sequence read subsequences that map to repetitive elements are not allowed to pass the filter 110. In some embodiments, reads that include sequence read subsequences that map to repetitive elements are deleted, moved to a trash file or temporary file (e.g., 10), or retained in their original data location and / or format. Reads that do not include sequence read subsequences that map to repetitive elements are optionally retained in the output subset of filtered reads. Reads that do not include sequence read subsequences that map to repetitive elements are often identified and / or sent to another module or filter. In some embodiments, reads that do not include sequence read subsequences that map to repetitive elements are identified, assigned a "false" or "no" value, and retained in the output subset of filtered reads. In some embodiments, reads that do not include sequence read subsequences that map to repetitive elements are accepted into and / or passed through a filter (e.g., 110).Reads can be filtered according to sequence read subsequences that map to repetitive elements via a repetitive element filter (e.g., filter 110). The filter module optionally includes a repetitive element filter.

[0180] In some embodiments, the filtering step includes excluding, rejecting, and / or discarding reads containing singleton events. In some embodiments, the conditional query of the filter (e.g., 100) includes determining the presence or absence of singleton events. As used herein, a "singleton event" refers to a read or mismatched read pair containing a first candidate breakpoint where a substantially similar candidate breakpoint (e.g., a candidate breakpoint substantially similar to the first candidate breakpoint) is not identified and / or not present in any other reads obtained from the sample. In some embodiments, singleton events are assigned a "true" or "yes" value and are excluded, rejected, and / or discarded. In some embodiments, singleton events are not allowed to pass the filter 100. In some embodiments, singleton events are deleted, moved to a trash file or a temporary file (e.g., 10), or retained in their original data location and / or format. Reads that are not singleton events are optionally retained in the output subset (e.g., a selected subset) of filtered reads. Reads that are not singleton events are often identified and / or sent to another module or filter. Reads that are not singleton events are identified, assigned a "false" or "no" value, and retained in an output subset of filtered reads (e.g., a selected subset). In some embodiments, reads that are not singleton events are accepted into and / or passed through a filter (e.g., 100). Reads can be filtered according to the presence or absence of singleton events via a singleton event filter (e.g., filter 100). A filter module optionally includes a singleton event filter.

[0181] In some embodiments, the filtering step comprises excluding, rejecting, and / or discarding a subset of sample reads that contain breakpoints or candidate breakpoints substantially similar to breakpoints or candidate breakpoints found in the subset of reads derived from the reference. In some embodiments, the subset of sample reads that contain breakpoints or candidate breakpoints substantially similar to breakpoints or candidate breakpoints found in the subset of reads derived from the reference are assigned a "true" or "yes" value and are excluded, rejected, and / or rejected. In some embodiments, the subset of sample reads that contain breakpoints or candidate breakpoints substantially similar to breakpoints or candidate breakpoints found in the subset of reads derived from the reference are not allowed to pass the filter. In some embodiments, the subset of sample reads that contain breakpoints or candidate breakpoints substantially similar to breakpoints or candidate breakpoints found in the subset of reads derived from the reference are deleted, moved to a trash file or temporary file (e.g., 10), or retained in their original data location and / or format. A subset of sample reads containing candidate breakpoints or breakpoints that are not found and / or not present in the subset of reads derived from the reference are optionally retained in the output subset (e.g., selected subset). A subset of sample reads containing candidate breakpoints or breakpoints that are not found and / or not present in the subset of reads derived from the reference are often identified and / or sent to another module or filter. A subset of sample reads containing candidate breakpoints or breakpoints that are not found and / or not present in the subset of reads derived from the reference are identified, assigned a "false" or "no" value, and retained in the output subset (e.g., selected subset). In some embodiments, a subset of sample reads containing candidate breakpoints or breakpoints that are not found and / or not present in the subset of reads derived from the reference are accepted into and / or passed through a filter.The subset of reads for the sample can be filtered via a breakpoint filter according to the presence or absence of a subset of reads containing candidate breakpoints or breakpoints found in the subset of reads derived from the reference. The filter module optionally includes a breakpoint filter.

[0182] A system or method for determining the presence or absence of one or more chromosomal alterations in a sample may include one or more filtering steps and / or filters. A system or method for determining the presence or absence of one or more chromosomal alterations in a sample may include one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, twenty or more, thirty or more, forty or more, or fifty or more filtering steps and / or filters. Filtering steps may be performed before and / or after any of the methods, or portions or steps thereof, as described herein. The systems described herein may include suitable filters before and / or after any suitable process or module described herein. For example, in the exemplary system shown in Figure 6, one or more filters and / or filtering steps may be introduced before 15 or after 15, at 150, 151, 152, 153, and / or after 140. The one or more filtering processes and / or filters may be applied in any suitable order or arrangement.For example, as shown in FIG. 7 , the mismatched read identification module sends mismatched reads to PCR duplicate filter 30; sends reads filtered by filter 30 to sequence quality filter 40; sends reads filtered by filter 40 to mapping discontinuity filter 60; sends reads filtered by filter 60 to mapping filter 70; sends reads filtered by filter 70 to read selection module 120; sends selected reads by module 120 to singleton event filter 100; sends reads filtered by filter 100 to repeat element filter 110; sends reads filtered by filter 110 to comparison module 130. In some embodiments, reads can be filtered one or more times by the same filter. Any suitable filter can be added to the methods or systems described herein. Filters and / or filtering processes are, in some cases, optional and may or may not be used with the methods or systems described herein. For example, filters 30, 40, 60, 70, 80, 90, 100, and / or 110 may be included or excluded from the methods or systems described herein.

[0183] Any suitable procedure can be utilized to process the datasets described herein. Non-limiting examples of procedures suitable for use in processing datasets include filtering procedures, standardization procedures, weighting procedures, mathematical processing of data, statistical processing of data, application of statistical algorithms, plotting data to identify patterns or trends for further processing, and the like, as well as combinations of the foregoing. In certain embodiments, processing a dataset as described herein can reduce the complexity and / or dimensionality of a large and / or complex dataset. Non-limiting examples of complex datasets include sequence read data generated from one or more test subjects and multiple reference subjects of different ages and ethnic backgrounds. In some embodiments, a dataset can include thousands to millions of sequence reads for each test subject and / or each reference subject.

[0184] In certain embodiments, the processing of data can be performed in any number of steps. For example, in some embodiments, the data can be processed using only a single processing procedure, and in certain embodiments, the data can be processed using 1 or more, 5 or more, 10 or more, or 20 or more processing steps (e.g., 1 or more processing steps, 2 or more processing steps, 3 or more processing steps, 4 or more processing steps, 5 or more processing steps, 6 or more processing steps, 7 or more processing steps, 8 or more processing steps, 9 or more processing steps, 10 or more processing steps, 11 or more processing steps, 12 or more processing steps, 13 or more processing steps, 14 or more processing steps, 15 or more processing steps, 16 or more processing steps, 17 or more processing steps, 18 or more processing steps, 19 or more processing steps, or 20 or more processing steps). In some embodiments, the processing step can be the same step repeated two or more times (e.g., two or more filtering steps, two or more normalizing steps), while in certain embodiments, the processing step can be two or more different processing steps performed simultaneously or sequentially (e.g., filtering, normalizing; normalizing, monitoring peak heights and edges; filtering, normalizing, normalizing against a reference, statistical operations to determine p-values, etc.). In some embodiments, any suitable number of the same or different processing steps and / or combinations of the same or different processing steps can be utilized to process the sequence read data to facilitate providing an outcome.In certain embodiments, the complexity and / or dimensionality of a dataset can be reduced by processing the dataset through the criteria described herein.

[0185] In some embodiments, the processing step may include the use of one or more statistical algorithms. Any suitable statistical algorithm, alone or in combination, may be used to analyze and / or manipulate the datasets described herein. Any suitable number of statistical algorithms may be used. In some embodiments, a dataset may be analyzed using one or more, five or more, ten or more, or twenty or more statistical algorithms. Non-limiting examples of statistical algorithms suitable for use with the methods described herein include decision trees, counter-nulls, multiple comparisons, omnibus tests, the Behrens-Fisher problem, bootstrap, Fisher's method for combining independent tests for significance, null hypothesis, type I error, type II error, exact test, one-sample Z-test, two-sample Z-test, one-sample t-test, paired t-test, pooled two-sample t-test with equal variances, unpooled two-sample t-test with unequal variances, one-part z-test, pooled two-part z-test, unpooled two-part z-test, one-sample chi-square test, two-sample F-test for equal variances, confidence interval, credible interval, significance, meta-analysis, simple linear regression, robust linear regression, and the like, or combinations of the foregoing.

[0186] In certain embodiments, the dataset is subjected to multiple (e.g., two or more) statistical algorithms (e.g., least squares regression, principal component analysis, linear discriminant analysis, quadratic discriminant analysis, bagging, neural networks, supporter vector machine models, random forests, classification tree models, K-nearest neighbors, logistic regression, and / or Loess smoothing) and / or mathematical operations and / or statistical techniques. A dataset can be analyzed by utilizing multiple operations (e.g., referred to herein as operations). In some embodiments, the use of multiple operations can generate an N-dimensional space that can be used to provide an outcome. In certain embodiments, analysis of a dataset through the use of multiple operations can reduce the complexity and / or dimensionality of the dataset.

[0187] In some embodiments, after filtering, normalizing, clustering, counting, and / or weighting a dataset of sequence reads, the processed datasets can be analyzed and / or compared mathematically and / or statistically (e.g., by using a statistical function or algorithm). In certain embodiments, the processed datasets can be further analyzed and / or compared by calculating Z-scores for one or more selected chromosomes or portions thereof. In some embodiments, the processed datasets can be further analyzed and / or compared by calculating P-values. One embodiment of a formula for calculating Z-scores is provided in Formula A (Example 1).

[0188] Determination of fetal nucleic acid content In some embodiments, the amount (e.g., concentration, relative amount, absolute amount, copy number, etc.) of fetal nucleic acid in nucleic acid is determined. In certain embodiments, the amount of fetal nucleic acid in a sample is referred to as the "fetal fraction." In some embodiments, "fetal fraction" refers to the fraction of fetal nucleic acid in circulating cell-free nucleic acid in a sample (e.g., blood sample, serum sample, plasma sample) obtained from a pregnant woman. In some embodiments, the method of determining a chromosomal alteration may also include a step of determining the fetal fraction. In some embodiments, the presence or absence of a chromosomal alteration is determined according to the fetal fraction (e.g., determining the fetal fraction for the sample). Determining the fetal fraction can be performed by any suitable method, non-limiting examples of which include those described below.

[0189] In some embodiments, the fetal fraction can be determined using the methods described herein for determining fragment length. Fragments of cell-free fetal nucleic acid are generally shorter than fragments of maternal nucleic acid (see, e.g., Chan et al. (2004) Clin. Chem. 50:88-92; Lo et al. (2010) Sci. Transl. Med. 2:61ra91). (See, e.g., 100,0 ...

[0190] In certain embodiments, the amount of fetal nucleic acid is determined according to markers specific for male fetuses (e.g., Y chromosome STR markers (e.g., DYS19, DYS385, DYS392 markers); RhD markers in RhD-negative women), according to the allelic ratio of polymorphic sequences, or according to one or more markers specific for fetal nucleic acid but not maternal nucleic acid (e.g., epigenetic biomarkers (e.g., methylation; described in more detail below) that are differential between the mother and the fetus, or fetal RNA markers in maternal plasma (e.g., Lo, 2005, Journal of Histochemistry and Cytochemistry, vol. 53 ( 3): pp. 293-296).

[0191] Determination of fetal nucleic acid content (e.g., fetal fraction) is optionally performed using a fetal quantification assay (FQA), for example, as described in U.S. Patent Application Publication No. 2010 / 0105049, which is incorporated herein by reference. This type of assay allows fetal nucleic acid in a maternal sample to be detected and quantified based on the methylation status of the nucleic acid in the sample. In certain embodiments, the amount of fetal nucleic acid from the maternal sample can be determined relative to the total amount of nucleic acid present, resulting in the percentage of fetal nucleic acid in the sample. In certain embodiments, the copy number of fetal nucleic acid can be determined in the maternal sample. In certain embodiments, the amount of fetal nucleic acid can be determined in a sequence-specific (or portion-specific) manner, optionally with sufficient sensitivity to enable accurate chromosome dosage analysis (e.g., to detect the presence or absence of fetal chromosomal alterations).

[0192] A fetal quantification assay (FQA) can be performed with any of the methods described herein. Such an assay can be performed by any method known in the art and / or described in U.S. Patent Application Publication No. 2010 / 0105049, such as a method that can distinguish between maternal and fetal DNA based on differential methylation status and quantify (i.e., determine the amount of) fetal DNA. Methods for differentiating nucleic acids based on methylation status include methylation-sensitive capture, such as capture using MBD2-Fc fragment (MBD-FC), in which the methyl-binding domain of MBD2 is fused to the Fc fragment of an antibody (Gebhard et al., 2006). (2013), Cancer Res., 66(12):6118-28; methylation-specific antibodies; bisulfite conversion, e.g., MSP (methylation-sensitive PCR), COBRA, methylation-sensitive single nucleotide primer extension (Ms-SNuPE), or Sequenom MassCLEAVE™ technology; and the use of methylation-sensitive restriction enzymes (e.g., digesting maternal DNA in a maternal sample with one or more methylation-sensitive restriction enzymes, thereby enriching for fetal DNA). Methyl-sensitive enzymes can also be used to differentiate nucleic acids based on methylation status; these enzymes can preferentially or substantially cleave or digest at their DNA recognition sequences, e.g., if the latter is unmethylated. Thus, unmethylated DNA samples will be cleaved into smaller fragments than methylated DNA samples, and hypermethylated DNA samples will not be cleaved. Except where expressly stated, any method for differentiating nucleic acids based on methylation status can be used with the compositions and methods of the present technology. The amount of fetal DNA can be determined, for example, by introducing one or more competitors at known concentrations into the amplification reaction.The amount of fetal DNA can also be determined, for example, by RT-PCR, primer extension, sequencing, and / or counting.In certain cases, the amount of nucleic acid can be determined using BEAMing technology, as described in US Patent Application Publication No. 2007 / 0065823.In certain embodiments, the effectiveness of restriction can be determined, and the amount of fetal DNA can be further determined using restriction efficiency.

[0193] In certain embodiments, fetal quantification assays (FQAs) can be used to determine the concentration of fetal DNA in a maternal sample, for example, by the following method: a) determining the total amount of DNA present in the maternal sample; b) selectively digesting maternal DNA in the maternal sample using one or more methylation-sensitive restriction enzymes, thereby enriching for fetal DNA; c) determining the amount of fetal DNA obtained from step b); d) comparing the amount of fetal DNA obtained from step c) with the total amount of DNA obtained from step a), thereby determining the concentration of fetal DNA in the maternal sample. In certain embodiments, the absolute copy number of fetal nucleic acids in a maternal sample can be determined, for example, using a system that uses mass spectrometry and / or competitive PCR to measure absolute copy number. See, for example, Ding and Cantor (2003), PNAS USA, 100:3059-3064, and U.S. Patent Application Publication No. 2004 / 008124, both of which are incorporated herein by reference. Please refer to issue 993.

[0194] In certain embodiments, the fetal fraction can be determined based on the allele ratio of a polymorphic sequence (e.g., a single nucleotide polymorphism (SNP)), for example, using a method such as that described in U.S. Patent Application Publication No. 2011 / 0224087, which is incorporated herein by reference. In such a method, nucleotide sequence reads are obtained for a maternal sample, and the fetal fraction is determined by comparing the total number of nucleotide sequence reads that map to a first allele and the total number of nucleotide sequence reads that map to a second allele at an information-carrying polymorphic site (e.g., SNP) in a reference genome. In certain embodiments, fetal alleles are identified, for example, by their relatively small contribution to the mixture of fetal and maternal nucleic acids in the sample, compared to the large contribution of maternal nucleic acids to the mixture. Thus, the relative abundance of fetal nucleic acids in a maternal sample can be determined as a parameter of the total number of unique sequence reads that map to a target nucleic acid sequence on a reference genome for each of the two alleles at a polymorphic site.

[0195] The amount of fetal nucleic acid in extracellular nucleic acid can be quantified and used in conjunction with the methods provided herein. Thus, in certain embodiments, the method according to the technology described herein includes the additional step of determining the amount of fetal nucleic acid. The amount of fetal nucleic acid in a nucleic acid sample derived from a subject can be determined before or after the treatment for preparing the sample nucleic acid. In certain embodiments, after the sample nucleic acid is processed and prepared, the amount of fetal nucleic acid in the sample is determined, and this amount is utilized for further evaluation. In some embodiments, the outcome includes factoring the fraction of fetal nucleic acid in the sample nucleic acid (e.g., adjusting the data, excluding the sample, making a decision, or not making a decision).

[0196] The determining step (e.g., determining the presence or absence of a chromosomal alteration) can be performed before, during, or at any time within the methods described herein, or can be performed after certain methods described herein. For example, to achieve a given sensitivity or specificity in a determination (e.g., determining a chromosomal alteration in a fetus), a fetal nucleic acid quantification method can be implemented before, during, or after the determination of the chromosomal alteration to identify samples with about 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25% or more of fetal nucleic acid. In some embodiments, samples determined to have a certain threshold amount of fetal nucleic acid (e.g., about 15% or more fetal nucleic acid; about 4% or more fetal nucleic acid) are further analyzed, for example, for fetal sex or the presence or absence of chromosomal alterations. In certain embodiments, for example, the determination of the presence or absence of chromosomal alterations is selected (e.g., selected and communicated to the patient) for only samples having a certain threshold amount of fetal nucleic acid (e.g., about 15% or more fetal nucleic acid; about 4% or more fetal nucleic acid).

[0197] In some embodiments, determining the presence or absence of chromosomal alteration does not require or require determining the fetal fraction or the amount of fetal nucleic acid.In some embodiments, identifying the presence or absence of chromosomal alteration does not require differentiation of the sequence of fetal DNA compared to maternal DNA.In certain embodiments, this is because the total contribution of both maternal and fetal sequences in a particular chromosome, chromosomal portion, or segment thereof is analyzed.In some embodiments, identifying the presence or absence of chromosomal alteration does not rely on prior sequence information to distinguish fetal DNA from maternal DNA.

[0198] Fetal sex In certain cases, it may be beneficial to determine the sex of a fetus in utero. For example, a patient (e.g., a pregnant woman) with a family history of one or more sex-linked disorders may wish to determine the sex of the fetus she is carrying to help assess the fetus's risk of inheriting such a disorder.

[0199] In some embodiments, fetal sex or sex-related disorder prediction can be determined by the method, system, machine, device, or non-transient computer-readable storage medium described herein.Gender determination is generally based on sex chromosome.Humans have two sex chromosomes, X chromosome and Y chromosome.Y chromosome contains the SRY gene, which induces embryonic development as male.In addition, the Y chromosome of humans and other mammals also contains other genes that are required for normal sperm production.

[0200] In some embodiments, the method for determining the sex of a fetus may also include determining the presence or absence of a fetal fraction and / or a fetal chromosomal alteration. The determination of the presence or absence of fetal sex can be performed in any suitable manner, including, but not limited to, karyotyping, amniocentesis, circulating cell-free nucleic acid analysis, cell-free fetal DNA analysis, nucleotide sequence analysis, quantification of sequence reads, targeting methods, amplification-based techniques, mass spectrometry-based techniques, differential methylation-based techniques, differential digestion-based techniques, polymorphism-based techniques, hybridization-based techniques (e.g., using probes), etc.

[0201] Medical Disorders and Conditions The methods described herein may be applicable to any suitable medical disorder or condition, non-limiting examples of which include cell proliferative disorders and conditions, wasting disorders and conditions, degenerative disorders and conditions, autoimmune disorders and conditions, pre-eclampsia, chemical or environmental toxicity, liver injury or disease, kidney injury or disease, vascular disease, hypertension, and myocardial infarction.

[0202] In some embodiments, the cell proliferative disorder or condition is cancer of the liver, lung, spleen, pancreas, colon, skin, bladder, eye, brain, esophagus, head, neck, ovary, testis, prostate, etc., or a combination thereof. Non-limiting examples of cancer include hematopoietic neoplastic disorders, which are diseases involving hyperplastic / neoplastic cells of hematopoietic origin (e.g., arising from the myeloid, lymphoid, or erythroid lineages, or their precursor cells), and can arise from poorly differentiated acute leukemias (e.g., erythroblastic leukemia and acute megakaryoblastic leukemia). Certain myeloid disorders include, but are not limited to, acute promyelocytic leukemia (APML), acute myeloid leukemia (AML), and chronic myeloid leukemia (CML). Certain lymphoid malignancies include, but are not limited to, acute lymphoblastic leukemia (ALL), including B-cell lineage ALL and T-cell lineage ALL, chronic lymphocytic leukemia (CLL), prolymphocytic leukemia (PLL), hairy cell leukemia (HLL), and Waldenstrom's macroglobulinemia (WM). Certain forms of malignant lymphoma include, but are not limited to, non-Hodgkin's lymphoma and its variants, peripheral T-cell lymphoma, adult T-cell leukemia / lymphoma (ATL), cutaneous T-cell lymphoma (CTCL), large granular lymphocytic leukemia (LGF), Hodgkin's disease, and Reed-Sternberg disease. Cell proliferative disorders are sometimes non-endocrine or endocrine tumors. Illustrative examples of non-endocrine tumors include, but are not limited to, adenocarcinoma, acinar cell carcinoma, adenosquamous carcinoma, giant cell tumor, intraductal papillary mucinous neoplasm, mucinous cystadenocarcinoma, pancreatoblastoma, serous cystadenoma, solid pseudopapillary tumor. The endocrine tumor is optionally a pancreatic islet cell tumor.

[0203] In some embodiments, the wasting or degenerative disorder or condition is cirrhosis of the liver, amyotrophic lateral sclerosis (ALS), Alzheimer's disease, Parkinson's disease, multiple system atrophy, atherosclerosis, progressive supranuclear palsy, Tay-Sachs disease, diabetes, heart disease, keratoconus, inflammatory bowel disease (IBD), prostatitis, osteoarthritis, osteoporosis, rheumatoid arthritis, Huntington's disease, chronic traumatic encephalopathy, chronic obstructive pulmonary disease (COPD), tuberculosis, chronic diarrhea, acquired immune deficiency syndrome (AIDS), superior mesenteric artery syndrome, or the like, or a combination thereof.

[0204] In some embodiments, the autoimmune disorder or condition is selected from the group consisting of acute disseminated encephalomyelitis (ADEM), Addison's disease, alopecia areata, ankylosing spondylitis, antiphospholipid syndrome (APS), autoimmune hemolytic anemia, autoimmune hepatitis, autoimmune inner ear disease, bullous pemphigoid, celiac disease, Chagas' disease, chronic obstructive pulmonary disease, Crohn's disease (a type of idiopathic inflammatory bowel disease "IBD"), dermatomyositis, type 1 diabetes, endometriosis, Goodpasture's syndrome, Graves' disease, Guillain-Barré syndrome (GBS), Hashimoto's disease, hidradenitis suppurativa, idiopathic inflammatory bowel disease ... thrombocytopenic purpura, interstitial cystitis, systemic lupus erythematosus, mixed connective tissue disease, localized scleroderma, multiple sclerosis (MS), myasthenia gravis, lethargy, euromyotonia, pemphigus vulgaris, pernicious anemia, polymyositis, primary biliary cirrhosis, rheumatoid arthritis, schizophrenia, scleroderma, Sjogren's syndrome, temporal arteritis (also known as "giant cell arteritis"), ulcerative colitis (a type of idiopathic inflammatory bowel disease "IBD"), vasculitis, vitiligo, Wegener's granulomatosis, and the like, or a combination thereof.

[0205] Systems, Machines, Storage Media, and Interfaces Certain processes and methods described herein often cannot be implemented without a computer, microprocessor, software, module, or other machine. The methods described herein are typically computer-implemented methods, with one or more portions of the methods being implemented by one or more processors (e.g., microprocessors), computers, or microprocessor-controlled machines, as the case may be. Method-related embodiments described herein are generally applicable to the same or similar processes implemented by instructions in the systems, machines, and computer program products described herein. Method-related embodiments described herein may generally be applicable to the same or similar processes implemented by a non-transitory computer-readable storage medium having an executable program stored thereon, where the program instructs a microprocessor to perform the method or portions thereof. The descriptive term "non-transitory" as used herein is expressly restrictive and excludes transient propagating signals (e.g., transmission signals, electronic transmissions, waves (e.g., carrier waves)). As used herein, the terms "non-transitory computer-readable medium" and / or "non-transitory computer-readable media" include all computer-readable media except for transient, propagating signals. In some embodiments, the processes and methods described herein are performed in an automated manner. In some embodiments, one or more steps and methods described herein are performed by a microprocessor and / or computer and / or in conjunction with a memory.In some embodiments, the automated method is embodied by software, modules, microprocessors, peripherals, and / or machines, etc., that (i) identify discordant reads, (ii) generate mappability changes, (iii) select a subset of reads according to mappability changes, (iv) determine candidate breakpoints, (v) filter reads, (vi) compare reads to substantially similar candidate breakpoints, (vii) determine the presence or absence of chromosomal alterations, or (viii) perform any combination thereof.

[0206] Sequence reads, mismatched reads, changes in mappability of reads, a select subset of reads selected according to changes in mappability, a subset of filtered reads, a subset of reads containing similar candidate breakpoints, reads from the reference, and / or reads from the test subject can be further analyzed and processed to determine the presence or absence of chromosomal alterations. The reads, selected reads, subsets of reads, and quantified reads are sometimes referred to as "data" or "datasets." In some embodiments, the data or dataset can be characterized by one or more features or variables (e.g., sequence-based features or variables [e.g., GC content, specific nucleotide sequences, etc.], function-specific features or variables [e.g., expressed genes, cancer genes, etc.], location-based features or variables [genome-specific, chromosome-specific], etc., and combinations thereof). In certain embodiments, the data or dataset can be organized into a matrix of two or more degrees based on one or more features or variables. The data organized into a matrix can be organized using any suitable features or variables. Non-limiting examples of data in a matrix include data consisting of candidate breakpoints for the reference, candidate breakpoints for the test sample, Z-scores for the reference, Z-scores for the sample, and breakpoint locations.

[0207] The machines, software, and interfaces can be used to perform the methods described herein. Using the machines, software, and interfaces, a user can input, request, query, or determine a selection for using a particular information, program, or process (e.g., a process for mapping sequence reads, generating sequence read subsequences, mapping sequence read subsequences, generating relationships, generating mappability changes, selecting a subset of reads, comparing reads, and / or providing an outcome), which may involve, for example, implementing statistical analysis algorithms, statistical significance algorithms, statistical algorithms, iterative steps, validation algorithms, and graphical displays. In some embodiments, a user can enter datasets as input information, download one or more datasets via appropriate hardware media (e.g., a flash drive), and / or send datasets from one system to another for subsequent processing and / or providing outcomes (e.g., sending sequence read data from a sequencer to a computer system for sequence read mapping; sending mapped sequence data to a computer system for processing and generating outcomes and / or reports).

[0208] A system typically includes one or more machines. Each machine includes one or more of memory, one or more microprocessors, and instructions. When a system includes two or more machines, some or all of the machines can be co-located, some or all of the machines can be in different locations, all of the machines can be in one location, and / or all of the machines can be in different locations. When a system includes two or more machines, some or all of the machines can be co-located with the user, some or all of the machines can be in different locations from the user, all of the machines can be co-located with the user, and / or all of the machines can be in one or more locations different from the user.

[0209] The system optionally includes a computing device or a sequencing device, or a computing device and a sequencing device (i.e., a sequencing machine and / or a computing machine). The device referred to herein is optionally a machine. A sequencing device is generally configured to accept physical nucleic acids and generate signals corresponding to the nucleotide bases of the nucleic acids. A sequencing device is often "loaded" with a sample containing nucleic acids, and the nucleic acids of the sample loaded into the sequencing device are generally subjected to a nucleic acid sequencing process. As used herein, the term "loading a sequencing device" refers to contacting a portion of the sequencing device (e.g., a flow cell) with a nucleic acid sample, where the portion of the sequencing device is configured to accept the sample for performing the nucleic acid sequencing process. In some embodiments, a variant of the sample nucleic acid is loaded onto the sequencing device. Variants are sometimes created by a process that modifies the sample nucleic acid into a form suitable for sequencing the nucleic acid (e.g., by ligation (e.g., adding adapters to the ends of the sample nucleic acid by ligation), amplification, restriction digestion, etc., or a combination thereof). Sequencing equipment is often configured, in part, to perform an appropriate DNA sequencing method that generates a signal (e.g., an electronic signal, a detector signal, an image, etc., or a combination thereof) corresponding to the nucleotide bases of the loaded nucleic acid.

[0210] One or more signals corresponding to each base of a DNA sequence are often processed and / or converted into a base call (e.g., a specific nucleotide base, e.g., guanine, cytosine, thymine, uracil, adenine, etc.) by an appropriate process. The collection of base calls derived from the loaded nucleic acid is often processed and / or assembled into one or more sequence reads. In embodiments in which multiple sample nucleic acids are sequenced at once (i.e., multiplexed), an appropriate demultiplexing process can be utilized to associate specific reads with the sample nucleic acid from which they originated. The sequence reads can be aligned to a reference genome by an appropriate process, and reads aligned to portions of the reference genome can be counted as described herein.

[0211] The sequencing device may be associated with and / or include one or more computing devices in the system. The one or more computing devices may be configured to perform one or more of the following processes: generating base calls from sequencing device signals, assembling reads (e.g., generating reads), demultiplexing reads, aligning reads to a reference genome, counting reads aligned to genome portions within the reference genome, etc. The one or more computing devices may be configured to perform one or more of the following additional processes: normalizing read counts (e.g., reducing or eliminating bias), generating one or more determinations (e.g., determining fetal fraction, fetal ploidy, fetal sex, fetal chromosome count, outcome, the presence or absence of a genetic mutation (e.g., the presence or absence of fetal chromosomal aneuploidy (e.g., trisomy 13, trisomy 18, and / or trisomy 21)), etc.).

[0212] In some embodiments, a single computing device is associated with a sequencing device, and in certain embodiments, a single computing device performs many or all of the following processes: generating base calls from sequencing device signals, assembling reads, demultiplexing reads, aligning reads and counting aligned reads against genomic portions of a reference genome, and normalizing read counts to generate one or more outcomes (e.g., fetal fraction, the presence or absence of a specific genetic mutation). In the latter embodiment, where a single computing device is associated with a sequencing device, the computing device often includes one or more processors (e.g., microprocessors) and memory having instructions executed by the one or more processors to perform the processes. In some embodiments, a single computing device can be a single-core or multi-core computing device that is local to the sequencing device (e.g., located in the same location (e.g., the same street address, the same building, the same floor, the same room, etc.)). In some embodiments, a single computing device is integrated with the sequencing device.

[0213] In some embodiments, multiple computing devices in the system are associated with a sequencing device, and a subset of all processes performed by the system can be assigned to or divided among specific computing devices in the system. A subset of all processes can be divided among two or more computing devices or groups thereof in any suitable combination. In certain embodiments, the processes of generating base calls from sequencing device signals, assembling reads, and demultiplexing reads are performed by a first computing device or groups thereof, the processes of aligning and counting reads mapped to portions of a reference genome are performed by a second computing device or groups thereof, and the processes of normalizing read counts and providing one or more outcomes are performed by a third computing device or groups thereof. In a system including two or more computing devices or groups thereof, each specific computing device can include memory, one or more processors, or a combination thereof. The multiple computing device system optionally includes one or more suitable servers local to the sequencing device, and optionally includes one or more suitable servers not local to the sequencing device (e.g., web servers, online servers, application servers, remote file servers, cloud servers (e.g., cloud environments, cloud computing)).

[0214] Devices in different system configurations may generate different types of output data. For example, a sequencing device may output base signals, and the base signal output data may be transferred to a computing device that converts the base signal data into base calls. In some embodiments, the base calls are output data from one computing device and transferred to another computing device to generate sequence reads. In certain embodiments, the base calls are not output data from a particular device, but are utilized in the same device that receives the sequencing device's base signals to generate sequence reads. In some embodiments, one device receives the sequencing device's base signals, generates base calls, sequence reads, demultiplexes the sequence reads, and outputs demultiplexed sequence reads for the sample, which may be transferred to another device or group of devices that align the sequence reads to a reference genome. In some embodiments, one device or a group thereof can output aligned sequence reads (e.g., a SAM file or a BAM file) that are mapped to portions of a reference genome, and such output data can be transferred to a second computing device or a group thereof, which normalizes the sequence reads (e.g., normalizes the counts of the sequence reads) and generates an outcome (e.g., the presence or absence of fetal fraction and / or fetal trisomy). The output data from one device can be transferred to a second device in any suitable manner. For example, the output data from one device can be stored in a physical storage device, and the storage device can be transported and connected to a second device to which the output data is transferred. The output data can be stored by one device in a database, and the second device can access the output data from the same database.

[0215] In some embodiments, a user interacts with the device (e.g., a computing device, a sequencing device). The user can, for example, query software and then collect datasets via access to the Internet; in certain embodiments, the programmable microprocessor can instruct the user to collect appropriate datasets based on given parameters. The programmable microprocessor can also instruct the user to select one or more dataset options selected by the microprocessor based on given parameters. The programmable microprocessor can instruct the user to select one or more dataset options selected by the microprocessor based on information found via the Internet, other internal information, or external information, etc. The options can be selected to select one or more graphical representations of one or more data features, one or more statistical algorithms, one or more statistical analysis algorithms, one or more statistical significance algorithms, iterative steps, one or more validation algorithms, and a method, machine, apparatus (also referred to herein as plural apparatus, plural apparatus), computer program, or non-transitory computer-readable storage medium having an executable program stored thereon.

[0216] Systems of interest herein may include common components of a computer system, such as, for example, a network server, a laptop system, a desktop system, a portable system, a digital patient aid, a computing kiosk, etc. A computer system may include one or more input means, such as a keyboard, a touch screen, a mouse, voice recognition, or other means that allows a user to input data into the system. A system may further include one or more outputs, including, but not limited to, a display screen (e.g., CRT or LCD), a speaker, a fax machine, a printer (e.g., laser printer, inkjet printer, impact printer, monochrome printer, or color printer), or other output useful for providing visual, audible, and / or hardcopy output of information (e.g., outcomes and / or reports).

[0217] Within the system, the input and output means can be connected to a central processing unit, which can include, among other components, a microprocessor for executing program instructions and memory for storing program code and program data. In some embodiments, the process can be implemented as a single-user system located in a single geographic location. In certain embodiments, the process can be implemented as a multi-user system. In multi-user implementations, multiple central processing units can be connected via a network. The network can be a local network encompassing a single department within a portion of a building, an entire building, or can span multiple buildings, a region, an entire country, or the entire world. The network can be a private network, owned and managed by a provider, or implemented as an Internet-based service where users access web pages to input and search information. Thus, in certain embodiments, the system includes one or more machines, which can be local or remote to the user. A user can access more than one machine, at one location or multiple locations, and data can be mapped and / or processed serially and / or in parallel. Thus, suitable configuration and control can be utilized to map and / or process data using multiple machines, such as within a local network, a remote network, and / or within a "cloud" computing platform.

[0218] In some embodiments, the system may include a communications interface. The communications interface allows software and data to be transferred between the computer system and one or more external devices. Non-limiting examples of communications interfaces include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot, and a PCMCIA card. The software and data transferred via the communications interface are generally in the form of signals, which may be electronic, electromagnetic, optical, and / or other signals capable of being received...

Claims

[Claim 1] The invention as depicted in the drawings.

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