Systems and methods to detect rare mutations and copy number variation

Abstract

To provide systems and methods to detect rare mutations and copy number variation.SOLUTION: The present disclosure provides a system and method for the detection of rare mutations and copy number variations in cell free polynucleotides. Generally, the systems and methods comprise sample preparation, or the extraction and isolation of cell free polynucleotide sequences from a bodily fluid; subsequent sequencing of cell free polynucleotides by techniques known in the art; and application of bioinformatics tools to detect rare mutations and copy number variations in comparison to a reference. The systems and methods also may contain a database or collection of different rare mutations or copy number variation profiles of different diseases, to be used as additional references in aiding detection of rare mutations, copy number variation profiling or general genetic profiling of a disease.SELECTED DRAWING: None

Description

[Background technology]

[0001] cross reference This application claims priority to U.S. Provisional Patent Application No. 61 / 696,734, filed September 4, 2012, U.S. Provisional Patent Application No. 61 / 704,400, filed September 21, 2012, U.S. Provisional Patent Application No. 61 / 793,997, filed March 15, 2013, and U.S. Provisional Patent Application No. 61 / 845,987, filed July 13, 2013, each of which is incorporated by reference in its entirety herein for all purposes.

[0002] 2. Background of the Invention The detection and quantification of polynucleotides is important for medical applications such as molecular biology and diagnostics. Genetic testing is particularly useful for some diagnostic methods. For example, disorders caused by rare genetic changes (e.g., sequence variants) or epigenetic marker changes (e.g., cancer and partial or complete aneuploidy) can be detected or more precisely characterized using DNA sequence information.

[0003] Early detection and monitoring of genetic diseases such as cancer is often useful and necessary for successful treatment or management of the disease. One approach may include monitoring samples derived from cell-free nucleic acid, which is a collection of polynucleotides that can be found in various types of bodily fluids. In some cases, disease can be characterized or detected based on the detection of genetic abnormalities (e.g., copy number variation and / or sequence mutation changes of one or more nucleic acid sequences) or the occurrence of certain other rare genetic changes. Cell-free DNA ("cfDNA") has been known in the art for several decades and may contain genetic abnormalities associated with certain diseases. Despite improvements in sequencing methods and techniques for manipulating nucleic acids, there is a need in the art for improved methods and systems for using cell-free DNA to detect and monitor disease. Summary of the Invention [Means for solving the problem]

[0004] Summary of the Invention The present disclosure provides a method for detecting copy number variation, comprising: a) sequencing extracellular polynucleotides from a body sample of a subject, each of which is optionally attached to a unique barcode; b) filtering out reads that do not meet a specified threshold; c) mapping the sequence reads obtained from step (a) to a reference sequence; d) quantifying / counting the mapped reads in two or more predefined regions of the reference sequence; e) determining copy number variation in one or more of the predefined regions by: (i) normalizing the number of reads in the predefined regions relative to each other and / or the number of unique barcodes in the predefined regions relative to each other; and (ii) comparing the normalized numbers obtained in step (i) to normalized numbers obtained from a control sample.

[0005] The present disclosure also provides a method for detecting rare mutations in a cell-free or substantially cell-free sample obtained from a subject, the method comprising the steps of: a) sequencing extracellular polynucleotides from the subject's bodily sample, each of the extracellular polynucleotides generating a plurality of sequencing reads; b) sequencing extracellular polynucleotides from the subject's bodily sample, each of the extracellular polynucleotides generating a plurality of sequencing reads; sequencing extracellular polynucleotides from the subject's bodily sample, each of the extracellular polynucleotides generating a plurality of sequencing reads; c) filtering out reads that do not meet a specified threshold. d) mapping the sequence reads derived from the sequencing step onto a reference sequence; e) identifying a subset of mapped sequence reads that align with variants of the reference sequence at each mappable base position; f) calculating for each mappable base position the ratio of (a) the number of mapped sequence reads that contain a variant compared to the reference sequence to (b) the total number of sequence reads for each mappable base position; g) normalizing said ratios or variances frequency for each mappable base position to determine potential rare variants or mutations; h) comparing the numbers obtained for each of the regions containing potential rare variants or mutations with numbers similarly obtained from a reference sample. Includes.

[0006] Additionally, the present disclosure provides a method of characterizing heterogeneity of an abnormal condition in a subject, the method comprising generating a genetic profile of extracellular polynucleotides in the subject, the genetic profile comprising a plurality of data resulting from analysis of copy number variations and / or other rare variants (e.g., genetic alterations).

[0007] In some embodiments, the prevalence / concentration of each rare variant identified in a subject is reported and quantified simultaneously, hi other embodiments, a confidence score for the prevalence / concentration of rare variants in a subject is reported.

[0008] In some embodiments, the extracellular polynucleotide comprises DNA. In other embodiments, the extracellular polynucleotide comprises RNA. The polynucleotide may be a fragment or may be fragmented after isolation. Additionally, the present disclosure provides a method for the isolation and extraction of circulating nucleic acids.

[0009] In some embodiments, the extracellular polynucleotides are isolated from a bodily sample which may be selected from the group consisting of blood, plasma, serum, urine, saliva, mucosal discharge, sputum, stool and tears.

[0010] In some embodiments, the methods of the present disclosure also include determining the percentage of sequences in the body sample that have copy number variations or other rare genetic alterations (eg, sequence variants).

[0011] In some embodiments, the percentage of sequences in the body sample that have copy number variation is determined by calculating the percentage of predefined regions that have amounts of polynucleotides above or below a predefined threshold.

[0012] In some embodiments, the bodily fluid is collected from a subject suspected of having an abnormal condition which may be selected from the group consisting of a mutation, a rare mutation, a single nucleotide variant, an indel, a copy number variation, a transversion, a translocation, an inversion, a deletion, an aneuploidy, a partial aneuploidy, polyploidy, chromosomal instability, a chromosomal structural change, a gene fusion, a chromosomal fusion, a gene truncation, a gene amplification, a gene duplication, a chromosomal lesion, a DNA lesion, an abnormal change in a nucleic acid chemical modification, an abnormal change in an epigenetic pattern, an abnormal change in a nucleic acid methylation, an infection, and a cancer.

[0013] In some embodiments, the subject may be a pregnant woman, and the abnormal condition in the pregnant woman may be a fetal abnormality selected from the group consisting of single nucleotide variants, indels, copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, gene fusions, chromosomal fusions, gene truncations, gene amplifications, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, abnormal changes in nucleic acid methylation, infections, and cancer.

[0014] In some embodiments, the method may include attaching one or more barcodes to the extracellular polynucleotide or fragment thereof prior to sequencing, where the barcodes are unique. In other embodiments, the barcodes attached to the extracellular polynucleotide or fragment thereof prior to sequencing are not unique.

[0015] In some embodiments, the disclosed methods may include selectively enriching regions from a subject's genome or transcriptome prior to sequencing. In other embodiments, the disclosed methods include selectively enriching regions from a subject's genome or transcriptome prior to sequencing. In other embodiments, the disclosed methods include non-selectively enriching regions from a subject's genome or transcriptome prior to sequencing.

[0016] The methods of the disclosure also include a step of attaching one or more barcodes to the extracellular polynucleotides or fragments thereof prior to any amplifying or enriching step.

[0017] In some embodiments, the barcode is a polynucleotide that may further comprise a random sequence or a fixed or semi-random set of oligonucleotides that, in combination with the diversity of molecules sequenced from a selected region, allows for the identification of unique molecules and may be at least 3, 5, 10, 15, 20, 25, 30, 35, 40, 45 or 50-mer base pairs in length.

[0018] In some embodiments, the extracellular polynucleotide or fragment thereof may be amplified, hi some embodiments, the amplification comprises global amplification or whole genome amplification.

[0019] In some embodiments, a sequence read of unique identity can be detected based on sequence information at the beginning (start) and end (end) regions of the sequence read, and the length of the sequence read. In other embodiments, a sequence molecule of unique identity is detected based on sequence information at the beginning (start) and end (end) regions of the sequence read, the length of the sequence read, and the attachment of a barcode.

[0020] In some embodiments, amplification comprises selective amplification, non-selective amplification, suppression amplification, or subtractive enrichment.

[0021] In some embodiments, the methods of the present disclosure include removing a subset of reads from further analysis prior to quantifying or enumerating the reads.

[0022] In some embodiments, the method may include filtering out reads with accuracy or quality scores below a threshold, e.g., less than 90%, 99%, 99.9% or 99.99%, and / or mapping scores below a threshold, e.g., less than 90%, 99%, 99.9% or 99.99%. In other embodiments, the disclosed method includes filtering out reads with quality scores below a specified threshold.

[0023] In some embodiments, the predefined regions are uniform or substantially uniform in size and are about 10 kb, 20 kb, 30 kb, 40 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90 kb, or 100 kb in size, in some embodiments, at least 50, 100, 200, 500, 1000, 2000, 5000, 10,000, 20,000, or 50,000 regions are analyzed.

[0024] In some embodiments, the genetic variant, rare mutation, or copy number variation is present in a region of the genome selected from the group consisting of gene fusion, gene duplication, gene deletion, gene translocation, microsatellite region, gene fragment, or combination thereof.In other embodiments, the genetic variant, rare mutation, or copy number variation is present in a region of the genome selected from the group consisting of gene, oncogene, tumor suppressor gene, promoter, regulatory sequence element, or combination thereof.In some embodiments, the variant is a nucleotide variant, single base substitution or small indel, transversion, translocation, inversion, deletion, truncation, or gene truncation, and is about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, or 20 nucleotides in length.

[0025] In some embodiments, the method includes a step of correcting / normalizing / adjusting the quantity of mapped reads using barcodes or unique characteristics of individual reads.

[0026] In some embodiments, the step of enumerating the reads is performed by enumerating the unique barcodes in each of the predefined regions and normalizing the number of them across at least a subset of the sequenced predefined regions. In some embodiments, samples from the same subject at subsequent time intervals are analyzed and compared with the results of the previous sample. The method of the present disclosure may further include measuring the frequency of partial copy number variation, loss of heterozygosity, gene expression analysis, epigenetic analysis, and / or hypermethylation analysis after amplifying the barcoded extracellular polynucleotides.

[0027] In some embodiments, the analysis of copy number variation and rare mutation is measured in the cell-free or substantially cell-free sample obtained from the subject by using multiplex sequencing, comprising: performing more than 10,000 sequencing reactions; simultaneously sequencing at least 10,000 different reads; or performing data analysis for at least 10,000 different reads across genome.The method can comprise multiplex sequencing, comprising performing data analysis for at least 10,000 different reads across genome.The method can further comprise the step of enumerating the uniquely identifiable sequenced reads.

[0028] In some embodiments, the methods of the present disclosure include normalization and detection performed using one or more of Hidden Markov, Dynamic Programming, Support Vector Machines, Bayesian Networks, Trellis Decoding, Viterbi Decoding, Expectation Maximization, Kalman Filtering or Neural Network methods.

[0029] In some embodiments, the methods of the disclosure include monitoring disease progression, monitoring residual disease, monitoring a treatment, diagnosing a condition, prognosing a condition, or selecting a treatment based on the discovered variants.

[0030] In some embodiments, treatment is modified based on the latest sample analysis. The disclosed method also includes inferring the genetic profile of the tumor, infection or other tissue abnormality. In some embodiments, the growth, remission or progression of the tumor, infection or other tissue abnormality is monitored. In some embodiments, the subject's immune system is analyzed and monitored at a single instance or over time.

[0031] In some embodiments, the methods of the present disclosure include identification of variants that are followed up through imaging studies (e.g., CT, PET-CT, MRI, X-ray, ultrasound) to identify the location of tissue abnormalities suspected to cause the identified variant.

[0032] In some embodiments, the methods of the disclosure involve the use of genetic data obtained from tissue or tumor biopsies from the same patient, hi some embodiments, the phylogeny of tumors, infections or other tissue abnormalities is inferred.

[0033] In some embodiments, the disclosed method includes population-based no-calling and identifying low confidence regions. In some embodiments, obtaining sequence coverage measurements includes measuring sequence coverage depth at every position of the genome. In some embodiments, correcting for bias in sequence coverage measurements includes calculating window average coverage. In some embodiments, correcting for bias in sequence coverage measurements includes making adjustments to account for GC bias in the library construction and sequencing process. In some embodiments, correcting for bias in sequence coverage measurements includes making adjustments based on additional weighting factors associated with individual mappings to offset bias.

[0034] In some embodiments, the methods of the present disclosure include extracellular polynucleotides derived from a diseased cellular source. In some embodiments, the extracellular polynucleotides are derived from a healthy cellular source.

[0035] The present disclosure also provides a system comprising a computer readable medium for carrying out the following steps: selecting a predefined region in a genome; enumerating the number of sequence reads in the predefined region; normalizing the number of sequence reads across the predefined region; and determining the percentage of copy number variation in the predefined region. In some embodiments, the entire genome or at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% or 90% of the genome is analyzed. In some embodiments, the computer readable medium provides data to an end user regarding the percentage of cancer DNA or cancer RNA in plasma or serum.

[0036] In some embodiments, the amount of genetic variation, such as polymorphisms or causal variants, is analyzed. In some embodiments, the presence or absence of a genetic alteration is detected.

[0037] The present disclosure also provides a method for detecting rare mutations in a cell-free or substantially cell-free sample obtained from a subject, the method comprising: a) sequencing extracellular polynucleotides from a bodily sample of the subject, each of which generates a plurality of sequencing reads; b) filtering out reads that do not meet a specified threshold; c) mapping sequence reads from the sequencing step onto a reference sequence; d) identifying a subset of mapped sequence reads that align with variants of the reference sequence at each mappable base position; e) calculating, for each mappable base position, a ratio of (a) the number of mapped sequence reads that contain a variant compared to the reference sequence to (b) the total number of sequence reads for each mappable base position; f) normalizing the ratio or frequency of variance for each mappable base position to determine potential rare variants or other genetic alterations; and g) comparing the numbers obtained for each region.

[0038] The present disclosure also provides a method comprising the steps of: a. providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b. generating a corresponding set of amplified progeny polynucleotides by amplifying the tagged parent polynucleotides in the set; c. generating a set of sequencing reads by sequencing a subset (including a suitable subset) of the set of amplified progeny polynucleotides; and d. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides. In certain embodiments, the method further comprises e. analyzing the set of consensus sequences for each set of tagged parent molecules.

[0039] In some embodiments, each polynucleotide in the set is mappable to a reference sequence.

[0040] In some embodiments, the method comprises providing multiple sets of tagged parent polynucleotides, each set mappable to a different reference sequence.

[0041] In some embodiments, the method further comprises converting the initial starting genetic material into tagged parent polynucleotides.

[0042] In some embodiments, the initial starting genetic material comprises 100 ng or less of polynucleotide.

[0043] In some embodiments, the method includes a step of bottlenecking the initial genetic material prior to conversion.

[0044] In some embodiments, the method comprises converting initial starting genetic material into tagged parent polynucleotides with a conversion efficiency of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 80% or at least 90%.

[0045] In some embodiments, the conversion step comprises any of blunt end ligation, sticky end ligation, molecular inversion probes, PCR, ligation-based PCR, single stranded ligation and single stranded circularization.

[0046] In some embodiments, the initial genetic material is cell-free nucleic acid.

[0047] In some embodiments, the multiple reference sequences are derived from the same genome.

[0048] In some embodiments, each of the tagged parent polynucleotides in a set is uniquely tagged.

[0049] In some embodiments, the tag is not unique.

[0050] In some embodiments, the generation of the consensus sequence is based on information from the tags and / or at least one of sequence information at the beginning (start) region of the sequence read, sequence information at the end (stop) region of the sequence read, and the length of the sequence read.

[0051] In some embodiments, the method includes sequencing a subset of the set of amplified progeny polynucleotides sufficient to generate sequence reads for at least one progeny of each of at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 98%, at least 99%, at least 99.9%, or at least 99.99% of the unique polynucleotides among the set of tagged parent polynucleotides.

[0052] In some embodiments, at least one progeny is a plurality of progeny, eg, at least two, at least five, or at least ten progeny.

[0053] In some embodiments, the number of sequence reads in the set of sequence reads is greater than the number of unique tagged parent polynucleotides in the set of tagged parent polynucleotides.

[0054] In some embodiments, the subset of the set of sequenced amplified progeny polynucleotides is of sufficient size such that any nucleotide sequence represented in the set of tagged parent polynucleotides has at least a 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 98%, at least 99%, at least 99.9% or at least 99.99% probability of being represented in the set of consensus sequences, at a percentage equal to the percentage of sequencing error rate per base of the sequencing platform used.

[0055] In some embodiments, the method comprises enriching the set of amplified progeny polynucleotides for polynucleotides that map to one or more selected reference sequences by (i) selective amplification of sequences from the starting initial genetic material that are converted into tagged parent polynucleotides; (ii) selective amplification of the tagged parent polynucleotides; (iii) selective sequence capture of the amplified progeny polynucleotides; or (iv) selective sequence capture of the starting initial genetic material.

[0056] In some embodiments, the analyzing step includes normalizing a measure (eg, a number) generated from the set of consensus sequences to a measure generated from a set of consensus sequences from a control sample.

[0057] In some embodiments, the analyzing step comprises detecting mutations, rare mutations, single nucleotide variants, indels, copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, gene fusions, chromosomal fusions, gene truncations, gene amplifications, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, abnormal changes in nucleic acid methylation, infection or cancer.

[0058] In some embodiments, the polynucleotide comprises DNA, RNA, a combination of the two, or DNA+RNA-derived cDNA.

[0059] In some embodiments, a particular subset of polynucleotides is selected or enriched from the initial set of polynucleotides or the amplified polynucleotides based on their polynucleotide length in base pairs.

[0060] In some embodiments, the analysis further includes detection and monitoring of abnormalities or diseases in an individual, such as infection and / or cancer.

[0061] In some embodiments, the methods are performed in combination with immune repertoire profiling.

[0062] In some embodiments, the polynucleotides are extracted from the group consisting of blood, plasma, serum, urine, saliva, mucosal discharge, sputum, stool and tears.

[0063] In some embodiments, the folding step comprises detecting and / or correcting errors, nicks or damage present in the sense or antisense strand of the tagged parent polynucleotides or the amplified progeny polynucleotides.

[0064] The present disclosure also provides a method comprising detecting genetic variations in the initial starting genetic material with a sensitivity of at least 5%, at least 1%, at least 0.5%, at least 0.1%, or at least 0.05%. In some embodiments, the initial starting genetic material is provided in an amount of nucleic acid less than 100 ng, the genetic variations are copy number variations / heterozygous variations, and the detecting step is performed at sub-chromosomal resolution; for example, at least 100 megabase resolution, at least 10 megabase resolution, at least 1 megabase resolution, at least 100 kilobase resolution, at least 10 kilobase resolution, or at least 1 kilobase resolution. In another embodiment, the method comprises providing a plurality of sets of tagged parent polynucleotides, each set being mappable to a different reference sequence. In another embodiment, the reference sequence is a tumor marker locus, and the analyzing step comprises detecting the tumor marker in the set of consensus sequences. In another embodiment, the tumor marker is present in the set of consensus sequences at a frequency lower than the error rate introduced in the amplifying step. In another embodiment, the at least one set is a plurality of sets, and the reference sequence comprises a plurality of reference sequences, each of which is a tumor marker locus. In another embodiment, the analyzing step comprises detecting copy number variation of consensus sequences between at least two sets of parent polynucleotides. In another embodiment, the analyzing step comprises detecting the presence of sequence variation compared to the reference sequence. In another embodiment, the analyzing step comprises detecting the presence of sequence variation compared to the reference sequence and detecting copy number variation of consensus sequences between at least two sets of parent polynucleotides. In another embodiment, the collapsing step comprises: i. grouping sequence reads sequenced from amplified progeny polynucleotides into families, each family being amplified from the same tagged parent polynucleotide; and ii. determining consensus sequences based on sequence reads within the families.

[0065] The disclosure also provides a system comprising a computer readable medium for performing the following steps: a. providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b. generating a corresponding set of amplified progeny polynucleotides by amplifying the tagged parent polynucleotides in the set; c. generating a set of sequencing reads by sequencing a subset (including a suitable subset) of the set of amplified progeny polynucleotides; and d. collapsing the set of sequencing reads to generate a set of consensus sequences, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides, and optionally e. analyzing the set of consensus sequences for each set of tagged parent molecules.

[0066] The present disclosure also provides a method comprising: a. providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b. generating a corresponding set of amplified progeny polynucleotides by amplifying the tagged parent polynucleotides in the set; c. generating a set of sequencing reads by sequencing a subset (including a suitable subset) of the set of amplified progeny polynucleotides; d. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides; and e. filtering out those consensus sequences that do not meet a quality threshold. In one embodiment, the quality threshold considers the number of sequence reads from the amplified progeny polynucleotides that collapse into a consensus sequence. In another embodiment, the quality threshold considers the number of sequence reads from the amplified progeny polynucleotides that collapse into a consensus sequence. The present disclosure also provides a system comprising a computer readable medium for performing the aforementioned method.

[0067] The present disclosure also provides a method comprising the steps of: a. providing at least one set of tagged parent polynucleotides, each set mapping to a different reference sequence in one or more genomes; and for each set of tagged parent polynucleotides; i. generating a set of amplified polynucleotides by amplifying a first polynucleotide; ii. generating a set of sequencing reads by sequencing a subset of the amplified polynucleotides of the set; iii.1. collapsing the sequence reads by grouping sequence reads sequenced from the amplified progeny polynucleotides into families, each family being amplified from the same tagged parent polynucleotide. In one embodiment, the collapsing step further comprises: 2. determining a quantitative measure of sequence reads within each family. In another embodiment, the method further comprises: b. determining a quantitative measure of unique families; and c. inferring a measure of tagged unique parent polynucleotides within the set based on (1) the quantitative measure of unique families and (2) the quantitative measure of sequence reads within each group (including a) including a). In another embodiment, the inference step is performed using a statistical or probabilistic model. In another embodiment, the at least one set is a plurality of sets. In another embodiment, the method further comprises correcting for amplification or representational bias between the two sets. In another embodiment, the method further comprises correcting for amplification or representational bias between the two sets by using a control or a set of control samples. In another embodiment, the method further comprises determining copy number variation between the sets. In another embodiment, the method further comprises d. determining a quantitative measure of polymorphic morphology between the families; and e. inferring a quantitative measure of polymorphic morphology in the number of inferred tagged unique parent polynucleotides based on the determined quantitative measure of polymorphic morphology (including a, b, c).In another embodiment, the form of polymorphism includes, but is not limited to, substitution, insertion, deletion, inversion, microsatellite change, transversion, translocation, fusion, methylation, hypermethylation, hydroxymethylation, acetylation, epigenetic variant, regulatory-associated variant or protein binding site. In another embodiment, the set is derived from a common sample, the method further comprises: a. inferring copy number variation for the plurality of sets based on a comparison of the inferred number of tagged parent polynucleotides in each set located in each of the plurality of reference sequences. In another embodiment, the original number of polynucleotides in each set is further inferred. The present disclosure also provides a system comprising a computer readable medium for carrying out the above-mentioned method.

[0068] The present disclosure also provides a method for determining copy number variation in a sample containing polynucleotides, the method comprising the steps of: a. providing at least two sets of first polynucleotides, each set being located in a different reference sequence in a genome, for each set of first polynucleotides; i. amplifying the polynucleotides to generate a set of amplified polynucleotides; ii. sequencing a subset of the amplified polynucleotides of the set to generate a set of sequencing reads; iii. grouping the sequence reads sequenced from the amplified polynucleotides into families, each family being amplified from the same first polynucleotide in the set; iv. inferring a quantitative measure of the families in the set; b. determining copy number variation by comparing the quantitative measures of the families in each set. The present disclosure also provides a system comprising a computer readable medium for performing the above method.

[0069] The present disclosure also provides a method of inferring a frequency of a sequence call in a sample of polynucleotides, the method comprising the steps of: a. providing at least one set of first polynucleotides, each set mapping to a different reference sequence in one or more genomes, and for each set of first polynucleotides; i. generating a set of amplified polynucleotides by amplifying the first polynucleotides; ii. generating a set of sequencing reads by sequencing a subset of the amplified polynucleotides of the set; iii. grouping the sequence reads into families, each family comprising sequence reads of amplified polynucleotides amplified from the same first polynucleotide; b. inferring, for each set of first polynucleotides, a call frequency for one or more bases in the set of first polynucleotides, the inferring comprising: i. for each family, assigning a confidence score for each of a plurality of calls, the confidence score taking into account the frequency of the call among members of the family; and ii. estimating the frequency of the one or more calls taking into account the confidence scores of the one or more calls assigned to each family. The present disclosure also provides a system comprising a computer-readable medium for performing the aforementioned method.

[0070] The present disclosure also provides a method of communicating sequence information for at least one individual polynucleotide molecule, the method comprising: a. providing at least one individual polynucleotide molecule; b. generating a signal by encoding sequence information in the at least one individual polynucleotide molecule; c. passing at least a portion of the signal through a channel to generate a received signal including nucleotide sequence information for the at least one individual polynucleotide molecule, the received signal including noise and / or distortion; d. generating a message including sequence information for the at least one individual polynucleotide molecule by decoding the received signal, the decoding reducing noise and / or distortion in the message; and e. providing the message to a recipient. In one embodiment, the noise includes erroneous nucleotide calls. In another embodiment, the distortion includes uneven amplification of an individual polynucleotide molecule relative to other individual polynucleotide molecules. In another embodiment, the distortion is due to amplification bias or sequencing bias. In another embodiment, the at least one individual polynucleotide molecule is a plurality of individual polynucleotide molecules, and the decoding generates a message for each molecule in the plurality. In another embodiment, the encoding step comprises amplifying at least each individual polynucleotide molecule, optionally tagged, and the signal comprises a collection of amplified molecules. In another embodiment, the channel comprises a polynucleotide sequence analyzer, and the received signal comprises a plurality of polynucleotide sequence reads amplified from at least one individual polynucleotide molecule. In another embodiment, the decoding step comprises grouping the sequence reads of the amplified molecules amplified from each of at least one individual polynucleotide molecule. In another embodiment, the decoding step comprises a probabilistic or statistical method of sorting the generated sequence signals. The present disclosure also provides a system comprising a computer readable medium for performing the aforementioned method.

[0071] In another embodiment, the polynucleotide is derived from tumor genomic DNA or RNA. In another embodiment, the polynucleotide is derived from cell-free, exosomal, bacterial or viral polynucleotides. In another embodiment, the method further comprises detection and / or association of affected molecular pathways. In another embodiment, the method further comprises continuous monitoring of the health or disease state of an individual. In another embodiment, the phylogeny of genomes associated with disease in an individual is inferred. In another embodiment, the method further comprises diagnosis, monitoring or treatment of disease. In another embodiment, the treatment regimen is selected or modified based on the detected polymorphic forms or CNVs or associated pathways. In another embodiment, the treatment comprises combination therapy.

[0072] The disclosure also provides a computer readable medium in a non-transitory, tangible form comprising executable code configured to perform the following steps: selecting a predefined region within the genome; accessing sequence reads and enumerating the number of sequence reads in the predefined region; normalizing the number of sequence reads across the predefined region; and determining the percent of copy number variation in the predefined region.

[0073] The disclosure also provides a computer readable medium in a non-transitory, tangible form comprising executable code configured to perform the following steps: a. accessing a data file comprising a plurality of sequencing reads; b. filtering out reads that do not meet a specified threshold; c. mapping sequence reads derived from the sequencing step onto a reference sequence; d. identifying a subset of mapped sequence reads that align with variants of the reference sequence at each mappable base position; e. calculating, for each mappable base position, a ratio of (a) the number of mapped sequence reads that contain a variant relative to the reference sequence to (b) the total number of sequence reads for each mappable base position; f. normalizing the ratio or frequency of variance for each mappable base position to determine potential rare variants or other genetic alterations; and g. comparing the numbers obtained for each of the regions containing potential rare variants or mutations to numbers similarly obtained from a reference sample.

[0074] The present disclosure also provides a computer readable medium in a non-transitory, tangible form comprising executable code configured to perform the following steps: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; b. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides.

[0075] The present disclosure also provides a computer readable medium in a non-transitory, tangible form that includes executable code configured to perform the following steps: a. accessing a data file including a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; b. collapsing the set of sequencing reads to generate a set of consensus sequences, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides; and c. filtering out from among the consensus sequences those that do not meet a quality threshold.

[0076] The present disclosure also provides a non-transitory, tangible form of computer readable medium comprising executable code configured to perform the following steps: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; and i.1. grouping the sequence reads sequenced from the amplified progeny polynucleotides into families, each family being amplified from the same tagged parent polynucleotide, and optionally, 2. collapsing the sequence reads by determining a quantitative measure of the sequence reads within each family. In certain embodiments, the executable code further performs b. determining a quantitative measure of unique families; c. inferring a measure of unique tagged parent polynucleotides within the set based on (1) the quantitative measure of unique families and (2) the quantitative measure of the sequence reads within each group. In certain embodiments, the executable code further performs the steps of: d. determining a quantitative measure of polymorphic morphology between the families; and e. inferring a quantitative measure of polymorphic morphology in the number of inferred tagged unique parent polynucleotides based on the determined quantitative measure of polymorphic morphology.

[0077] The disclosure also provides a computer readable medium in a non-transitory, tangible form comprising executable code configured to perform the following steps: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides, and grouping the sequence reads sequenced from the amplified polynucleotides into families, each family amplified from the same first polynucleotide in the set; b. inferring quantitative measures for the families in the set; and c. determining copy number variation by comparing the quantitative measures for the families in each set.

[0078] The disclosure also provides a computer readable medium in a non-transitory, tangible form comprising executable code configured to perform the following steps: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides, and grouping the sequence reads into families, each family comprising sequence reads of amplified polynucleotides amplified from the same first polynucleotide; b. for each set of first polynucleotides, inferring a call frequency for one or more bases in the set of first polynucleotides, the inferring comprising: c. for each family, assigning a confidence score for each of the plurality of calls, the confidence score taking into account the frequency of the call among members of the family; and d. estimating the frequency of the one or more calls taking into account the confidence scores of the one or more calls assigned to each family.

[0079] The disclosure also provides a computer readable medium in a non-transitory, tangible form comprising executable code configured to perform the following steps: a. accessing a data file comprising a received signal comprising encoded sequence information from at least one individual polynucleotide molecule, the received signal comprising noise and / or distortion; b. generating a message comprising sequence information for at least one individual polynucleotide molecule by decoding the received signal, the decoding reducing noise and / or distortion for each individual polynucleotide in the message; and c. writing the message comprising sequence information for the at least one individual polynucleotide molecule to a computer file.

[0080] The present disclosure also provides a computer readable medium in a non-transitory, tangible form that includes executable code configured to perform the following steps: a. accessing a data file including a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; b. collapsing the set of sequencing reads to generate a set of consensus sequences, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides; and c. filtering out from among the consensus sequences those that do not meet a quality threshold.

[0081] The present disclosure also provides a non-transitory tangible form of computer readable medium comprising executable code configured to perform the following steps: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; and b. grouping the sequence reads sequenced from the amplified progeny polynucleotides into families, each family being amplified from the same tagged parent polynucleotide; and ii. optionally collapsing the sequence reads by determining a quantitative measure of the sequence reads within each family. In certain embodiments, the executable code further performs c. determining a quantitative measure of unique families; d. inferring a measure of the tagged unique parent polynucleotides within the set based on (1) the quantitative measure of the unique families and (2) the quantitative measure of the sequence reads within each group. In certain embodiments, the executable code further performs the steps of: e. determining a quantitative measure of polymorphic morphology between the families; and f. inferring a quantitative measure of polymorphic morphology in the number of inferred unique tagged parent polynucleotides based on the determined quantitative measure of polymorphic morphology. In certain embodiments, the executable code further performs the steps of: e. inferring copy number variation for the plurality of sets based on a comparison of the inferred number of tagged parent polynucleotides in each set that map to each of the plurality of reference sequences.

[0082] The disclosure also provides a computer readable medium in a non-transitory, tangible form comprising executable code configured to perform the following steps: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; b. grouping the sequence reads sequenced from the amplified polynucleotides into families, each family amplified from the same first polynucleotide in the set; c. inferring a quantitative measure for the families in the set; and d. determining copy number variation by comparing the quantitative measures for the families in each set.

[0083] The disclosure also provides a computer readable medium in a non-transitory, tangible form comprising executable code configured to perform the following steps: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides, and grouping the sequence reads into families, each family comprising sequence reads of amplified polynucleotides amplified from the same first polynucleotide; and b. inferring, for each set of first polynucleotides, a call frequency for one or more bases in the set of first polynucleotides, the inferring comprising: i. for each family, assigning a confidence score for each of the plurality of calls, the confidence score taking into account the frequency of the call among members of the family; and ii. estimating the frequency of the one or more calls taking into account the confidence scores of the one or more calls assigned to each family.

[0084] The present disclosure also provides a method comprising: a. providing a sample comprising 100 to 100,000 haploid human genome equivalent cell-free DNA (cfDNA) polynucleotides; and b. tagging the polynucleotides with 2 to 1,000,000 unique identifiers. In certain embodiments, the number of unique identifiers is at least 3, at least 5, at least 10, at least 15, or at least 25 and at most 100, at most 1000, or at most 10,000. In certain embodiments, the number of unique identifiers is at most 100, at most 1000, at most 10,000, or at most 100,000.

[0085] The present disclosure provides a method for determining a method for identifying a polynucleotide sequence comprising: a. providing a sample comprising a plurality of human haploid genome equivalents of fragmented polynucleotides; b. determining z, where z is a measure of central tendency (e.g., mean, median, or mode) of the expected number of duplicated polynucleotides starting at any position in the genome, where the duplicated polynucleotides have the same start and end positions; and c. tagging the polynucleotides in the sample with n unique identifiers, where n is between 2 and 100,000. * z, 2 to 10,000 * z, 2 to 1,000 * z or 2 to 100 * z.

[0086] The present disclosure also provides a method comprising the steps of: a. providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b. generating a set of sequencing reads by generating a plurality of sequence reads for each of the tagged parent polynucleotides in the set; and c. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides.

[0087] The present disclosure provides a method for detecting copy number variation, comprising the steps of: a) sequencing extracellular polynucleotides from a body sample of a subject, each of which generates a plurality of sequencing reads; b) filtering out reads that do not meet a specified threshold; c) mapping the sequence reads obtained from step (a) to a reference sequence after filtering out the reads; d) quantifying or enumerating the mapped reads in two or more predefined regions of the reference sequence; and e) (ii) normalizing the numbers of reads in the predefined regions to each other and / or the numbers of unique sequence reads in the predefined regions to each other; and (ii) determining copy number variation in one or more of the predefined regions by comparing the normalized numbers obtained in step (i) to normalized numbers obtained from a control sample.

[0088] The present disclosure also provides a method for detecting rare mutations in a cell-free or substantially cell-free sample obtained from a subject, the method comprising the steps of: a) sequencing extracellular polynucleotides from a bodily sample of the subject, each of which generates a plurality of sequencing reads; b) performing multiplex sequencing in the region or, if enrichment is not performed, performing whole genome sequencing; c) filtering out reads that do not meet a specified threshold; d) mapping the sequence reads from the sequencing step onto a reference sequence; e) identifying a subset of mapped sequence reads that align with variants of the reference sequence at each mappable base position; f) calculating, for each mappable base position, a ratio of (a) the number of mapped sequence reads that contain a variant compared to the reference sequence and (b) the total number of sequence reads for each mappable base position; g) normalizing the frequency of the ratio or variance for each mappable base position to determine potential rare variants or mutations; and h) comparing the numbers obtained for each of the regions containing potential rare variants or mutations to numbers similarly obtained from a reference sample.

[0089] The present disclosure also provides a method of characterizing heterogeneity of an abnormal condition in a subject, the method comprising generating a genetic profile of extracellular polynucleotides in the subject, the genetic profile comprising a plurality of data resulting from an analysis of copy number variations and rare mutations.

[0090] In some embodiments, the prevalence / concentration of each rare variant identified in a subject is reported and quantified simultaneously, hi some embodiments, a confidence score for the prevalence / concentration of rare variants in a subject is reported.

[0091] In some embodiments, the extracellular polynucleotide comprises DNA. In some embodiments, the extracellular polynucleotide comprises RNA.

[0092] In some embodiments, the method further comprises isolating the extracellular polynucleotides from the body sample. In some embodiments, the isolating step comprises a method for the isolation and extraction of circulating nucleic acids. In some embodiments, the method further comprises fragmenting the isolated extracellular polynucleotides. In some embodiments, the body sample is selected from the group consisting of blood, plasma, serum, urine, saliva, mucosal discharge, sputum, stool, and tears.

[0093] In some embodiments, the method further comprises determining the percent of sequences in the body sample that have copy number variations or rare mutations or variants, hi some embodiments, the determining step comprises calculating the percentage of predefined regions that have an amount of polynucleotides above or below a predefined threshold.

[0094] In some embodiments, the subject is suspected of having an abnormal condition. In some embodiments, the abnormal condition is selected from the group consisting of mutations, rare mutations, indels, copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, gene fusions, chromosomal fusions, gene truncations, gene amplifications, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, abnormal changes in nucleic acid methylation, infections, and cancers.

[0095] In some embodiments, the subject is a pregnant woman.In some embodiments, the copy number variation or rare mutation or genetic variant indicates fetal abnormality.In some embodiments, the fetal abnormality is selected from the group consisting of mutation, rare mutation, indel, copy number variation, transversion, translocation, inversion, deletion, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural change, gene fusion, chromosomal fusion, gene truncation, gene amplification, gene duplication, chromosomal damage, DNA damage, abnormal change of nucleic acid chemical modification, abnormal change of epigenetic pattern, abnormal change of nucleic acid methylation, infection and cancer.

[0096] In some embodiments, the method further comprises attaching one or more barcodes to the extracellular polynucleotide or fragment thereof prior to sequencing. In some embodiments, each barcode attached to the extracellular polynucleotide or fragment thereof prior to sequencing is unique. In some embodiments, each barcode attached to the extracellular polynucleotide or fragment thereof prior to sequencing is not unique.

[0097] In some embodiments, the method further comprises selectively enriching regions from the subject's genome or transcriptome prior to sequencing. In some embodiments, the method further comprises non-selectively enriching regions from the subject's genome or transcriptome prior to sequencing.

[0098] In some embodiments, the method further comprises attaching one or more barcodes to the extracellular polynucleotides or fragments thereof prior to any amplifying or enriching step. In some embodiments, the barcode is a polynucleotide. In some embodiments, the barcode comprises a random sequence. In some embodiments, the barcode comprises a fixed or semi-random set of oligonucleotides that, in combination with the diversity of molecules sequenced from a selected region, allows for the identification of unique molecules. In some embodiments, the barcode comprises an oligonucleotide that is at least 3, 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50-mer base pairs in length.

[0099] In some embodiments, the method further comprises amplifying the extracellular polynucleotide or a fragment thereof. In some embodiments, the amplification comprises global amplification or whole genome amplification. In some embodiments, the amplification comprises selective amplification. In some embodiments, the amplification comprises non-selective amplification. In some embodiments, suppression amplification or enrichment by subtraction is performed.

[0100] In some embodiments, unique identity sequence reads are detected based on sequence information at the beginning (start) and end (end) regions of the sequence read and the length of the sequence read. In some embodiments, unique identity sequence molecules are detected based on sequence information at the beginning (start) and end (end) regions of the sequence read, the length of the sequence read, and the attachment of barcodes.

[0101] In some embodiments, the method further comprises removing a subset of reads from further analysis before quantifying or enumerating the reads. In some embodiments, the removing comprises excluding reads having an accuracy or quality score below a threshold, e.g., below 90%, 99%, 99.9% or 99.99%, and / or a mapping score below a threshold, e.g., below 90%, 99%, 99.9% or 99.99%. In some embodiments, the method further comprises culling reads having a quality score below a specified threshold.

[0102] In some embodiments, the predefined regions are uniform or substantially uniform in size, hi some embodiments, the predefined regions are at least about 10 kb, 20 kb, 30 kb, 40 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90 kb, or 100 kb in size.

[0103] In some embodiments, at least 50, 100, 200, 500, 1000, 2000, 5000, 10,000, 20,000 or 50,000 regions are analyzed.

[0104] In some embodiments, the variant appears in a region of the genome selected from the group consisting of gene fusions, gene duplications, gene deletions, gene translocations, microsatellite regions, gene fragments, or combinations thereof. In some embodiments, the variant appears in a region of the genome selected from the group consisting of genes, oncogenes, tumor suppressor genes, promoters, regulatory sequence elements, or combinations thereof. In some embodiments, the variant is a nucleotide variant of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, or 20 nucleotides in length, a single base substitution, a small indel, a transversion, a translocation, an inversion, a deletion, a truncation, or a gene truncation.

[0105] In some embodiments, the method further comprises correcting / normalizing / adjusting the number of mapped reads using barcodes or unique characteristics of individual reads. In some embodiments, enumerating the reads is performed by enumerating the unique barcodes in each of the predefined regions and normalizing their number across at least a subset of the predefined regions that have been sequenced.

[0106] In some embodiments, samples from the same subject at subsequent time intervals are analyzed and compared to the results of the previous sample. In some embodiments, the method further comprises amplifying the barcoded extracellular polynucleotides. In some embodiments, the method further comprises determining the frequency of partial copy number variations, measuring loss of heterozygosity, performing gene expression analysis, performing epigenetic analysis, and / or performing hypermethylation analysis.

[0107] The present disclosure also provides methods that include determining copy number variation or performing rare mutation analysis in a cell-free or substantially cell-free sample obtained from a subject using multiplex sequencing.

[0108] In some embodiments, the multiplexed sequencing comprises performing more than 10,000 sequencing reactions. In some embodiments, the multiplexed sequencing comprises simultaneously sequencing at least 10,000 different reads. In some embodiments, the multiplexed sequencing comprises performing data analysis on at least 10,000 different reads across the genome. In some embodiments, the normalization and detection are performed using one or more of hidden Markov, dynamic programming, support vector machines, Bayesian or probability modeling, trellis decoding, Viterbi decoding, expectation maximization, Kalman filtering, or neural network methods. In some embodiments, the method further comprises monitoring disease progression, monitoring residual disease, monitoring treatment, diagnosing a condition, prognosing a condition, or selecting a treatment based on the discovered variant for the subject. In some embodiments, the treatment is modified based on the latest sample analysis. In some embodiments, a genetic profile of a tumor, infection, or other tissue abnormality is inferred.

[0109] In some embodiments, tumor growth, remission or progression, infection or other tissue abnormality is monitored. In some embodiments, sequences related to the immune system of the subject are analyzed and monitored at a single instance or over time. In some embodiments, the identification of variants is tracked through imaging studies (e.g., CT, PET-CT, MRI, X-ray, ultrasound) to identify the location of tissue abnormalities suspected to cause the identified variants. In some embodiments, the analysis further comprises the use of genetic data obtained from tissue or tumor biopsies from the same patient. In some embodiments, the phylogeny of the tumor, infection or other tissue abnormality is inferred. In some embodiments, the method further comprises population-based uncalling and identification of low confidence regions. In some embodiments, obtaining the measurement data for sequence coverage comprises measuring the depth of sequence coverage at all positions of the genome. In some embodiments, correcting the measurement data for sequence coverage for bias comprises calculating a window average coverage. In some embodiments, correcting the measurement data for sequence coverage for bias comprises adjusting to account for GC bias in the library construction and sequencing process. In some embodiments, correcting for bias in the measured data for sequence coverage comprises making an adjustment based on an additional weighting factor associated with each mapping to offset the bias.

[0110] In some embodiments, the extracellular polynucleotide is derived from a diseased cellular source. In some embodiments, the extracellular polynucleotide is derived from a healthy cellular source.

[0111] The present disclosure also provides a system comprising a computer readable medium for performing the following steps: selecting a predefined region within a genome; enumerating the number of sequence reads within the predefined region; normalizing the number of sequence reads across the predefined region; and determining the percent of copy number variation within the predefined region.

[0112] In some embodiments, the entire genome or at least 85% of the genome is analyzed. In some embodiments, the computer readable medium provides data to an end user regarding the percentage of cancer DNA or cancer RNA in plasma or serum. In some embodiments, the identified copy number variants are fractional (i.e., non-integer levels) due to heterogeneity in the sample. In some embodiments, enrichment of selected regions is performed. In some embodiments, copy number variation information is simultaneously extracted based on the methods described herein. In some embodiments, the method includes an initial step of bottlenecking polynucleotides to limit the initial starting copy number or diversity of polynucleotides in the sample.

[0113] The present disclosure also provides a method for detecting rare mutations in a cell-free or substantially cell-free sample obtained from a subject, the method comprising the steps of: a) sequencing extracellular polynucleotides from a bodily sample of the subject, each of which generates a plurality of sequencing reads; b) filtering out reads that do not meet a specified quality threshold; c) mapping sequence reads from the sequencing step onto a reference sequence; d) identifying a subset of mapped sequence reads that align with variants of the reference sequence at each mappable base position; e) calculating, for each mappable base position, a ratio of (a) the number of mapped sequence reads that contain a variant compared to the reference sequence to (b) the total number of sequence reads for each mappable base position; f) normalizing the ratio or frequency of variance for each mappable base position to determine potential rare variants or other genetic alterations; and g) comparing the numbers obtained for each of the regions containing potential rare variants or mutations to numbers similarly obtained from a reference sample.

[0114] The disclosure also provides a method comprising the steps of: a) providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b) generating a corresponding set of amplified progeny polynucleotides by amplifying the tagged parent polynucleotides in the set; c) generating a set of sequencing reads by sequencing a subset (including a suitable subset) of the set of amplified progeny polynucleotides; and d) generating a set of consensus sequences by collapsing the set of sequencing reads, wherein each consensus sequence corresponds to a unique polynucleotide among the set of tagged parent polynucleotides.

[0115] In some embodiments, each polynucleotide in a set is mappable to a reference sequence. In some embodiments, the method includes providing a plurality of sets of tagged parent polynucleotides, each set being mappable to a different mappable position in the reference sequence. In some embodiments, the method further includes e) analyzing the consensus sequence of each set of tagged parent molecules, either separately or in combination. In some embodiments, the method further includes converting the initial starting genetic material to tagged parent polynucleotides. In some embodiments, the initial starting genetic material includes 100 ng or less of polynucleotides. In some embodiments, the method includes restricting the initial starting genetic material prior to conversion. In some embodiments, the method includes converting the initial starting genetic material to tagged parent polynucleotides with a conversion efficiency of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 80%, or at least 90%. In some embodiments, the conversion step comprises any of blunt end ligation, sticky end ligation, molecular inversion probe, PCR, ligation-based PCR, single-stranded ligation and single-stranded circularization. In some embodiments, the initial genetic material is cell-free nucleic acid. In some embodiments, the multiple sets are located at different mappable positions in the reference sequence from the same genome.

[0116] In some embodiments, each of the tagged parent polynucleotides in the set is uniquely tagged.In some embodiments, each set of parent polynucleotides can be mapped to a position in a reference sequence, and the polynucleotides in each set are not uniquely tagged.In some embodiments, the generation of consensus sequence is based on information from the tag and / or at least one of (i) sequence information in the beginning (start) region of sequence read, (ii) the end (stop) region of sequence read, and (iii) the length of sequence read.

[0117] In some embodiments, the method includes sequencing a subset of the set of amplified progeny polynucleotides sufficient to generate sequence reads for at least one progeny of at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 98%, at least 99%, at least 99.9%, or at least 99.99% of each of the unique polynucleotides in the set of tagged parent polynucleotides. In some embodiments, the at least one progeny is a plurality of progeny, e.g., at least two, at least five, or at least ten progeny. In some embodiments, the number of sequence reads in the set of sequence reads is greater than the number of tagged unique parent polynucleotides in the set of tagged parent polynucleotides. In some embodiments, the subset of the set of sequenced amplified progeny polynucleotides is of sufficient size such that any nucleotide sequence represented in the set of tagged parent polynucleotides has at least a 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 98%, at least 99%, at least 99.9% or at least 99.99% probability of being represented in the set of consensus sequences, at a percentage equal to the percentage of sequencing error rate per base of the sequencing platform used.

[0118] In some embodiments, the method comprises enriching the set of amplified progeny polynucleotides for polynucleotides that map to one or more selected mappable locations in a reference sequence by (i) selective amplification of sequences from the starting initial genetic material that are converted into tagged parent polynucleotides; (ii) selective amplification of the tagged parent polynucleotides; (iii) selective sequence capture of the amplified progeny polynucleotides; or (iv) selective sequence capture of the starting initial genetic material.

[0119] In some embodiments, the analyzing step includes normalizing the scale (e.g., number) generated from the set of consensus sequences to the scale generated from the set of consensus sequences from a control sample. In some embodiments, the analyzing step includes detecting mutations, rare mutations, indels, copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, gene fusions, chromosomal fusions, gene truncations, gene amplifications, gene duplications, chromosomal damage, DNA damage, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, abnormal changes in nucleic acid methylation, infections, or cancers.

[0120] In some embodiments, the polynucleotides comprise DNA, RNA, a combination of the two, or DNA+RNA derived cDNA. In some embodiments, a certain subset of polynucleotides is selected or enriched from the initial set of polynucleotides or the amplified polynucleotides based on their polynucleotide length in base pairs. In some embodiments, the analysis further comprises detection and monitoring of abnormalities or diseases in an individual, such as infection and / or cancer. In some embodiments, the method is performed in combination with profiling of immune repertoire. In some embodiments, the polynucleotides are extracted from a sample selected from the group consisting of blood, plasma, serum, urine, saliva, mucosal discharge, sputum, stool, and tears. In some embodiments, the folding step comprises detecting and / or correcting errors, nicks, or lesions present in the sense or antisense strand of the tagged parent polynucleotides or the amplified progeny polynucleotides.

[0121] The disclosure also provides a method that includes detecting genetic variations in initial genetic material that is not uniquely tagged with a sensitivity of at least 5%, at least 1%, at least 0.5%, at least 0.1%, or at least 0.05%.

[0122] In some embodiments, the initial genetic material is provided in an amount of nucleic acid less than 100ng, the genetic variation is a copy number variation / heterozygous variation, and the detecting step is performed at a resolution smaller than chromosome; for example, at least 100 megabase resolution, at least 10 megabase resolution, at least 1 megabase resolution, at least 100 kilobase resolution, at least 10 kilobase resolution, or at least 1 kilobase resolution. In some embodiments, the method includes providing a plurality of sets of tagged parent polynucleotides, each set being mappable to a different mappable location in a reference sequence. In some embodiments, the mappable location in a reference sequence is a tumor marker locus, and the analyzing step includes detecting the tumor marker within the consensus sequence of the set.

[0123] In some embodiments, tumor markers are present in the set of consensus sequences at a frequency lower than the error rate introduced in the amplifying step.In some embodiments, at least one set is a plurality of sets, and the mappable position of the reference sequence comprises a plurality of mappable positions in the reference sequence, each of which is the locus of the tumor marker.In some embodiments, the analyzing step comprises detecting the copy number variation of the consensus sequence between at least two sets of parent polynucleotides.In some embodiments, the analyzing step comprises detecting the presence of sequence variation compared to the reference sequence.

[0124] In some embodiments, the analyzing step comprises detecting the presence of sequence variations compared to the reference sequence and detecting copy number variations of consensus sequences between at least two sets of parent polynucleotides. In some embodiments, the collapsing step comprises (i) grouping sequence reads sequenced from the amplified progeny polynucleotides into families, each family being amplified from the same tagged parent polynucleotide; and (ii) determining a consensus sequence based on the sequence reads within the families.

[0125] The disclosure also provides a system comprising a computer-readable medium for performing the following steps: a) receiving at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b) generating a corresponding set of amplified progeny polynucleotides by amplifying the tagged parent polynucleotides in the set; c) generating a set of sequencing reads by sequencing a subset (including a suitable subset) of the set of amplified progeny polynucleotides; d) collapsing the set of sequencing reads to generate a set of consensus sequences, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides, and optionally e) analyzing the set of consensus sequences for each set of tagged parent molecules.

[0126] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 10% of the individual's genome is sequenced.

[0127] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 20% of the individual's genome is sequenced.

[0128] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 30% of the individual's genome is sequenced.

[0129] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 40% of the individual's genome is sequenced.

[0130] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 50% of the individual's genome is sequenced.

[0131] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 60% of the individual's genome is sequenced.

[0132] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 70% of the individual's genome is sequenced.

[0133] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 80% of the individual's genome is sequenced.

[0134] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 90% of the individual's genome is sequenced.

[0135] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 10% of the individual's genome is sequenced.

[0136] The disclosure also provides a method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 20% of the individual's genome is sequenced.

[0137] The disclosure also provides a method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 30% of the individual's genome is sequenced.

[0138] The disclosure also provides a method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 40% of the individual's genome is sequenced.

[0139] The disclosure also provides a method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 50% of the individual's genome is sequenced.

[0140] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 60% of the individual's genome is sequenced.

[0141] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 70% of the individual's genome is sequenced.

[0142] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 80% of the individual's genome is sequenced.

[0143] The present disclosure also provides a method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, wherein at least 90% of the individual's genome is sequenced.

[0144] In some embodiments, the genetic alterations are copy number variations or one or more rare mutations. In some embodiments, the genetic alterations include one or more causative variants and one or more polymorphisms. In some embodiments, the amount of genetic alterations and / or genetic alterations in an individual can be compared to the amount of genetic alterations and / or genetic alterations in one or more individuals with a known disease. In some embodiments, the amount of genetic alterations and / or genetic alterations in an individual can be compared to the amount of genetic alterations and / or genetic alterations in one or more individuals without the disease. In some embodiments, the cell-free nucleic acid is DNA. In some embodiments, the cell-free nucleic acid is RNA. In some embodiments, the cell-free nucleic acid is DNA and RNA. In some embodiments, the disease is cancer or a precancerous condition. In some embodiments, the method further comprises diagnosing or treating the disease.

[0145] The disclosure also provides a method comprising: a) providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b) generating a corresponding set of amplified progeny polynucleotides by amplifying the tagged parent polynucleotides in the set; c) generating a set of sequencing reads by sequencing a subset (including a suitable subset) of the set of amplified progeny polynucleotides; d) collapsing the set of sequencing reads to generate a set of consensus sequences, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides; and e) filtering out from among the consensus sequences that do not meet a quality threshold.

[0146] In some embodiments, the quality threshold considers the number of sequence reads from amplified progeny polynucleotides that collapse into a consensus sequence. In some embodiments, the quality threshold considers the number of sequence reads from amplified progeny polynucleotides that collapse into a consensus sequence.

[0147] The present disclosure also provides a system comprising a computer-readable medium for performing the methods described herein.

[0148] The disclosure also provides a method comprising: a) providing at least one set of tagged parent polynucleotides, each set located at a different mappable location in a reference sequence in one or more genomes, and for each set of tagged parent polynucleotides; i) generating a set of amplified polynucleotides by amplifying a first polynucleotide; ii) generating a set of sequencing reads by sequencing a subset of the amplified polynucleotides of the set; and iii) (1) collapsing the sequence reads by grouping sequence reads sequenced from the amplified progeny polynucleotides into families, each family amplified from the same tagged parent polynucleotide.

[0149] In some embodiments, the collapsing step further comprises determining a quantitative measure of sequence reads within each family. In some embodiments, the method further comprises a) determining a quantitative measure of unique families; and b) inferring a measure of tagged unique parent polynucleotides within the set based on (1) the quantitative measure of unique families and (2) the quantitative measure of sequence reads within each group. In some embodiments, the inferring step is performed using a statistical or probabilistic model. In some embodiments, the at least one set is a plurality of sets. In some embodiments, the method further comprises correcting for amplification or representation bias between the two sets. In some embodiments, the method further comprises correcting for amplification or representation bias between the two sets by using a control or set of control samples. In some embodiments, the method further comprises determining copy number variation between the sets.

[0150] In some embodiments, the method further comprises: d) determining a quantitative measure of polymorphic morphology between the family; and e) inferring a quantitative measure of polymorphic morphology in the number of inferred tagged unique parent polynucleotides based on the determined quantitative measure of polymorphic morphology. In some embodiments, polymorphic morphology includes, but is not limited to, substitutions, insertions, deletions, inversions, microsatellite alterations, transversions, translocations, fusions, methylation, hypermethylation, hydroxymethylation, acetylation, epigenetic variants, regulatory associated variants, or protein binding sites.

[0151] In some embodiments, the sets are derived from a common sample, and the method further comprises: d) inferring copy number variation for the plurality of sets based on a comparison of the inferred number of tagged parent polynucleotides in each set that are located at each of a plurality of mappable positions in the reference sequence. In some embodiments, the original number of polynucleotides in each set is further inferred. In some embodiments, at least a subset of tagged parent polynucleotides in each set is not uniquely tagged.

[0152] The present disclosure also provides a method for determining copy number variation in a sample comprising polynucleotides, the method comprising: a) providing at least two sets of first polynucleotides, each set located at a different mappable location in a reference sequence in a genome, and for each set of first polynucleotides; (i) amplifying the polynucleotides to generate a set of amplified polynucleotides; (ii) sequencing a subset of the amplified polynucleotides of the set to generate a set of sequencing reads; (iii) grouping sequence reads sequenced from the amplified polynucleotides into families, each family amplified from the same first polynucleotide in the set; (iv) inferring a quantitative measure for the families in the set; and b) determining copy number variation by comparing the quantitative measures for the families in each set.

[0153] The present disclosure also provides a method of inferring a frequency of a sequence call in a sample of polynucleotides, the method comprising the steps of: a) providing at least one set of first polynucleotides, each set located at a different mappable location in a reference sequence in one or more genomes, and for each set of first polynucleotides; (i) generating a set of amplified polynucleotides by amplifying the first polynucleotides; (ii) generating a set of sequencing reads by sequencing a subset of the amplified polynucleotides of the set; (iii) grouping the sequence reads into families, each family comprising sequence reads of amplified polynucleotides amplified from the same first polynucleotide; b) inferring, for each set of first polynucleotides, a call frequency for one or more bases in the set of first polynucleotides, the inferring comprising: (i) assigning, for each family, a confidence score for each of a plurality of calls, the confidence score taking into account the frequency of the call among members of the family; and (ii) estimating the frequency of the one or more calls taking into account the confidence scores of the one or more calls assigned to each family.

[0154] The disclosure also provides a method of communicating sequence information for at least one individual polynucleotide molecule, the method comprising: a) providing at least one individual polynucleotide molecule; b) generating a signal by encoding sequence information in the at least one individual polynucleotide molecule; c) passing at least a portion of the signal through a channel to generate a received signal including nucleotide sequence information for the at least one individual polynucleotide molecule, the received signal including noise and / or distortion; d) generating a message including sequence information for the at least one individual polynucleotide molecule by decoding the received signal, the decoding reducing noise and / or distortion for each individual polynucleotide in the message; and e) providing a message including sequence information for the at least one individual polynucleotide molecule to a recipient.

[0155] In some embodiments, the noise comprises an incorrect nucleotide call. In some embodiments, the distortion comprises uneven amplification of an individual polynucleotide molecule relative to other individual polynucleotide molecules. In some embodiments, the distortion is due to amplification bias or sequencing bias. In some embodiments, the at least one individual polynucleotide molecule is a plurality of individual polynucleotide molecules, and the decoding step generates a message for each molecule in the plurality. In some embodiments, the encoding step comprises amplifying at least one individual polynucleotide molecule, optionally tagged, and the signal comprises a collection of amplified molecules. In some embodiments, the channel comprises a polynucleotide sequence analyzer, and the received signal comprises a plurality of polynucleotide sequence reads amplified from at least one individual polynucleotide molecule. In some embodiments, the decoding step comprises grouping sequence reads of the amplified molecules amplified from each of the at least one individual polynucleotide molecule. In some embodiments, the decoding step comprises a probabilistic or statistical method of sorting the generated sequence signals.

[0156] In some embodiments, the polynucleotide is derived from tumor genomic DNA or RNA. In some embodiments, the polynucleotide is derived from cell-free polynucleotides, exosomal polynucleotides, bacterial polynucleotides, or viral polynucleotides. In some embodiments of any of the methods herein, the method further comprises detection and / or association of affected molecular pathways. In some embodiments of any of the methods herein, the method further comprises continuous monitoring of the health or disease state of the individual. In some embodiments, the phylogeny of genomes associated with the disease in the individual is inferred. In some embodiments, any of the methods described herein further comprises diagnosis, monitoring, or treatment of the disease. In some embodiments, treatment regimens are selected or modified based on the detected polymorphic forms or CNVs or associated pathways. In some embodiments, the treatment comprises combination therapy. In some embodiments, the diagnosis further comprises localizing the disease using radiography, e.g., CT-Scan, PET-CT, MRI, ultrasound, ultrasound with microbubbles, etc.

[0157] The disclosure also provides a computer readable medium comprising non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: selecting a predefined region within a genome; accessing sequence reads and enumerating a number of sequence reads in the predefined region; normalizing the number of sequence reads across the predefined region; and determining a percent of copy number variation in the predefined region.

[0158] The disclosure also provides a computer readable medium comprising non-transitory machine-executable code that, when executed by a computer processor, performs a method, the method comprising: accessing a data file comprising a plurality of sequencing reads; filtering out reads that do not meet a specified threshold; mapping sequence reads from the sequencing process onto a reference sequence; identifying a subset of mapped sequence reads that align with a variant of the reference sequence at each mappable base position; for each mappable base position, calculating a ratio of (a) the number of mapped sequence reads that contain a variant relative to the reference sequence and (b) the total number of sequence reads for each mappable base position; normalizing the ratio or frequency of variance for each mappable base position to determine potential rare variants or other genetic alterations; and comparing the numbers obtained for each of the regions containing potential rare variants or mutations to numbers similarly obtained from a reference sample.

[0159] The disclosure also provides a computer-readable medium comprising non-transitory machine-executable code that, when executed by a computer processor, performs a method, the method comprising: a) accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; and b) generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides.

[0160] The disclosure also provides a computer-readable medium comprising non-transitory machine-executable code that, when executed by a computer processor, performs a method, the method comprising: a) accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; b) collapsing the set of sequencing reads to generate a set of consensus sequences, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides; and c) filtering out from among the consensus sequences those that do not meet a quality threshold.

[0161] A computer readable medium comprising non-transitory machine-executable code that, when executed by a computer processor, performs a method comprising: a) accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; and i) (1) grouping the sequence reads sequenced from the amplified progeny polynucleotides into families, each family amplified from the same tagged parent polynucleotides, and optionally (2) collapsing the sequence reads by determining a quantitative measure of the sequence reads within each family.

[0162] In some embodiments, when executed by a computer processor, the executable code further performs the steps of: b) determining a quantitative measure of unique families; and c) inferring a measure of tagged unique parent polynucleotides in the set based on (1) the quantitative measure of unique families and (2) the quantitative measure of sequence reads in each group.

[0163] In some embodiments, when executed by a computer processor, the executable code further performs the steps of: d) determining a quantitative measure of polymorphic morphology between the families; and e) inferring a quantitative measure of polymorphic morphology in the number of inferred tagged unique parent polynucleotides based on the determined quantitative measure of polymorphic morphology.

[0164] The disclosure also provides a computer readable medium comprising non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a) accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides, and grouping sequence reads sequenced from the amplified polynucleotides into families, each family amplified from the same first polynucleotide in the set; b) inferring a quantitative measure of the families in the set; and c) determining copy number variation by comparing the quantitative measures of the families in each set.

[0165] The disclosure also provides a computer readable medium comprising non-transitory machine executable code that, when executed by a computer processor, performs a method comprising: a) accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides, and grouping the sequence reads into families, each family comprising sequence reads of amplified polynucleotides amplified from the same first polynucleotide; b) inferring, for each set of first polynucleotides, a call frequency for one or more bases in the set of first polynucleotides, the inferring comprising: c) assigning, for each family, a confidence score for each of a plurality of calls, the confidence score taking into account the frequency of the call among members of the family; and d) estimating a frequency of the one or more calls taking into account the confidence scores of the one or more calls assigned to each family.

[0166] The disclosure also provides a computer-readable medium comprising non-transitory machine-executable code that, when executed by a computer processor, performs a method comprising: a) accessing a data file comprising a received signal comprising encoded sequence information from at least one individual polynucleotide molecule, the received signal comprising noise and / or distortion; b) generating a message comprising sequence information for at least one individual polynucleotide molecule by decoding the received signal, the decoding reducing noise and / or distortion for each individual polynucleotide in the message; and c) writing the message comprising sequence information for at least one individual polynucleotide molecule to a computer file.

[0167] The disclosure also provides a computer-readable medium comprising non-transitory machine-executable code that, when executed by a computer processor, performs a method, the method comprising: a) accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; b) collapsing the set of sequencing reads to generate a set of consensus sequences, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides; and c) filtering out from among the consensus sequences those that do not meet a quality threshold.

[0168] The disclosure also provides a computer-readable medium comprising non-transitory machine-executable code that, when executed by a computer processor, performs a method, the method comprising: a) accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; and b) (i) grouping the sequence reads sequenced from the amplified progeny polynucleotides into families, each family amplified from the same tagged parent polynucleotides; and (ii) optionally collapsing the sequence reads by determining a quantitative measure of the sequence reads within each family.

[0169] In some embodiments, when executed by a computer processor, the executable code further performs the steps of: d) determining a quantitative measure of unique families; e) inferring a measure of tagged unique parent polynucleotides in the set based on (1) the quantitative measure of unique families and (2) the quantitative measure of sequence reads in each group.

[0170] In some embodiments, when executed by a computer processor, the executable code further performs the steps of e) determining a quantitative measure of polymorphic morphology between the families; and f) inferring a quantitative measure of polymorphic morphology in the number of inferred tagged unique parent polynucleotides based on the determined quantitative measure of polymorphic morphology.

[0171] In some embodiments, when executed by a computer processor, the executable code further performs the step of e) inferring copy number variation for the plurality of sets based on a comparison of the inferred numbers of tagged parent polynucleotides in each set that map to each of the plurality of reference sequences.

[0172] The disclosure also provides a computer readable medium comprising non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a) accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; b) grouping sequence reads sequenced from the amplified polynucleotides into families, each family amplified from the same first polynucleotide in the set; c) inferring a quantitative measure for the families in the set; and d) determining copy number variation by comparing the quantitative measures for the families in each set.

[0173] The disclosure also provides a computer-readable medium comprising non-transitory machine-executable code that, when executed by a computer processor, performs a method comprising: accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides, grouping the sequence reads into families, each family comprising sequence reads of amplified polynucleotides amplified from the same first polynucleotide; and, for each set of first polynucleotides, inferring a call frequency for one or more bases in the set of first polynucleotides, the inferring comprising: (i) for each family, assigning a confidence score for each of a plurality of calls, the confidence score taking into account the frequency of the call among members of the family; and (ii) estimating the frequency of the one or more calls taking into account the confidence scores of the one or more calls assigned to each family.

[0174] The disclosure also provides a composition comprising between 100 and 100,000 human haploid genome equivalents of cfDNA polynucleotides, wherein the polynucleotides are tagged with between 2 and 1,000,000 unique identifiers.

[0175] In some embodiments, the composition comprises 1000-50,000 haploid human genome equivalent cfDNA polynucleotides, wherein the polynucleotides are tagged with 2-1,000 unique identifiers. In some embodiments, the unique identifiers comprise nucleotide barcodes. The present disclosure also provides a method comprising: a) providing a sample comprising 100-100,000 haploid human genome equivalent cfDNA polynucleotides; and b) tagging the polynucleotides with 2-1,000,000 unique identifiers.

[0176] The disclosure provides a method for detecting polynucleotides comprising the steps of: a) providing a sample comprising fragmented polynucleotides of a plurality of human haploid genome equivalents; b) determining z, where z is a measure of central tendency (e.g., mean, median, or mode) of the expected number of duplicated polynucleotides starting at any position within the genome, where the duplicated polynucleotides have the same start and end positions; and c) tagging the polynucleotides in the sample with n unique identifiers, where n is a number between 2 and 100,000. * z, 2 to 10,000 * z, 2 to 1,000 * z or 2 to 100 * z. The present disclosure also provides a method comprising the steps of: a) providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b) generating a set of sequencing reads by generating a plurality of sequence reads for each of the tagged parent polynucleotides in the set; and c) generating a set of consensus sequences by collapsing the set of sequencing reads, wherein each consensus sequence corresponds to a unique polynucleotide among the set of tagged parent polynucleotides.

[0177] The present disclosure also provides a system comprising a computer readable medium comprising machine executable code as described herein.The present disclosure also provides a system comprising a computer readable medium comprising machine executable code that, when executed by a computer processor, performs a method as described herein.

[0178] Further aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, in which merely illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. In certain embodiments, for example, the following is provided: (Item 1) 1. A method for detecting copy number variation, the method comprising: a. sequencing extracellular polynucleotides from a bodily sample of a subject, each of the extracellular polynucleotides generating a plurality of sequencing reads; b. filtering out leads that do not meet a specified threshold; c. mapping the sequence reads obtained from step (a) to a reference sequence after filtering out the reads; d. Quantifying or enumerating the mapped reads in two or more predefined regions of the reference sequence; and e. i. normalizing the number of reads in said predefined regions relative to each other and / or the number of unique sequence reads in said predefined regions relative to each other; ii. comparing the normalized number obtained in step (i) with the normalized number obtained from a control sample. determining copy number variation in one or more of said predefined regions by A method comprising: (Item 2) 1. A method for detecting rare mutations in a cell-free or substantially cell-free sample obtained from a subject, the method comprising: a. sequencing extracellular polynucleotides from a bodily sample of a subject, each of the extracellular polynucleotides generating a plurality of sequencing reads; b. performing multiplex sequencing in the region or, if no enrichment is performed, performing whole genome sequencing; c. filtering out leads that do not meet a specified threshold; d. mapping sequence reads derived from said sequencing step onto a reference sequence; e. identifying a subset of mapped sequence reads that align with a variant of the reference sequence at each mappable base position; f. calculating, for each mappable base position, the ratio of (a) the number of mapped sequence reads that contain a variant relative to the reference sequence and (b) the total number of sequence reads for each mappable base position; g. normalizing the frequency of the ratio or variance for each mappable base position to determine potential rare variant(s) or mutation(s); and h. Comparing the numbers obtained for each of the regions containing potential rare variant(s) or mutation(s) with numbers similarly obtained from a reference sample. A method comprising: (Item 3) A method for characterizing heterogeneity of an abnormal condition in a subject, the method comprising generating a genetic profile of extracellular polynucleotides in the subject, the genetic profile comprising a plurality of data resulting from analysis of copy number variation and rare mutations. (Item 4) 4. The method of claim 1, 2 or 3, wherein the prevalence / concentration of each rare variant identified in the subject is reported and quantified simultaneously. (Item 5) 4. The method of claim 1, 2 or 3, wherein a confidence score regarding the prevalence / concentration of rare variants in said subject is reported. (Item 6) 4. The method of claim 1, 2 or 3, wherein the extracellular polynucleotide comprises DNA. (Item 7) 4. The method of claim 1, 2 or 3, wherein the extracellular polynucleotide comprises RNA. (Item 8) 4. The method of claim 1, 2 or 3, further comprising isolating extracellular polynucleotides from the body sample. (Item 9) 4. The method according to item 1, 2 or 3, wherein the isolating step comprises a method for isolation and extraction of circulating nucleic acid. (Item 10) 4. The method of claim 1, 2 or 3, further comprising the step of fragmenting the isolated extracellular polynucleotides. (Item 11) 9. The method of claim 8, wherein the body sample is selected from the group consisting of blood, plasma, serum, urine, saliva, mucosal discharge, sputum, stool and tears. (Item 12) 4. The method of claim 1, 2 or 3, further comprising determining the percentage of sequences in the body sample that have copy number variations or rare mutations or variants. (Item 13) 13. The method of claim 12, wherein the determining step comprises calculating the percentage of the predefined region having an amount of polynucleotide greater or less than a predetermined threshold. (Item 14) 4. The method of claim 1, 2 or 3, wherein the subject is suspected of having an abnormal condition. (Item 15) 15. The method according to item 14, wherein the abnormal condition is selected from the group consisting of mutations, rare mutations, indels, copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, gene fusions, chromosomal fusions, gene truncations, gene amplifications, gene duplications, chromosomal damage, DNA damage, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, abnormal changes in nucleic acid methylation, infections and cancers. (Item 16) 4. The method of claim 1, 2 or 3, wherein the subject is a pregnant woman. (Item 17) 3. The method of claim 1 or 2, wherein the copy number variation or rare mutation or genetic variant is indicative of a fetal abnormality. (Item 18) 18. The method according to item 17, wherein the fetal abnormality is selected from the group consisting of mutations, rare mutations, indels, copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, gene fusions, chromosomal fusions, gene truncations, gene amplifications, gene duplications, chromosomal damage, DNA damage, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, abnormal changes in nucleic acid methylation, infections and cancers. (Item 19) 4. The method of claim 1, 2 or 3, further comprising attaching one or more barcodes to the extracellular polynucleotides or fragments thereof prior to sequencing. (Item 20) 20. The method of claim 19, wherein each barcode attached to an extracellular polynucleotide or a fragment thereof prior to sequencing is unique. (Item 21) 20. The method of claim 19, wherein each barcode attached to an extracellular polynucleotide or a fragment thereof prior to sequencing is not unique. (Item 22) 4. The method of claim 1, 2 or 3, further comprising the step of selectively enriching regions from the genome or transcriptome of the subject prior to sequencing. (Item 23) 4. The method of claim 1, 2 or 3, further comprising the step of non-selectively enriching regions from the subject's genome or transcriptome prior to sequencing. (Item 24) 4. The method of claim 1, 2 or 3, further comprising attaching one or more barcodes to said extracellular polynucleotides or fragments thereof prior to any amplification or enrichment step. (Item 25) 20. The method of claim 19, wherein the barcode is a polynucleotide. (Item 26) 20. The method of claim 19, wherein the barcode comprises a random sequence. (Item 27) 20. The method of claim 19, wherein the barcode comprises a fixed or semi-random set of oligonucleotides that, in combination with the diversity of molecules sequenced from a selected region, allows for the identification of unique molecules. (Item 28) 20. The method of claim 19, wherein the barcode comprises an oligonucleotide that is at least 3, 5, 10, 15, 20, 25, 30, 35, 40, 45 or 50-mer base pairs in length. (Item 29) 4. The method of claim 1, 2 or 3, further comprising amplifying the extracellular polynucleotide or a fragment thereof. (Item 30) 30. The method of item 29, wherein the amplification comprises global amplification or whole genome amplification. (Item 31) 4. The method according to item 1, 2 or 3, wherein unique identity sequence reads are detected based on sequence information at the beginning (start) and end (stop) regions of the sequence read and the length of the sequence read. (Item 32) 32. The method of claim 31, wherein a unique identity sequence molecule is detected based on sequence information at the beginning (start) and end (stop) regions of the sequence read, the length of the sequence read, and the attachment of a barcode. (Item 33) 31. The method of claim 30, wherein the amplification comprises selective amplification. (Item 34) 34. The method of claim 33, wherein the amplification comprises non-selective amplification. (Item 35) 4. The method of claim 1, 2 or 3, wherein suppression amplification or enrichment by subtraction is performed. (Item 36) 4. The method of claim 1, 2 or 3, further comprising removing said subset of reads from further analysis prior to quantifying or enumerating the reads. (Item 37) 37. The method of claim 36, wherein the removing step comprises excluding reads having an accuracy score or quality score below a threshold, e.g., less than 90%, 99%, 99.9% or 99.99%, and / or a mapping score below a threshold, e.g., less than 90%, 99%, 99.9% or 99.99%. (Item 38) 4. The method of claim 1, 2 or 3, further comprising a step of filtering out reads having a quality score below a specified threshold. (Item 39) 2. The method of claim 1, wherein the predefined regions are of uniform or substantially uniform size. (Item 40) 40. The method of claim 39, wherein the predefined region is at least about 10 kb, 20 kb, 30 kb, 40 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90 kb or 100 kb in size. (Item 41) 4. The method of item 1, 2 or 3, wherein at least 50, 100, 200, 500, 1000, 2000, 5000, 10,000, 20,000 or 50,000 regions are analyzed. (Item 42) 4. The method of claim 1, 2 or 3, wherein the variant is present in a region of the genome selected from the group consisting of a gene fusion, a gene duplication, a gene deletion, a gene translocation, a microsatellite region, a gene fragment or a combination thereof. (Item 43) 4. The method of claim 1, 2 or 3, wherein the variant is present in a region of the genome selected from the group consisting of a gene, an oncogene, a tumor suppressor gene, a promoter, a regulatory sequence element or a combination thereof. (Item 44) 3. The method of claim 2, wherein the variant is a nucleotide variant of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15 or 20 nucleotides in length, a single base substitution, a small indel, a transversion, a translocation, an inversion, a deletion, a truncation or a gene truncation. (Item 45) 4. The method of claim 1, 2 or 3, further comprising a step of correcting / normalizing / adjusting the quantity of mapped reads using said barcodes or unique characteristics of individual reads. (Item 46) 3. The method of claim 1 or 2, wherein the enumerating of reads is performed by enumerating unique barcodes in each of the predefined regions and normalizing their counts across at least a subset of the sequenced predefined regions. (Item 47) 4. The method according to item 1, 2 or 3, wherein samples at subsequent time intervals from the same subject are analyzed and compared with the results of the previous sample. (Item 48) 46. ​​The method of claim 45, wherein the method further comprises amplifying the barcoded extracellular polynucleotide. (Item 49) 4. The method of claim 1, 2 or 3, further comprising determining the frequency of partial copy number variations, measuring loss of heterozygosity, performing gene expression analysis, performing epigenetic analysis, and / or performing hypermethylation analysis. (Item 50) A method comprising determining copy number variation or performing rare mutation analysis in a cell-free or substantially cell-free sample obtained from a subject using multiplex sequencing. (Item 51) 51. The method of claim 50, wherein the multiplex sequencing comprises performing more than 10,000 sequencing reactions. (Item 52) 51. The method of claim 50, wherein the multiplex sequencing comprises simultaneously sequencing at least 10,000 different reads. (Item 53) 51. The method of claim 50, wherein the multiplex sequencing comprises performing data analysis on at least 10,000 distinct reads across the genome. (Item 54) 3. The method of claim 1 or 2, wherein the normalizing and detecting are performed using one or more of the following methods: Hidden Markov, Dynamic Programming, Support Vector Machines, Bayesian or Probabilistic Modeling, Trellis Decoding, Viterbi Decoding, Expectation Maximization, Kalman Filtering or Neural Networks. (Item 55) 4. The method of item 1, 2 or 3, further comprising monitoring disease progression, monitoring residual disease, monitoring treatment, diagnosing a condition, prognosing a condition, or selecting a treatment based on the variant discovered for said subject. (Item 56) 56. The method of claim 55, wherein the treatment is modified based on the latest sample analysis. (Item 57) 4. The method according to item 1, 2 or 3, wherein the genetic profile of a tumor, infection or other tissue abnormality is inferred. (Item 58) 4. The method according to item 1, 2 or 3, wherein tumor growth, remission or progression, infection or other tissue abnormality is monitored. (Item 59) 4. The method according to item 1, 2 or 3, wherein sequences related to the subject's immune system are analyzed and monitored at a single instance or over time. (Item 60) The method of any one of items 1 to 3, wherein the identification of a variant is followed up through imaging studies (e.g., CT, PET-CT, MRI, X-ray, ultrasound) to identify the location of tissue abnormalities suspected to cause the identified variant. (Item 61) 4. The method of claim 1, 2 or 3, wherein said analysis further comprises the use of genetic data obtained from tissue or tumor biopsies from the same patient. (Item 62) 4. The method according to items 1, 2 or 3, wherein the phylogeny of a tumor, infection or other tissue abnormality is inferred. (Item 63) 3. The method of claim 1 or 2, wherein the method further comprises undercalling and identifying low confidence regions based on population. (Item 64) 3. The method according to item 1 or 2, wherein obtaining measurement data on sequence coverage comprises measuring the depth of sequence coverage at every position of the genome. (Item 65) Item 65. The method of item 64, wherein correcting the measured data for bias in the sequence coverage comprises calculating a window average coverage. (Item 66) Item 65. The method of item 64, wherein correcting for bias in the measured data for sequence coverage comprises making an adjustment to account for GC bias in the library construction and sequencing process. (Item 67) Item 65. The method of item 64, wherein correcting for bias in the measured data for the sequence coverage comprises making an adjustment based on additional weighting factors associated with each mapping to offset bias. (Item 68) 4. The method according to item 1, 2 or 3, wherein the extracellular polynucleotide is derived from a pathological cellular source. (Item 69) 4. The method of claim 1, 2 or 3, wherein the extracellular polynucleotide is derived from a healthy cell source. (Item 70) A system comprising a computer readable medium for performing the following steps: selecting a predefined region within a genome; enumerating the number of sequence reads within the predefined region; normalizing the number of sequence reads across the predefined region; and determining a percent of copy number variation within the predefined region. (Item 71) 71. The system of item 70, wherein the entire genome or at least 85% of the genome is analyzed. (Item 72) 71. The system of claim 70, wherein the computer readable medium provides an end user with data regarding the percent of cancer DNA or cancer RNA in plasma or serum. (Item 73) 2. The method of claim 1, wherein the identified copy number variation is a fraction (i.e., a non-integer level) due to heterogeneity in the sample. (Item 74) The method of claim 1, wherein enrichment of selected regions is performed. (Item 75) 2. The method according to item 1, wherein copy number variation information is simultaneously extracted based on the methods described in items 1, 64, 65, 66 and 67. (Item 76) 77. The method according to claim 1 or 2, which is used with a first step of disrupting polynucleotides to limit the initial copy number or diversity of starting polynucleotides in the sample. 1. A method for detecting rare mutations in a cell-free or substantially cell-free sample obtained from a subject, the method comprising: a. sequencing extracellular polynucleotides from a bodily sample of a subject, each of the extracellular polynucleotides generating a plurality of sequencing reads; b. filtering out leads that do not meet a specified quality threshold; c. mapping sequence reads derived from said sequencing step onto a reference sequence; d. identifying a subset of mapped sequence reads that align with a variant of the reference sequence at each mappable base position; e. calculating, for each mappable base position, the ratio of (a) the number of mapped sequence reads that contain a variant relative to the reference sequence and (b) the total number of sequence reads for each mappable base position; f. normalizing the frequency of the ratio or variance for each mappable base position to determine potential rare variant(s) or other genetic alteration(s); and g. Comparing the numbers obtained for each of the regions containing potential rare variant(s) or mutation(s) with numbers similarly obtained from a reference sample. A method comprising: (Item 78) a. providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b. generating a corresponding set of amplified progeny polynucleotides by amplifying the tagged parent polynucleotides in the set; c. sequencing a subset (including a suitable subset) of the set of amplified progeny polynucleotides to generate a set of sequencing reads; and d. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides. A method comprising: (Item 79) 79. The method of claim 78, wherein each polynucleotide in a set is mappable to a reference sequence. (Item 80) 80. The method of claim 78, comprising providing a plurality of sets of tagged parent polynucleotides, each set being mappable to a different mappable position in the reference sequence. (Item 81) e. The method of claim 78, further comprising analyzing the consensus sequence of the set for each set of tagged parent molecules, separately or in combination. (Item 82) 80. The method of claim 78, further comprising converting initial starting genetic material into said tagged parent polynucleotides. (Item 83) 83. The method of claim 82, wherein the initial genetic material comprises 100 ng or less of polynucleotide. (Item 84) 83. The method of claim 82, further comprising a step of disrupting the initial genetic material prior to the conversion. (Item 85) 83. The method of claim 82, comprising converting the initial starting genetic material into tagged parent polynucleotides with a conversion efficiency of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 80%, or at least 90%. (Item 86) 83. The method of claim 82, wherein the converting step comprises any of blunt end ligation, sticky end ligation, molecular inversion probes, PCR, ligation-based PCR, single-stranded ligation and single-stranded circularization. (Item 87) 83. The method of claim 82, wherein the initial genetic material is cell-free nucleic acid. (Item 88) 80. The method of claim 79, wherein a plurality of the sets map to different mappable positions within a reference sequence from the same genome. (Item 89) 80. The method of claim 78, wherein each of the tagged parent polynucleotides in the set is uniquely tagged. (Item 90) 80. The method of claim 78, wherein each set of parent polynucleotides is mappable to a position in a reference sequence, and the polynucleotides in each set are not uniquely tagged. (Item 91) 80. The method of claim 78, wherein the generation of a consensus sequence is based on information from the tags and / or at least one of: (i) sequence information in a beginning (start) region of the sequence read, (ii) an end (stop) region of the sequence read, and (iii) a length of the sequence read. (Item 92) 80. The method of claim 78, comprising sequencing a subset of the set of amplified progeny polynucleotides sufficient to generate sequence reads for at least one progeny of each of at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 98%, at least 99%, at least 99.9%, or at least 99.99% of the unique polynucleotides among the set of tagged parent polynucleotides. (Item 93) 93. The method of claim 92, wherein the at least one progeny is a plurality of progeny, e.g., at least two, at least five, or at least ten progeny. (Item 94) 80. The method of claim 78, wherein the number of sequence reads in the set of sequence reads is greater than the number of unique tagged parent polynucleotides in the set of tagged parent polynucleotides. (Item 95) 96. The method of claim 78, wherein the subset of amplified progeny polynucleotides of the sequenced set is of sufficient size such that any nucleotide sequence represented in the set of tagged parent polynucleotides has at least a 50%, at least a 60%, at least a 70%, at least a 80%, at least a 90%, at least a 95%, at least a 98%, at least a 99%, at least a 99.9%, or at least a 99.99% probability of being represented in the set of consensus sequences at a percentage equal to the percentage of the per base sequencing error rate of the sequencing platform used. 79. The method of claim 78, comprising enriching the set of amplified progeny polynucleotides for polynucleotides that map to one or more selected mappable positions in a reference sequence by: (i) selective amplification of sequences from starting initial genetic material that are converted into tagged parent polynucleotides; (ii) selective amplification of tagged parent polynucleotides; (iii) selective sequence capture of amplified progeny polynucleotides; or (iv) selective sequence capture of starting initial genetic material. (Item 97) 82. The method of claim 81, wherein the analyzing step comprises normalizing a measure (e.g., a number) generated from the set of consensus sequences to a measure generated from the set of consensus sequences from a control sample. (Item 98) 82. The method of item 81, wherein the analyzing step comprises detecting mutations, rare mutations, indels, copy number variations, transversions, translocations, inversions, deletions, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural changes, gene fusions, chromosomal fusions, gene truncations, gene amplifications, gene duplications, chromosomal damage, DNA damage, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns, abnormal changes in nucleic acid methylation, infections, or cancers. (Item 99) 79. The method of claim 78, wherein the polynucleotide comprises DNA, RNA, a combination of the two, or DNA+RNA-derived cDNA. (Item 100) 83. The method of claim 82, wherein a specific subset of polynucleotides is selected or enriched from the initial set of polynucleotides or the amplified polynucleotides based on polynucleotide length in base pairs. (Item 101) 83. The method of claim 82, wherein the analysis further includes detection and monitoring of abnormalities or diseases in an individual, such as infection and / or cancer. (Item 102) The method of claim 101, in combination with immune repertoire profiling. (Item 103) 80. The method of claim 78, wherein the polynucleotides are extracted from a sample selected from the group consisting of blood, plasma, serum, urine, saliva, mucosal discharge, sputum, stool and tears. (Item 104) 80. The method of claim 78, wherein the folding step comprises detecting and / or correcting errors, nicks or damage present in the sense or antisense strand of the tagged parent polynucleotides or amplified progeny polynucleotides. (Item 105) A method comprising detecting genetic variations in initial genetic material that are not uniquely tagged with a sensitivity of at least 5%, at least 1%, at least 0.5%, at least 0.1%, or at least 0.05%. (Item 106) 106. The method of claim 105, wherein the initial genetic material is provided in an amount of nucleic acid less than 100 ng, the genetic variation is a copy number variation / heterozygosity variation, and the detecting step is performed at sub-chromosomal resolution; for example, at least 100 megabase resolution, at least 10 megabase resolution, at least 1 megabase resolution, at least 100 kilobase resolution, at least 10 kilobase resolution or at least 1 kilobase resolution. (Item 107) 82. The method of claim 81, comprising providing a plurality of sets of tagged parent polynucleotides, each set being mappable to a different mappable position in the reference sequence. (Item 108) 108. The method of claim 107, wherein the mappable location in the reference sequence is a tumor marker locus, and analyzing comprises detecting the tumor marker within a consensus sequence of the set. (Item 109) 19. The method of claim 108, wherein the tumor marker is present in the set of consensus sequences at a frequency lower than the error rate introduced in the amplifying step. (Item 110) 108. The method of claim 107, wherein the at least one set is a plurality of sets, and the mappable locations of the reference sequence comprise a plurality of mappable locations in the reference sequence, each of which is a locus of a tumor marker. (Item 111) Item 108. The method of item 107, wherein the analyzing step comprises detecting copy number variations of consensus sequences between at least two sets of parent polynucleotides. (Item 112) 108. The method of claim 107, wherein the analyzing step comprises detecting the presence of sequence variations compared to the reference sequence. (Item 113) 108. The method of claim 107, wherein the analyzing step comprises detecting the presence of sequence variations compared to the reference sequence and detecting copy number variations of consensus sequences between at least two sets of parent polynucleotides. (Item 114) The folding process is i. grouping sequence reads sequenced from amplified progeny polynucleotides into families, each family amplified from the same tagged parent polynucleotide; and ii. determining a consensus sequence based on sequence reads within the family. 79. The method according to claim 78, comprising: (Item 115) The process is as follows: a. receiving at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b. generating a corresponding set of amplified progeny polynucleotides by amplifying the tagged parent polynucleotides in the set; c. sequencing a subset (including a suitable subset) of the set of amplified progeny polynucleotides to generate a set of sequencing reads; d. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides; and optionally e. analyzing the set of consensus sequences for each set of tagged parent molecules. A system comprising a computer-readable medium for performing the steps of: (Item 116) 1. A method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 10% of the individual's genome is sequenced. (Item 117) 1. A method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 20% of the individual's genome is sequenced. (Item 118) 1. A method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 30% of the individual's genome is sequenced. (Item 119) 1. A method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 40% of the individual's genome is sequenced. (Item 120) 1. A method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 50% of the individual's genome is sequenced. (Item 121) 1. A method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 60% of the individual's genome is sequenced. (Item 122) 1. A method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 70% of the individual's genome is sequenced. (Item 123) 1. A method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 80% of the individual's genome is sequenced. (Item 124) 1. A method comprising detecting the presence or absence of a genetic alteration or the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 90% of the individual's genome is sequenced. (Item 125) 1. A method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 10% of the individual's genome is sequenced. (Item 126) 1. A method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 20% of the individual's genome is sequenced. (Item 127) 1. A method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 30% of the individual's genome is sequenced. (Item 128) 1. A method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 40% of the individual's genome is sequenced. (Item 129) 1. A method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 50% of the individual's genome is sequenced. (Item 130) 1. A method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 60% of the individual's genome is sequenced. (Item 131) 1. A method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 70% of the individual's genome is sequenced. (Item 132) 1. A method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 80% of the individual's genome is sequenced. (Item 133) 1. A method comprising detecting the presence or absence of a genetic alteration and the amount of genetic variation in an individual, wherein the detecting step is performed with the aid of cell-free nucleic acid sequencing, and wherein at least 90% of the individual's genome is sequenced. (Item 134) The method according to items 116 to 133, wherein the genetic alteration is a copy number variation or one or more rare mutations. (Item 135) The method according to items 116 to 133, wherein the genetic variations comprise one or more causative variants and one or more polymorphisms. (Item 136) The method according to items 116 to 133, wherein the amount of genetic alterations and / or genetic variations in the individual can be compared with the amount of genetic alterations and / or genetic variations in one or more individuals with a known disease. (Item 137) The method according to any one of items 116 to 133, wherein the amount of genetic alteration and / or genetic variation in the individual can be compared with the amount of genetic alteration and / or genetic variation in one or more individuals without the disease. (Item 138) 134. The method according to any one of claims 116 to 133, wherein the cell-free nucleic acid is DNA. (Item 139) 134. The method according to items 116 to 133, wherein the cell-free nucleic acid is RNA. (Item 140) The method according to items 116 to 133, wherein the cell-free nucleic acid is DNA or RNA. (Item 141) 137. The method of claim 136, wherein the disease is cancer or a precancerous condition. (Item 142) The method according to items 116 to 133, wherein the method further comprises diagnosis or treatment of a disease. (Item 143) a. providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b. generating a corresponding set of amplified progeny polynucleotides by amplifying the tagged parent polynucleotides in the set; c. sequencing a subset (including a suitable subset) of the set of amplified progeny polynucleotides to generate a set of sequencing reads; d. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides; and e. removing from among the consensus sequences those that do not meet a quality threshold. A method comprising: (Item 144) 144. The method of claim 143, wherein the quality threshold takes into account the number of sequence reads from amplified progeny polynucleotides that collapse into a consensus sequence. (Item 145) 144. The method of claim 143, wherein the quality threshold takes into account the number of sequence reads from amplified progeny polynucleotides that collapse into a consensus sequence. (Item 146) A system comprising a computer-readable medium for performing the method according to any one of items 143 to 145. (Item 147) a. Providing at least one set of tagged parent polynucleotides and for each set of tagged parent polynucleotides, each set located at a different mappable location in a reference sequence in one or more genomes; i. generating a set of amplified polynucleotides by amplifying a first polynucleotide; ii. sequencing a subset of the set of amplified polynucleotides to generate a set of sequencing reads; iii. 1. Collapsing sequence reads sequenced from amplified progeny polynucleotides by grouping the sequence reads into families, each family being amplified from the same tagged parent polynucleotide; method. (Item 148) The folding process is 2. Determine a quantitative measure of sequence reads within each family Item 148. The method of item 147, further comprising: (Item 149) b. determining a quantitative measure of unique families; and c. inferring a measure of tagged unique parent polynucleotides within the set based on (1) the quantitative measure of unique families and (2) the quantitative measure of sequence reads within each group. Item 149. The method of item 148, further comprising: (Item 150) Item 149. The method of item 149, wherein the inferring step is performed using a statistical or probabilistic model. (Item 151) Item 150. The method of item 149, wherein at least one of the sets is a plurality of sets. (Item 152) Item 152. The method of item 151, further comprising the step of correcting for amplification or representation bias between the two sets. (Item 153) 153. The method of claim 152, further comprising the step of correcting for amplification or representation bias between the two sets by using a control or set of control samples. (Item 154) 152. The method of claim 151, further comprising determining copy number variation between the sets. (Item 155) d. determining a quantitative measure of the polymorphic patterns among said families; and e. inferring a quantitative measure of the polymorphic form in the number of inferred tagged unique parent polynucleotides based on the determined quantitative measure of the polymorphic form. Item 149. The method of item 149, further comprising: (Item 156) The method of claim 155, wherein the polymorphic form includes, but is not limited to, a substitution, an insertion, a deletion, an inversion, a microsatellite alteration, a transversion, a translocation, a fusion, a methylation, a hypermethylation, a hydroxymethylation, an acetylation, an epigenetic variant, a regulatory associated variant, or a protein binding site. (Item 157) The set is derived from a common sample, and the method comprises: d. inferring copy number variation for a plurality of said sets based on a comparison of the inferred number of tagged parent polynucleotides in each set that map to each of a plurality of mappable positions in the reference sequence. Item 149. The method of item 149, further comprising: (Item 158) 158. The method of claim 157, wherein the original number of polynucleotides in each set is further inferred. (Item 159) 148. The method of claim 147, wherein at least a subset of the tagged parent polynucleotides within each set is not uniquely tagged. (Item 160) A system comprising a computer-readable medium comprising machine-executable code that, when executed by a computer processor, performs the method according to any one of items 147 to 158. (Item 161) 1. A method for determining copy number variation in a sample comprising polynucleotides, the method comprising the steps of: a. providing at least two sets of first polynucleotides, each set mapping to a different mappable location in a reference sequence within a genome, and for each set of first polynucleotides; i. amplifying the polynucleotides to generate a set of amplified polynucleotides; ii. sequencing a subset of the set of amplified polynucleotides to generate a set of sequencing reads; iii. grouping sequence reads sequenced from the amplified polynucleotides into families, each family being amplified from the same first polynucleotide in the set; iv. Inferring a quantitative measure of the families in the set; process; and b. determining copy number variation by comparing the quantitative measures of the families within each set. A method comprising: (Item 162) A system comprising a computer-readable medium containing machine-executable code which, when executed by a computer processor, performs the method of item 161. (Item 163) 1. A method for inferring a frequency of a sequence call in a sample of polynucleotides, the method comprising the steps of: a. providing at least one set of first polynucleotides, each set mapping to a different mappable location in a reference sequence in one or more genomes, and for each set of first polynucleotides; i. amplifying the first polynucleotide to generate a set of amplified polynucleotides; ii. sequencing a subset of the set of amplified polynucleotides to generate a set of sequencing reads; iii. grouping the sequence reads into families, each family comprising sequence reads of amplified polynucleotides amplified from the same first polynucleotide; process; b. for each set of first polynucleotides, inferring a call frequency for one or more bases in the set of first polynucleotides, the inferring comprising: i. for each family, assigning a confidence score to each of a plurality of calls, the confidence score taking into account the frequency of the call among members of the family; and ii. estimating the frequency of one or more of said calls taking into account said confidence scores of one or more of said calls assigned to each family. Process including A method comprising: (Item 164) A system comprising a computer-readable medium containing machine-executable code which, when executed by a computer processor, performs the method of item 163. (Item 165) 1. A method for communicating sequence information regarding at least one individual polynucleotide molecule, the method comprising: a. providing at least one individual polynucleotide molecule; b. generating a signal by encoding sequence information in said at least one individual polynucleotide molecule; c. passing at least a portion of said signal through a channel to generate a received signal comprising nucleotide sequence information for said at least one individual polynucleotide molecule, said received signal comprising noise and / or distortion; d. generating a message containing sequence information for the at least one individual polynucleotide molecule by decoding the received signal, the decoding reducing noise and / or distortion for each individual polynucleotide in the message; and e. providing said message comprising sequence information for said at least one individual polynucleotide molecule to a recipient. A method comprising: (Item 166) 166. The method of claim 165, wherein the noise comprises an incorrect nucleotide call. (Item 167) 166. The method of claim 165, wherein the distortion comprises uneven amplification of the individual polynucleotide molecules relative to other individual polynucleotide molecules. (Item 168) 168. The method of claim 167, wherein the distortion is due to amplification bias or sequencing bias. (Item 169) 166. The method of claim 165, wherein the at least one individual polynucleotide molecule is a plurality of individual polynucleotide molecules, and the decoding step generates a message for each molecule in the plurality. (Item 170) 166. The method of claim 165, wherein the encoding step comprises amplifying the at least one individual polynucleotide molecule, optionally tagged, and the signal comprises a collection of amplified molecules. (Item 171) 166. The method of claim 165, wherein the channel comprises a polynucleotide sequence analyzer and the received signal comprises a plurality of polynucleotide sequence reads amplified from the at least one individual polynucleotide molecule. (Item 172) 166. The method of claim 165, wherein the decoding step comprises grouping sequence reads of amplified molecules amplified from each of the at least one individual polynucleotide molecule. (Item 173) Item 169. The method of item 169, wherein the decoding step comprises a probabilistic or statistical method of sorting the generated sequence signals. (Item 174) A system comprising a computer-readable medium containing machine-executable code that, when executed by a computer processor, performs the method according to any one of items 165 to 173. (Item 175) 162. The method according to any one of items 143 to 145, 147 to 159 and 161, wherein the polynucleotide is derived from tumor genomic DNA or RNA. (Item 176) 176. The method according to any of items 143 to 175, wherein the polynucleotide is derived from a cell-free polynucleotide, an exosomal polynucleotide, a bacterial polynucleotide or a viral polynucleotide. (Item 177) 176. The method of any of items 1 to 3 or 143 to 175, further comprising detection and / or correlation of affected molecular pathways. (Item 178) 176. The method of any of items 1-3 or 143-175, further comprising continuous monitoring of the health or disease state of the individual. (Item 179) 176. The method of any of items 1-3 or 143-175, wherein a phylogeny of genomes associated with a disease within an individual is inferred. (Item 180) 176. The method of any of items 1-3 or 143-175, further comprising diagnosing, monitoring or treating a disease. (Item 181) Item 180, the treatment regimen is selected or modified based on the detected polymorphic form or CNV or associated pathway. (Item 182) Item 180 or 181, wherein the treatment comprises a combination therapy. (Item 183) Item 179, wherein the diagnosis further comprises the step of identifying the location of the disease using radiography, e.g., CT-Scan, PET-CT, MRI, ultrasound, ultrasound using microbubbles, and the like. (Item 184) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: selecting a predefined region within the genome; accessing sequence reads and enumerating the number of sequence reads in the predefined region; normalizing the number of sequence reads across the predefined region; and determining the percent of copy number variation in said predefined region. 13. A computer readable medium comprising: (Item 185) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file comprising a plurality of sequencing reads; b. filtering out leads that do not meet a specified threshold; c. mapping sequence reads derived from the sequencing onto a reference sequence; d. identifying a subset of mapped sequence reads that align with a variant of the reference sequence at each mappable base position; e. calculating, for each mappable base position, the ratio of (a) the number of mapped sequence reads that contain a variant relative to the reference sequence and (b) the total number of sequence reads for each mappable base position; f. normalizing the frequency of the ratio or variance for each mappable base position to determine potential rare variant(s) or other genetic alteration(s); and g. Comparing the numbers obtained for each of the regions containing potential rare variant(s) or mutation(s) with numbers similarly obtained from a reference sample. 13. A computer readable medium comprising: (Item 186) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; and b. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides. 13. A computer readable medium comprising: (Item 187) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; b. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides; and c. removing from among the consensus sequences those that do not meet a quality threshold. 13. A computer readable medium comprising: (Item 188) A computer readable medium comprising non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; and i. 1. Grouping sequence reads sequenced from amplified progeny polynucleotides into families, each family being amplified from the same tagged parent polynucleotide, and optionally: 2. Determine a quantitative measure of sequence reads within each family collapsing the sequence reads by 13. A computer readable medium comprising: (Item 189) When executed by a computer processor, the executable code: b. determining a quantitative measure of unique families; c. inferring a measure of tagged unique parent polynucleotides within the set based on (1) the quantitative measure of unique families and (2) the quantitative measure of sequence reads within each group. Item 189. The computer-readable medium of item 188, further comprising: (Item 190) When executed by a computer processor, the executable code: d. determining a quantitative measure of the polymorphic patterns among said families; and e. inferring a quantitative measure of the polymorphic form in the number of inferred tagged unique parent polynucleotides based on the determined quantitative measure of the polymorphic form. Item 189. The computer-readable medium of item 189, further comprising: (Item 191) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides, and grouping sequence reads sequenced from the amplified polynucleotides into families, each family amplified from the same first polynucleotide in the set; b. inferring a quantitative measure of the families in the set; c. Determining copy number variation by comparing the quantitative measures of families within each set. 13. A computer readable medium comprising: (Item 192) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides, and grouping the sequence reads into families, each family comprising sequence reads of amplified polynucleotides amplified from the same first polynucleotide; b. for each set of first polynucleotides, inferring a call frequency for one or more bases in the set of first polynucleotides, the inferring comprising: c. for each family, assigning a confidence score to each of a plurality of calls, the confidence score taking into account the frequency of the call among members of the family; and d. estimating the frequency of one or more of said calls taking into account said confidence scores of one or more of said calls assigned to each family. Process including 13. A computer readable medium comprising: (Item 193) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file containing a received signal comprising encoded sequence information from at least one individual polynucleotide molecule, the received signal comprising noise and / or distortion; b. generating a message containing sequence information for the at least one individual polynucleotide molecule by decoding the received signal, the decoding reducing noise and / or distortion for each individual polynucleotide in the message; and c. writing said message containing sequence information for said at least one individual polynucleotide molecule into a computer file. 13. A computer readable medium comprising: (Item 194) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; b. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides; and c. removing from among the consensus sequences those that do not meet a quality threshold. 13. A computer readable medium comprising: (Item 195) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; and b. i. grouping sequence reads sequenced from amplified progeny polynucleotides into families, each family being amplified from the same tagged parent polynucleotide; and ii. optionally, determining a quantitative measure of the sequence reads within each family. collapsing the sequence reads by 13. A computer readable medium comprising: (Item 196) When executed by a computer processor, the executable code: c. determining a quantitative measure of unique families; d. Inferring a measure of tagged unique parent polynucleotides within the set based on (1) the quantitative measure of unique families and (2) the quantitative measure of sequence reads within each group. Item 196. The computer-readable medium of Item 195, further comprising: (Item 197) When executed by a computer processor, the executable code: e. determining a quantitative measure of the polymorphic patterns among said families; and f. inferring a quantitative measure of the polymorphic form in the number of inferred tagged unique parent polynucleotides based on the determined quantitative measure of the polymorphic form. 201. The computer-readable medium of item 196, further comprising: (Item 198) When executed by a computer processor, the executable code: e. Inferring copy number variation for the plurality of sets based on a comparison of the inferred numbers of tagged parent polynucleotides in each set that map to each of the plurality of reference sequences. 201. The computer-readable medium of item 196, further comprising: (Item 199) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides; b. grouping sequence reads sequenced from the amplified polynucleotides into families, each family being amplified from the same first polynucleotide in the set; c. inferring a quantitative measure of the families in the set; and d. Determining copy number variation by comparing the quantitative measures of families within each set. 13. A computer readable medium comprising: (Item 200) A computer readable medium containing non-transitory machine executable code that, when executed by a computer processor, performs a method, the method comprising: a. accessing a data file comprising a plurality of sequencing reads, the sequence reads being derived from a set of progeny polynucleotides amplified from at least one set of tagged parent polynucleotides, and grouping the sequence reads into families, each family comprising sequence reads of amplified polynucleotides amplified from the same first polynucleotide; and b. for each set of first polynucleotides, inferring a call frequency for one or more bases in the set of first polynucleotides, the inferring comprising: i. for each family, assigning a confidence score to each of a plurality of calls, the confidence score taking into account the frequency of the call among members of the family; and ii. estimating the frequency of one or more of said calls taking into account said confidence scores of one or more of said calls assigned to each family. Process including 13. A computer readable medium comprising: (Item 201) A composition comprising between 100 and 100,000 human haploid genome equivalents of cfDNA polynucleotides, wherein the polynucleotides are tagged with between 2 and 1,000,000 unique identifiers. (Item 202) 202. The composition of claim 201, comprising 1,000 to 50,000 haploid human genome equivalent cfDNA polynucleotides, wherein the polynucleotides are tagged with 2 to 1,000 unique identifiers. (Item 203) 204. The composition of claim 201, wherein the unique identifier comprises a nucleotide barcode. a. providing a sample comprising between 100 and 100,000 haploid human genome equivalent cfDNA polynucleotides; and b. tagging the polynucleotides with between 2 and 1,000,000 unique identifiers. A method comprising: (Item 205) a. providing a sample comprising a plurality of human haploid genome equivalent fragmented polynucleotides; determining bz, where z is a measure of central tendency (e.g., mean, median, or mode) of the expected number of duplicated polynucleotides starting at any position in the genome, the duplicated polynucleotides having the same start and end positions; and c. tagging the polynucleotides in the sample with n unique identifiers, where n is between 2 and 100,000. * z, 2 to 10,000 * z, 2 to 1,000 * z or 2 to 100 * z, the process A method comprising: (Item 206) a. providing at least one set of tagged parent polynucleotides, and for each set of tagged parent polynucleotides; b. generating a set of sequencing reads by generating a plurality of sequence reads for each of the tagged parent polynucleotides in the set; and c. generating a set of consensus sequences by collapsing the set of sequencing reads, each consensus sequence corresponding to a unique polynucleotide among the set of tagged parent polynucleotides. A method comprising: Incorporation by Reference

[0179] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. [Brief description of the drawings]

[0180] The novel features of the disclosed system and method are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosed system and method are utilized, and the accompanying drawings, in which:

[0181] [Figure 1] FIG. 1 is a flow chart diagram of a method for detecting copy number variation using a single sample.

[0182] [Diagram 2] FIG. 2 is a flow chart diagram of a method for detecting copy number variation using paired samples.

[0183] [Diagram 3] FIG. 3 is a flow chart diagram of a method for detecting rare mutations (eg, single nucleotide variants).

[0184] [Figure 4A] FIG. 4A is a graphical copy number variation detection report generated from a normal non-cancerous subject.

[0185] [Figure 4B] FIG. 4B is a graphical copy number variation detection report generated from a subject with prostate cancer.

[0186] [Figure 4C] FIG. 4C is a schematic diagram of internet-enabled access to reports generated from copy number variation analysis of subjects with prostate cancer.

[0187] [Figure 5A] FIG. 5A is a graphical copy number variation detection report generated from a subject in remission of prostate cancer.

[0188] [Figure 5B] FIG. 5B is a graphical copy number variation detection report generated from a subject with recurrent prostate cancer.

[0189] [Figure 6A] Figure 6A shows graphical detection reports (e.g., for single nucleotide variants) generated from various mixing experiments using DNA samples containing both wild-type and mutant copies of MET and TP53.

[0190] [Figure 6B] Figure 6B is a logarithmic plot of detection results (e.g., single nucleotide variants). Actual vs. expected percent cancer measurements are shown for various mixing experiments using DNA samples containing both wild-type and mutant copies of MET, HRAS, and TP53.

[0191] [Figure 7A] FIG. 7A is a graphical report of the percentage of two (eg, single nucleotide variants) in two genes, PIK3CA and TP53, in subjects with prostate cancer compared to the reference (control).

[0192] [Figure 7B] FIG. 7B is a schematic diagram of internet-enabled access to reports generated from (eg, single nucleotide variant) analysis of subjects with prostate cancer.

[0193] [Figure 8] FIG. 8 is a flow chart diagram of a method for analyzing genetic material.

[0194] [Figure 9] FIG. 9 is a flowchart diagram of a method for decoding information in a set of sequence reads that produces a representation of the information in a set of tagged parent polynucleotides with less noise and / or distortion.

[0195] [Figure 10] FIG. 10 is a flow chart diagram of a method for reducing distortion in determining CNVs from a set of sequence reads.

[0196] [Figure 11] FIG. 11 is a flow chart diagram of a method for estimating the frequency of a base or sequence of bases at a locus in a tagged parent polynucleotide population from a set of sequence reads.

[0197] [Figure 12] FIG. 12 illustrates a method for communicating sequence information.

[0198] [Figure 13]Figure 13 shows the minor allele frequencies detected across the 70 kb panel in 0.3% LNCaP cfDNA titrations using standard and digital sequencing workflows. Standard "analog" sequencing (Figure 13A) masks all true positive rare variants in a very large amount of noise due to PCR and sequencing errors, despite Q30 filtering. Digital sequencing (Figure 13B) removes all PCR and sequencing noise, revealing true mutations without false positives: green circles are SNP points in normal cfDNA, red circles are detected LNCaP mutations.

[0199] [Figure 14] Figure 14: Shows titration of LNCap cfDNA.

[0200] [Figure 15] FIG. 15 illustrates a computer system that is programmed or otherwise configured to carry out various methods of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0201] Detailed Description of the Invention I. Overall Overview The present disclosure provides a system and method for detecting rare mutations (e.g., single or multiple nucleotide mutations) and copy number variation in cell-free polynucleotides.Generally, the system and method include sample preparation or extraction and isolation of cell-free polynucleotide sequences from body fluid; followed by sequencing of cell-free polynucleotides by methods known in the art; and application of bioinformatics tools to detect rare mutations and copy number variation by comparison with reference.The system and method can also include a database or collection of various rare mutations or copy number variation profiles of various diseases, which are used as additional references when assisting in the detection of rare mutations (e.g., single nucleotide mutation profiling), copy number variation profiling or general genetic profiling of disease.

[0202] The present system and method can be particularly useful in the analysis of cell-free DNA. In some cases, cell-free DNA is extracted and isolated from easily accessible bodily fluids such as blood. For example, cell-free DNA can be extracted using a variety of methods known in the art, including but not limited to isopropanol precipitation and / or silica-based purification. Cell-free DNA can be extracted from any number of subjects (e.g., subjects that do not have cancer, subjects that are at risk of cancer, or subjects that are known to have cancer (e.g., through other means)).

[0203] After the isolation / extraction step, any of several different sequencing operations can be performed on the cell-free polynucleotide sample. The sample can be treated with one or more reagents (e.g., enzymes, unique identifiers (e.g., barcodes), probes, etc.) before sequencing. In some cases, if the sample is treated with a unique identifier such as a barcode, the sample or fragments of the sample can be tagged with a unique identifier, either individually or as subgroups. The tagged sample can then be used in downstream applications such as sequencing reactions that can locate the parent molecules of individual molecules.

[0204] After cell-free polynucleotide sequence sequencing data is collected, one or more bioinformatics processes can be applied to the sequence data to detect genetic features or abnormalities, such as copy number variation, rare mutations (e.g., single or multiple nucleotide variations), or changes in epigenetic markers, including but not limited to methylation profiles. If copy number variation analysis is desired, the sequence data can be 1) aligned to a reference genome; 2) sorted and mapped; 3) divided into windows or bins of sequences; 4) coverage reads can be counted for each window; 5) coverage reads can then be normalized using probabilistic or statistical modeling algorithms; and 6) an output file can be generated that reflects distinct copy number states at various locations within the genome. In other cases where analysis of rare mutations is desired, sequence data may be 1) aligned to a reference genome; 2) filtered and mapped; 3) the frequency of variant bases may be calculated based on the coverage reads for that particular base; 4) the frequency of variant bases may be normalized using a probabilistic, statistical or probabilistic modeling algorithm; and 5) an output file may be generated that reflects the mutational status at various locations within the genome.

[0205] A variety of different reactions and / or operations can be carried out in the system and method disclosed herein, including but not limited to nucleic acid sequencing, nucleic acid quantification, sequencing optimization, gene expression detection, gene expression quantification, genomic profiling, cancer profiling or analysis of expressed markers.In addition, the system and method have numerous medical applications.For example, it can be used for identification, detection, diagnosis, treatment, stage classification or risk prediction of various genetic and non-genetic diseases and disorders, including cancer.It can be used to evaluate the response of subjects to various treatments of said genetic and non-genetic diseases, or to provide information on disease progression and prognosis.

[0206] Polynucleotide sequencing can be compared to a communication theory problem. The initial individual polynucleotide or collection of polynucleotides can be considered as the original message. Tagging and / or amplification can be considered as encoding the original message into a signal. Sequencing can be considered as a communication channel. The output of the sequence analyzer, e.g., sequence reads, can be considered as the received signal. Bioinformatics processing can be considered as a receiver that generates the transmitted message, e.g., nucleotide sequence, by decoding the received signal. The received signal can include artifacts such as noise and distortion. Noise can be considered as an unwanted random addition to a signal. Distortion can be considered as a change in the amplitude of a signal or a portion of a signal.

[0207] Noise can be introduced by errors in replicating and / or decoding a polynucleotide. For example, in a sequencing process, a single polynucleotide may first be subjected to amplification. Amplification may introduce errors such that a subset of the amplified polynucleotides may contain a base at a particular locus that is not the same as the original base at that locus. Also, in the decoding process, a base at any particular locus may be decoded incorrectly. As a result, a collection of sequence reads may contain a certain percentage of base calls at a locus that are not the same as the original base. In typical sequencing techniques, this error rate may be in the single digits, e.g., 2%-3%. When a collection of molecules that are all presumed to have the same sequence are sequenced, this noise is small enough that the original base can be identified with a high degree of confidence.

[0208] However, noise can be a significant problem when parent polynucleotide collection contains a subset of polynucleotides with sequence variants at specific loci.This can be the case, for example, when cell-free DNA contains not only germline DNA but also DNA from another source (e.g., fetal DNA or DNA from cancer cells).In this case, if the frequency of molecules with sequence variants is within the same range as the frequency of errors introduced by sequencing process, true sequence variants may not be distinguishable from noise.This can, for example, interfere with the detection of sequence variants in samples.

[0209] Distortion can appear in the sequencing process as the difference in signal intensity, e.g., the total number of sequence reads, that are generated by molecules in the parent population at the same frequency.Distortion can be introduced, for example, by amplification bias, GC bias, or sequencing bias.This can interfere with the detection of copy number polymorphism in a sample.GC bias results in uneven representation of GC-rich or low-GC-rich regions in sequence reading.

[0210] The present invention provides a method for reducing sequencing artifacts (e.g., noise and / or distortion) in the polynucleotide sequencing process. By grouping sequence reads derived from the original individual molecules into families, noise and / or distortion can be reduced from a single individual molecule or a collection of molecules. For single molecules, grouping reads into families reduces distortion, for example, by pointing out that many sequence reads are actually a single molecule rather than many different molecules. Collapsing sequence reads into a consensus sequence is one way to reduce noise in the received message from one molecule. Using a probabilistic function that transforms the received frequency is another. For collections of molecules, grouping reads into families and determining quantitative measures of those families reduces distortion, for example, in the number of molecules at each of multiple different loci. Collapsing sequence reads of various families into a consensus sequence also eliminates errors introduced by amplification and / or sequencing errors. Determining the frequency of base calls based on probabilities derived from family information also reduces noise in the received message from collections of molecules.

[0211] Methods for reducing noise and / or distortion from the sequencing process are known. These include, for example, filtering sequences, e.g., requiring sequences to meet a quality threshold, or reducing GC bias. Such methods are typically performed on a collection of sequence reads that are the output of a sequence analyzer, and can be performed on a sequence read-by-sequence read basis, regardless of family structure (subcollection of sequences derived from a single original parent molecule). Certain methods of the present invention reduce noise and distortion by reducing noise and / or distortion within a family of sequence reads, i.e., by operating on sequence reads grouped into families derived from a single parent polynucleotide molecule. Reducing signal artifacts at the family level can result in significantly less noise and distortion in the final message provided than reducing artifacts performed at the sequence read-by-sequence level or on the output of the sequence analyzer as a whole.

[0212] The present disclosure further provides a method and system for sensitive detection of genetic variations in an initial sample of genetic material. The method includes using one or both of the following tools: first, efficient conversion of individual polynucleotides in the initial sample of genetic material into tagged parent polynucleotides ready to be sequenced to increase the probability that the individual polynucleotides in the initial sample of genetic material are represented in the ready-to-sequence sample. This allows sequence information for more polynucleotides to be generated in the initial sample. Second, high-yield generation of consensus sequences for tagged parent polynucleotides by rapid sampling of progeny polynucleotides amplified from the tagged parent polynucleotides, and folding of the generated sequence reads into consensus sequences representative of the sequences of the tagged parent polynucleotides. This can reduce noise introduced by amplification bias and / or sequencing errors and increase the sensitivity of detection. The folding is performed on multiple sequence reads generated from the amplified molecular reads or multiple reads of a single molecule.

[0213] Sequencing methods typically include sample preparation, sequencing polynucleotides in the prepared sample to obtain sequence reads, and bioinformatics manipulation of those sequence reads to obtain quantitative and / or qualitative genetic information about the sample. Sample preparation typically includes converting the polynucleotides in the sample into a form compatible with the sequencing platform used. This conversion may include tagging the polynucleotides. In certain embodiments of the invention, the tags include polynucleotide sequence tags. The conversion methods used in sequencing may not be 100% efficient. For example, it is not uncommon to convert the polynucleotides in a sample with a conversion efficiency of about 1-5%, i.e., about 1-5% of the polynucleotides in a sample are converted into tagged polynucleotides. Polynucleotides that are not converted into tagged molecules will not be represented in the tagged library for sequencing. Thus, polynucleotides with genetic variants that are represented at low frequency in the initial genetic material may not be represented in the tagged library and therefore may not be sequenced or detected. By increasing conversion efficiency, the probability that rare polynucleotides in the original genetic material will be represented in the tagged library and thus detected by sequencing is increased.Also, most protocols to date require more than 1 microgram of DNA as input material, rather than directly addressing the problem of low conversion efficiency of library preparation.However, when input sample material is limited or when detection of low-representation polynucleotides is desired, high conversion efficiency allows the sample to be efficiently sequenced and / or such polynucleotides to be properly detected.

[0214] The present disclosure provides a method for converting an initial polynucleotide into a tagged polynucleotide with a conversion efficiency of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 80% or at least 90%. The method includes, for example, using any of blunt-end ligation, sticky-end ligation, molecular inversion probes, PCR, ligation-based PCR, multiplex PCR, single-stranded ligation and single-stranded circularization. The method may also include a step of limiting the amount of initial genetic material. For example, the amount of initial genetic material may be less than 1 μg, less than 100 ng or less than 10 ng. These methods are described in more detail herein.

[0215] Obtaining accurate quantitative and qualitative information about the polynucleotides in the tagged library can result in a more sensitive characterization of the original genetic material. Typically, the polynucleotides in the tagged library are amplified and the resulting amplified molecules are sequenced. Depending on the throughput of the sequencing platform used, only a subset of the molecules in the amplified library will generate sequence reads. Thus, for example, the number of amplified molecules sampled for sequencing may be only about 50% of the unique polynucleotides in the tagged library. Also, amplification may be biased in favor of or against certain sequences or certain members of the tagged library. This may distort the quantitative measurement of sequences in the tagged library. Also, the sequencing platform may introduce errors when sequencing. For example, sequences may have an error rate per base of 0.5-1%. Amplification bias and sequencing errors introduce noise into the final sequencing product. This noise may reduce the detection sensitivity. For example, sequence variants whose frequency in the tagged population is lower than the sequencing error rate may be mistaken for noise. Amplification bias can also distort the actual measurement of copy number variation by providing sequence reads in greater or lesser amounts than the actual number in the population. Alternatively, multiple sequence reads from a single polynucleotide can be generated without amplification. This can be done, for example, using nanopore methods.

[0216] The present disclosure provides a method for accurately detecting and deciphering unique polynucleotides in tagged pools.In certain embodiments, the present disclosure provides sequence-tagged polynucleotides that, when amplified and sequenced, or sequenced multiple times to generate multiple sequence reads, provide information that allows progeny polynucleotides to be traced back or folded to the parent polynucleotide molecule of the unique tag.Folding a family of amplified progeny polynucleotides reduces amplification bias by providing information about the original unique parent molecule.Folding also reduces sequencing errors by eliminating mutant sequences of progeny molecules from sequencing data.

[0217] The detection and reading of unique polynucleotides in tagged libraries can involve two strategies. In one strategy, a sufficiently large subset of the amplified progeny polynucleotide pool is sequenced so that the tagged unique parent polynucleotides in the set of tagged parent polynucleotides are a high percentage, so that there is a sequence read generated for at least one amplified progeny polynucleotide in the family generated from the tagged unique parent polynucleotide. In the second strategy, the amplified progeny polynucleotide set is sampled for sequencing at a level that generates sequence reads from multiple progeny members of the family derived from the unique parent polynucleotide. The generation of sequence reads from multiple progeny members of a family allows sequences to be folded into a consensus parent sequence.

[0218] Thus, for example, by sampling a number of amplified progeny polynucleotides from a set of amplified progeny polynucleotides equal to the number of unique tagged parent polynucleotides in the set of tagged parent polynucleotides (particularly when the number is at least 10,000), statistically, sequence reads will be generated for at least one progeny of about 68% of the tagged parent polynucleotides in the set, and about 40% of the tagged unique parent polynucleotides in the original set may be represented by at least two progeny sequence reads. In certain embodiments, the amplified progeny polynucleotide set is sampled sufficiently such that an average of 5-10 sequence reads are generated for each family. Sampling from an amplified progeny set of 10 times more molecules than the number of unique tagged parent polynucleotides may statistically generate sequence information for 99.995% of the families (99.95% of the entire family may be covered by multiple sequence reads). Consensus sequences may be constructed from the progeny polynucleotides in each family to dramatically reduce the error rate from the nominal per base sequencing error rate to a rate that is perhaps many orders of magnitude lower. For example, if a sequence analyzer has a random per-base error rate of 1% and a selected family has 10 reads, a consensus sequence constructed from these 10 reads will have an error rate of less than 0.0001%. Thus, the sampling size of the amplified progeny to be sequenced can be selected to ensure that sequences in the sample have a frequency at or below the nominal per-base sequencing error rate for a proportion of the sequencing platforms used, and have at least a 99% probability of being represented by at least one read.

[0219] In another embodiment, the set of amplified progeny polynucleotides is sampled to a level that provides a high probability, for example at least 90%, that a sequence represented in the set of tagged parent polynucleotides is covered by at least one sequence read, preferably multiple sequence reads, at approximately the same frequency as the per base sequencing error rate of the sequencing platform used.Thus, for example, if a sequencing platform has a per base error rate of 0.2% in a sequence, or a set of sequences is represented in the set of tagged parent polynucleotides at a frequency of about 0.2%, the number of polynucleotides in the sequenced amplified progeny pool can be about X times the number of unique molecules in the set of tagged parent polynucleotides.

[0220] These methods can be combined with any of the noise-reducing methods described, including, for example, qualifying sequence reads for inclusion in the pool of sequences used to generate a consensus sequence.

[0221] This information can be used here for both qualitative and quantitative analysis. For example, in the case of quantitative analysis, a measure of the amount of tagged parent molecules located in the reference sequence is determined, e.g., count. This measure can be compared to a measure of tagged parent molecules located in different genomic regions. That is, the amount of tagged parent molecules located in a first site or mappable position in a reference sequence, such as the human genome, can be compared to a measure of tagged parent molecules located in a second site or mappable position in the reference sequence. This comparison can reveal, for example, the relative amount of parent molecules located in each region. This can then provide an indication of copy number polymorphism for molecules located in a particular region. For example, if the measure of polynucleotides located in a first reference sequence is greater than the measure of polynucleotides located in a second reference sequence, this can suggest that the parent population, and by extension the original sample, contained polynucleotides from cells that exhibit aneuploidy. These measures can be normalized to a control sample to eliminate various biases. Quantitative measures can include, for example, numbers, counts, frequencies (whether relative, inferred, or absolute).

[0222] The reference genome may include the genome of any species of interest. Human genome sequences useful as references may include the hg19 assembly or any previous or available hg assembly. Such sequences can be queried using the genome browser available at genome.ucsc.edu / index.html. Genomes of other species include, for example, PanTro2 (chimpanzee) and mm9 (mouse).

[0223] For qualitative analysis, sequences from a set of tagged polynucleotides that map to a reference sequence can be analyzed for variant sequences and their frequency within a population of tagged parent polynucleotides can be measured. II. Sample Preparation A. Polynucleotide Isolation and Extraction

[0224] The disclosed system and method can have a wide variety of applications in the manipulation, preparation, identification and / or quantification of cell-free polynucleotides.Examples of polynucleotides include, but are not limited to, DNA, RNA, amplicon, cDNA, dsDNA, ssDNA, plasmid DNA, cosmid DNA, high molecular weight (MW) DNA, chromosomal DNA, genomic DNA, viral DNA, bacterial DNA, mtDNA (mitochondrial DNA), mRNA, rRNA, tRNA, nRNA, siRNA, snRNA, snoRNA, scaRNA, microRNA, dsRNA, ribozyme, riboswitch and viral RNA (e.g., retroviral RNA).

[0225] The cell-free polynucleotides may be derived from a variety of sources, including human, mammalian, non-human mammalian, ape, monkey, chimpanzee, reptilian, amphibian or avian origin. Samples may also be extracted from a variety of animal bodily fluids that contain cell-free sequences, including, but not limited to, blood, serum, plasma, vitreous humor, sputum, urine, tears, sweat, saliva, semen, mucosal discharge, mucus, cerebrospinal fluid, amniotic fluid, lymphatic fluid, etc. The cell-free polynucleotides may be of fetal origin (via bodily fluids taken from a pregnant subject) or may be derived from the subject's own tissues.

[0226] Isolation and extraction of cell-free polynucleotides can be performed through collection of bodily fluids using a variety of techniques. In some cases, collection can involve drawing bodily fluids from a subject using a syringe. In other cases, collection can involve pipetting or directly collecting bodily fluids into a collection vessel.

[0227] After collecting body fluid, cell-free polynucleotides can be isolated and extracted using various techniques known in the art. In some cases, cell-free DNA can be isolated, extracted and prepared using commercially available kits such as Qiagen Qiamp® Circulating Nucleic Acid Kit protocol. In other examples, Qiagen Qubit™ dsDNA HS Assay kit protocol, Agilent™ DNA 1000 kit or TruSeq™ Sequencing Library Preparation; Low-Throughput (LT) protocol can be used.

[0228] Generally, cell-free polynucleotides are extracted and isolated from body fluids through a partitioning process in which cell-free DNA is separated from cells and other insoluble components of body fluids when found in solution. Partitioning can include, but is not limited to, techniques such as centrifugation or filtration. In other cases, cells are not partitioned from cell-free DNA first, but are dissolved. In this example, intact cell genomic DNA is partitioned by selective precipitation. Cell-free polynucleotides including DNA can remain soluble and can be separated and extracted from insoluble genomic DNA. Generally, after addition of buffer and other washing steps specific to various kits, DNA can be precipitated using isopropanol precipitation. Additional clean-up steps such as silica-based columns can be used to remove contaminants or salts. The general process can be optimized for specific applications. For example, non-specific bulk carrier polynucleotides can be added throughout the reaction to optimize certain aspects of the procedure, such as yield.

[0229] Isolation and purification of cell-free DNA can be accomplished using any means, including but not limited to the use of commercially available kits and protocols provided by companies such as Sigma Aldrich, Life Technologies, Promega, Affymetrix, IBI, etc. Kits and protocols may not be commercially available.

[0230] After isolation, optionally the cell-free polynucleotides are premixed with one or more additional materials, such as one or more reagents (eg, ligases, proteases, polymerases) prior to sequencing.

[0231] One way to increase conversion efficiency involves using a ligase (e.g., ThermoPhage ssDNA ligase derivative) engineered for optimal reactivity towards single-stranded DNA. Such a ligase bypasses the traditional steps in library preparation of end-repair and A-tailing, which may have poor efficiency and / or accumulate losses due to intermediate clean-up steps, doubling the probability that a starting polynucleotide, either sense or antisense, is converted into a properly tagged polynucleotide. It also converts double-stranded polynucleotides that may have overhangs that may not be sufficiently blunted by typical end-repair reactions. The optimal reaction conditions for this ssDNA reaction are: 1× reaction buffer (50 mM MOPS (pH 7.5), 1 mM DTT, 5 mM MgCl2, 10 mM KCl). 50 mM ATP and 25 mg / ml BSA, 2.5mM MnCl2, 200pmol 85nt ssDNA oligomer and 5U ssDNA ligase were incubated at 65°C for 1 hour. Subsequent amplification using PCR can further convert the tagged single-stranded library into a double-stranded library, resulting in an overall conversion efficiency of well over 20%. Other methods to increase the conversion rate, for example to above 10%, include, for example, any of the following, alone or in combination: annealing-optimized molecular inversion probes, blunt-end ligation with a well-controlled polynucleotide size range, sticky-end ligation, or multiple pre-amplification steps with or without the use of fusion primers. B. Molecular Barcoding of Cell-Free Polynucleotides

[0232] The disclosed system and method may also allow tagging or tracking of cell-free polynucleotides to allow subsequent identification and origin of specific polynucleotides.This feature is in contrast to other methods that use pooled or multiplexed reactions and only provide measurement or analysis as the average of multiple samples.Now, by assigning identifiers to individual polynucleotides or subgroups of polynucleotides, it may be possible to assign unique identities to individual sequences or fragments of sequences.This may allow data to be obtained from individual samples and is not limited to the average of samples.

[0233] In some instances, nucleic acids or other molecules derived from a single strand may share a common tag or identifier, and therefore may later be identified as derived from that strand. Similarly, all of the fragments derived from a single strand of nucleic acid may be tagged with the same identifier or tag, allowing for later identification of the fragments derived from its parent strand. In other cases, gene expression products (e.g., mRNA) may be tagged to quantify expression that may count barcodes, or barcodes combined with sequences attached thereto. In yet other cases, the system and method may be used as a control for PCR amplification. In such cases, multiple amplification products from a PCR reaction may be tagged with the same tag or identifier. If the products are later sequenced to show sequence differences, the differences between products with the same identifier may be due to PCR errors.

[0234] Furthermore, individual sequences can be identified based on the characteristics of sequence data for the read itself.For example, the detection of unique sequence data at the beginning (start) and end (stop) of each sequencing read can be used alone or in combination with the length, i.e. number, of base pairs of unique sequence in each sequence read to assign unique identity to each molecule.Therefore, the fragments derived from the single strand of nucleic acid that are assigned unique identity can allow subsequent identification of fragments derived from the parent strand.This can be used together with the restriction of the initial genetic material of the start to limit diversity.

[0235] Furthermore, the use of unique sequence data and sequencing read length at the beginning (start) and end (end) of each sequencing read can be used alone or in combination with the use of barcodes. In some cases, barcodes can be unique as described herein. In other cases, barcodes themselves may not be unique. In this case, the use of non-unique barcodes in combination with sequence data and sequencing read length at the beginning (start) and end (end) of each sequencing read can allow for unique identity assignment to each sequence. Similarly, fragments from a single strand of nucleic acid that are assigned unique identity can then allow for the subsequent identification of fragments from the parent strand.

[0236] Generally, the methods and systems provided herein are useful for preparing cell-free polynucleotide sequences for downstream application, sequencing reactions. Often, the sequencing method is classical Sanger sequencing. Sequencing methods include high-throughput sequencing, pyrosequencing, sequencing by synthesis, single molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing by ligation, sequencing by hybridization, RNA-Seq (Illumina), Digital Gene Expression (Helicos), next-generation sequencing, Single Molecule Sequencing by Synthesis (SMSS) (Helicos), massively parallel sequencing, Clonal Sequencing (CLS), and other methods. These may include, but are not limited to, Single Molecule Array (Solexa), shotgun sequencing, Maxam-Gilbert sequencing, primer walking and any other sequencing method known in the art. C. Assignment of barcodes to cell-free polynucleotide sequences

[0237] The systems and methods disclosed herein can be used in applications involving the assignment of unique or non-unique identifiers or molecular barcodes to cell-free polynucleotides. Often, the identifier is a barcode oligonucleotide used to tag the polynucleotide; however, in some cases, a different unique identifier is used. For example, in some cases, the unique identifier is a hybridization probe. In other cases, the unique identifier is a dye, in which case the attachment can include intercalation of the dye into the analyte molecule (e.g., intercalation into DNA or RNA) or binding to a dye-labeled probe. In still other cases, the unique identifier can be a nucleic acid oligonucleotide, in which case the attachment to the polynucleotide sequence can include a ligation reaction between the oligonucleotide and the sequence or incorporation by PCR. In other cases, the reaction can include the addition of a metal isotope to the analyte directly or by an isotope-labeled probe. Generally, assignment of unique or non-unique identifiers or molecular barcodes in reactions of the present disclosure may follow the methods and systems described, for example, in U.S. Patent Applications 20010053519, 20030152490, 20110160078 and U.S. Patent No. 6,582,908.

[0238] Often, the method includes attaching an oligonucleotide barcode to a nucleic acid analyte by an enzymatic reaction, including but not limited to a ligation reaction. For example, a ligase enzyme can covalently attach a DNA barcode to fragmented DNA (e.g., high molecular weight DNA). After attachment of the barcode, the molecules can be subjected to a sequencing reaction.

[0239] However, other reactions can be used as well.For example, an oligonucleotide primer containing a barcode sequence can be used in an amplification reaction (e.g., PCR, qPCR, reverse transcriptase PCR, digital PCR, etc.) of a DNA template analyte, thereby generating a tagged analyte.After the assignment of barcodes to individual cell-free polynucleotide sequences, the pool of molecules can be sequenced.

[0240] In some cases, PCR can be used for global amplification of cell-free polynucleotide sequences. This can include first using adapter sequences that can be ligated to various molecules, followed by PCR amplification using universal primers. PCR for sequencing can be performed using any means, including but not limited to using commercial kits provided by Nugen (WGA kit), Life Technologies, Affymetrix, Promega, Qiagen, etc. In other cases, only certain target molecules in a population of cell-free polynucleotide molecules can be amplified. Specific primers can be used in conjunction with adapter ligation to selectively amplify certain targets for downstream sequencing.

[0241] Unique identifiers (e.g., oligonucleotide barcodes, antibodies, probes, etc.) can be randomly or non-randomly introduced into cell-free polynucleotide sequences. In some cases, they are introduced into microwells at the expected ratio of unique identifiers. For example, unique identifiers can be loaded such that more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000, 500,000, 1,000,000, 10,000,000, 50,000,000 or 1,000,000,000 unique identifiers are loaded per genome sample. In some cases, unique identifiers may be loaded such that less than about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000, 500,000, 1,000,000, 10,000,000, 50,000,000 or 1,000,000,000 unique identifiers are loaded per genome sample. In some cases, the average number of unique identifiers loaded per genomic sample is less than or more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000, 500,000, 1,000,000, 10,000,000, 50,000,000 or 1,000,000,000 unique identifiers per genomic sample.

[0242] In some cases, the unique identifiers can be of various lengths, with each barcode being at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000 base pairs. In other cases, the barcodes can include less than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000 base pairs.

[0243] In some cases, the unique identifier may be a predetermined or random or semi-random sequence oligonucleotide. In other cases, multiple barcodes may be used such that the barcodes are not necessarily unique to each other among the multiple. In this example, the barcodes may be ligated to individual molecules such that the combination of barcode and sequence is ligated to create a unique sequence that can be tracked individually. Detection of non-unique barcodes, as described herein, in combination with sequence data at the beginning (start) and end (stop) portions of the sequence read, may allow for the assignment of a unique identity to a particular molecule. The length, i.e., number of base pairs, of individual sequence reads may also be used to assign a unique identity to such molecules. As described herein, fragments derived from a single strand of nucleic acid that are thereby assigned a unique identity may allow subsequent identification of fragments derived from the parent strand. In this way, polynucleotides in a sample may be uniquely or substantially uniquely tagged.

[0244] The unique identifiers can be used to tag a wide range of analytes, including but not limited to RNA or DNA molecules. For example, the unique identifiers (e.g., barcode oligonucleotides) can be attached to entire nucleic acid strands or fragments of nucleic acids (e.g., fragmented genomic DNA, fragmented RNA). The unique identifiers (e.g., oligonucleotides) can also be attached to gene expression products, genomic DNA, mitochondrial DNA, RNA, mRNA, etc.

[0245] In many applications, it may be important to determine whether to give each individual cell-free polynucleotide sequence a different unique identifier (e.g., oligonucleotide barcode). If the population of unique identifiers introduced into the system and method is not significantly diverse, perhaps different specimens may be tagged with the same identifier. The system and method disclosed herein may allow the detection of cell-free polynucleotide sequences tagged with the same identifier. In some cases, a reference sequence may be included with the population of cell-free polynucleotide sequences to be analyzed. The reference sequence may be, for example, a nucleic acid with a known sequence and a known quantity. If the unique identifier is an oligonucleotide barcode and the specimen is a nucleic acid, the tagged specimen may then be sequenced and quantified. These methods may suggest that one or more fragments and / or specimens may be assigned the same barcode.

[0246] The method disclosed herein may include using the reagents necessary to assign a barcode to a specimen. For ligation reaction, reagents including but not limited to ligase enzyme, buffer, adapter oligonucleotide, multiple unique identifier DNA barcodes, etc. may be loaded into the system and method. For enrichment, reagents including but not limited to multiple PCR primers, oligonucleotides containing unique identification sequences, i.e. barcode sequences, DNA polymerase, DNTPs, and buffers, etc. may be used in preparation for sequencing.

[0247] Generally, the methods and systems of the present disclosure may use the methods of U.S. Patent No. 7,537,897, which uses molecular barcodes to count molecules or analytes.

[0248] In a sample containing fragmented genomic DNA from multiple genomes, e.g., cell-free DNA (cfDNA), there is some probability that two or more polynucleotides from different genomes will have the same start and end positions ("duplicates" or "cognate"). The estimated number of duplicates starting at any position is a function of the number of haploid genome equivalents in the sample and the distribution of fragment sizes. For example, cfDNA has a peak of fragments at about 160 nucleotides, with most of the fragments in this peak ranging from about 140 nucleotides to 180 nucleotides. Thus, cfDNA from a genome of about 3 billion bases (e.g., the human genome) contains nearly 20 million (2×10 7 A sample of about 30 ng of DNA may contain about 10,000 haploid human genome equivalents. (Similarly, a sample of about 100 ng of DNA may contain about 30,000 haploid human genome equivalents.) About 10,000 (10 4 A sample containing approximately 200 billion (2 × 10) haploid genome equivalents contains approximately 200 billion (2 × 10 11 ) individual polynucleotide molecules. Empirical evidence has shown that in a sample of about 10,000 haploid genome equivalents of human DNA, there are about three duplicated polynucleotides beginning at any given position. Thus, such a collection may have about 6×10 10 ~8×10 10 (About 60 billion to 80 billion, for example, about 70 billion (7×10 10 The diversity may include 1) differently sequenced polynucleotide molecules.

[0249] The probability of a molecule being correctly identified depends on the initial number of genome equivalents, the length distribution of the sequenced molecules, the uniformity of the sequence, and the number of tags. When the number of tags is equal to 1, it is equivalent to having no unique tag or not tagged. The table below lists the probability of correctly identifying a molecule as unique, assuming a representative cell-free size distribution as above. [Table 1A]

[0250] In this case, when sequencing genomic DNA, it may not be possible to determine which sequence reads are derived from which parent molecule.There is a possibility that two duplicate molecules, i.e., molecules with the same start and end positions, have different unique identifiers, and this problem can be reduced by tagging parent molecules with a sufficient number of unique identifiers (e.g., number of tags) so that sequence reads can be traced back to a specific parent molecule.One approach to this problem is to uniquely tag all or almost all different parent molecules in a sample.However, depending on the number of haploid gene equivalents and the distribution of fragment sizes in a sample, this may require billions of different unique identifiers.

[0251] This method can be cumbersome and expensive. The present invention provides methods and compositions in which a population of polynucleotides in a sample of fragmented genomic DNA is tagged with n different unique identifiers, where n is at least 2 and no greater than 100,000. * z, where z is a measure of central tendency (e.g., mean, median, mode) of the expected number of duplicated molecules having the same start and end positions. In certain embodiments, n is 2 * z, 3 * z, 4 * z, 5 * z, 6 * z, 7 * z, 8 * z, 9 * z, 10 * z, 11 * z, 12 * z, 13 * z, 14 * z, 15 * z, 16 * z, 17 * z, 18 * z, 19 * z or 20 * In another embodiment, n is 100,000 or more.* z, 10,000 * z, 1000 * z or 100 * z is not greater than (e.g., an upper limit). Thus, n can range between any combination of these lower and upper limits. In certain embodiments, n is 5 * z~15 * z, 8 * z~12 * z or about 10 * z. For example, a haploid human genome equivalent has about 3 picograms of DNA. A sample of about 1 microgram of DNA contains about 300,000 haploid human genome equivalents. The number n can be 15-45, 24-36, or about 30. As long as at least some of the duplicated or cognate polynucleotides have unique identifiers, i.e., have different tags, improved sequencing can be achieved. However, in certain embodiments, the number of tags used is selected such that there is at least a 95% probability that all of the duplicated molecules starting at any one position have a unique identifier. For example, a sample containing about 10,000 haploid human genome equivalents of cfDNA can be tagged with about 36 unique identifiers. The unique identifiers can include 6 unique DNA barcodes. When attached to both ends of the polynucleotide, 36 possible unique identifiers are generated. The sample to be tagged in such a manner can be a sample containing fragmented polynucleotides, e.g., genomic DNA, e.g., cfDNA, in the range of about 10 ng to about 100 ng, about 1 μg, or about 10 μg.

[0252] Thus, the present invention also provides a composition of tagged polynucleotides. The polynucleotides may include fragmented DNA, e.g., cfDNA. The set of polynucleotides in the composition located at mappable base positions in the genome may not be uniquely tagged, i.e., the number of different identifiers may be at least 2 and may be less than the number of polynucleotides located at mappable base positions. A composition of about 10 ng to about 10 μg (e.g., about 10 ng to 1 μg, about 10 ng to 100 ng, about 100 ng to 10 μg, about 100 ng to 1 μg, about 1 μg to 10 μg) may have any of 2, 5, 10, 50, or 100 to any of 100, 1000, 10,000, or 100,000 different identifiers. For example, 5 to 100 different identifiers may be used to tag the polynucleotides in such a composition. III. Nucleic Acid Sequencing Platform

[0253] After extracting and isolating cell-free polynucleotides from body fluids, cell-free sequences can be sequenced. Often, the sequencing method is classical Sanger sequencing. Sequencing methods can include, but are not limited to, high-throughput sequencing, pyrosequencing, sequencing by synthesis, single molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing by ligation, sequencing by hybridization, RNA-Seq (Illumina), Digital Gene Expression (Helicos), next-generation sequencing, Single Molecule Sequencing by Synthesis (SMSS) (Helicos), massively parallel sequencing, Clonal Single Molecule Array (Solexa), shotgun sequencing, Maxam-Gilbert sequencing, primer walking, sequencing using PacBio, SOLiD, Ion Torrent or nanopore platform, and any other sequencing method known in the art.

[0254] In some cases, various types of sequencing reactions as described herein may include various sample processing units. The sample processing units may include, but are not limited to, multiple lanes, multiple channels, multiple wells, or other means of processing multiple sample sets substantially simultaneously. Furthermore, the sample processing units may include multiple sample chambers that allow multiple runs to be processed simultaneously.

[0255] In some cases, simultaneous sequencing reactions can be carried out using multiplex sequencing.In some cases, cell-free polynucleotides can be sequenced by at least 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, 100,000 sequencing reactions.In other cases, cell-free polynucleotides can be sequenced by less than 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, 100,000 sequencing reactions.Sequencing reactions can be carried out sequentially or simultaneously.Subsequent data analysis can be carried out on all or part of sequencing reactions. In some cases, data analysis may be performed on at least 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, 100,000 sequencing reactions. In other cases, data analysis may be performed on less than 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, 100,000 sequencing reactions.

[0256] In other examples, the number of sequencing reactions can provide different amounts of coverage for the genome. In some cases, the sequence coverage of the genome can be at least 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.9% or 100%. In other cases, the sequence coverage of the genome can be less than 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.9% or 100%.

[0257] In some examples, sequencing can be performed on cell-free polynucleotides, which can include a variety of different types of nucleic acids.Nucleic acid can be polynucleotide or oligonucleotide.Nucleic acid includes, but is not limited to, DNA or RNA, single-stranded or double-stranded, or RNA / cDNA pair. IV. Polynucleotide Analysis Strategies

[0258] FIG. 8 is a flow chart 800 showing a strategy for analyzing polynucleotides in a sample of initial genetic material. In step 802, a sample containing initial genetic material is provided. The sample may contain a low abundance of target nucleic acid. For example, nucleic acid from a normal or wild type genome (e.g., germline genome) may predominate in a sample that also contains 20% or less, 10% or less, 5% or less, 1% or less, 0.5% or less, or 0.1% or less of nucleic acid from at least one other genome containing a genetic mutation, such as a cancer genome or fetal genome or another species genome. The sample may, for example, contain cell-free nucleic acid or cells containing nucleic acid. The initial genetic material may constitute 100 ng or less of nucleic acid. This may contribute to an appropriate oversampling of the original polynucleotides by the sequencing or genetic analysis process. Alternatively, the sample may be artificially capped or restricted to reduce the amount of nucleic acid to 100 ng or less, or may be selectively enriched to analyze only sequences of interest. The sample can be modified to selectively generate sequence reads for molecules that map to each of one or more selected sites in the reference sequence. A sample of 100 ng of nucleic acid can contain approximately 30,000 human haploid genome equivalents, i.e., molecules that collectively provide 30,000-fold coverage of the human genome.

[0259] In step 804, the initial genetic material is converted into a set of tagged parent polynucleotides. Tagging can include attaching sequenced tags to molecules in the initial genetic material. The sequenced tags can be selected such that all unique polynucleotides located at the same site in the reference sequence have a unique identifying tag. The conversion can be performed with high efficiency, for example, at least 50%.

[0260] In step 806, the set of tagged parent polynucleotides is amplified to generate a set of amplified progeny polynucleotides. The amplification can be, for example, 1,000-fold.

[0261] In step 808, the set of amplified progeny polynucleotides is sampled for sequencing at a sampling rate selected such that the sequence reads generated (1) cover a target number of unique molecules in the set of tagged parent polynucleotides, and (2) cover the unique molecules in the set of tagged parent polynucleotides at a factor of the target coverage (e.g., 5-10x coverage of the parent polynucleotides).

[0262] In step 810, the set of sequence reads is collapsed to generate a set of consensus sequences corresponding to the unique tagged parent polynucleotides. The sequence reads can be conditioned for inclusion in the analysis. For example, sequence reads that do not meet a quality control score can be removed from the pool. The sequence reads can be sorted into families that represent the reads of progeny molecules derived from a particular unique parent molecule. For example, a family of amplified progeny polynucleotides can comprise amplified molecules derived from a single parent polynucleotide. By comparing the sequences of the progeny within a family, the consensus sequence of the original parent polynucleotide can be deduced. This generates a set of consensus sequences that represent the unique parent polynucleotides in the tagged pool.

[0263] In step 812, the set of consensus sequences is analyzed using any of the analysis methods described herein. For example, the consensus sequences located at the site of a particular reference sequence can be analyzed to detect the occurrence of genetic variation. The consensus sequences located at a particular reference sequence can be measured and normalized to a control sample. The molecular scales located at the reference sequence can be compared across the genome to identify regions in the genome that have copy number variations or loss of heterozygosity.

[0264] Figure 9 is a flow diagram showing a more general method of extracting information from the signal represented by a collection of sequence reads. In this method, after sequencing the amplified progeny polynucleotides, the sequence reads are grouped into families of molecules amplified from molecules of unique identity (910). This grouping can be the starting point for a method of interpreting the information in the sequence, so that the content of the tagged parent polynucleotides is determined with higher fidelity, e.g., less noise and / or distortion.

[0265] Analysis of a collection of sequence reads allows inference to be made with respect to the parent polynucleotide population that generated the sequence read.Such inference may be useful, since sequencing usually involves reading only a small subset of the total amplified polynucleotides.Therefore, it cannot be ensured that every parent polynucleotide is represented by at least one sequence read in the collection of sequence reads.

[0266] One such inference is the number of unique parent polynucleotides in the original pool. Such inference can be made based on the number of unique families into which sequence reads can be grouped and the number of sequence reads in each family. In this case, a family refers to a collection of sequence reads that can be traced back to the original parent polynucleotide. The inference can be made using well-known statistical methods. For example, if the grouping produces many families (each represented by one or a few descendants), it can be inferred that the original population contained many more unique parent polynucleotides that were not sequenced. On the other hand, if the grouping produces only a few families (each represented by many descendants), it can be inferred that most of the unique polynucleotides in the parent population are represented in that family by at least one sequence read group.

[0267] Another such inference result is the frequency of a base or sequence of bases at a particular locus in the original pool of polynucleotides. Such inference can be made based on the number of unique families into which sequence reads can be grouped and the number of sequence reads in each family. When analyzing the base calls at a locus within a family of sequence reads, a confidence score is assigned to each particular base call or sequence. Then, taking into account the confidence scores for each base call in multiple families, the frequency of each base or sequence at that locus is determined. V. Copy Number Variation Detection A. Detection of copy number variation using a single sample

[0268] FIG. 1 is a flow chart 100 showing a strategy for detecting copy number variation in a single subject. As shown herein, the method for detecting copy number variation can be carried out as follows. After extracting and isolating cell-free polynucleotides in step 102, a single unique sample can be sequenced by a nucleic acid sequencing platform known in the art in step 104. This process generates multiple genome fragment sequence reads. In some cases, these sequence reads can include barcode information. In other examples, barcodes are not used. After sequencing, the reads are assigned a quality score. The quality score can be an indication of the reads that suggests that they may be useful in subsequent analysis based on a threshold value. In some cases, some reads are not of sufficient quality or length to perform subsequent mapping steps. Sequencing reads with a quality score of at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% can be removed from the data. In other cases, sequencing reads that are assigned a quality score of less than 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be removed from the data set. In step 106, genome fragment reads that meet a specified quality score threshold are mapped to a reference genome, i.e., a template sequence that is known to be free of copy number variations. After mapping alignment, the sequence reads are assigned a mapping score. The mapping score may be a representation or a read that is mapped back to the reference sequence that indicates whether each position is uniquely mappable or not. In some cases, the reads may be sequences that are not relevant to the analysis of copy number variations. For example, some sequence reads may originate from contaminant polynucleotides. Sequencing reads that have a mapping score of at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be removed from the data set. In other cases, sequencing reads assigned a mapping score less than 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be excluded from the dataset.

[0269] After data sorting and mapping, the multiple sequence reads provide coverage of chromosomal regions. In step 108, these chromosomal regions can be divided into windows or bins of variable length. The windows or bins can be at least 5kb, 10kb, 25kb, 30kb, 35kb, 40kb, 50kb, 60kb, 75kb, 100kb, 150kb, 200kb, 500kb, or 1000kb. The windows or bins can also have up to 5kb, 10kb, 25kb, 30kb, 35kb, 40kb, 50kb, 60kb, 75kb, 100kb, 150kb, 200kb, 500kb, or 1000kb of bases. The windows or bins can be about 5kb, 10kb, 25kb, 30kb, 35kb, 40kb, 50kb, 60kb, 75kb, 100kb, 150kb, 200kb, 500kb or 1000kb.

[0270] For coverage normalization in step 110, each window or bin is selected to contain approximately the same number of mappable bases. In some cases, each window or bin in a chromosomal region may contain the exact number of mappable bases. In other cases, each window or bin may contain a different number of mappable bases. Furthermore, each window or bin may not overlap with adjacent windows or bins. In other cases, a window or bin may overlap with another adjacent window or bin. In some cases, a window or bin may overlap by at least 1 bp, 2 bp, 3 bp, 4 bp, 5 bp, 10 bp, 20 bp, 25 bp, 50 bp, 100 bp, 200 bp, 250 bp, 500 bp, or 1000 bp. In other cases, a window or bin may overlap by up to 1 bp, 2 bp, 3 bp, 4 bp, 5 bp, 10 bp, 20 bp, 25 bp, 50 bp, 100 bp, 200 bp, 250 bp, 500 bp, or 1000 bp. In some cases, the windows or bins may overlap by about 1 bp, 2 bp, 3 bp, 4 bp, 5 bp, 10 bp, 20 bp, 25 bp, 50 bp, 100 bp, 200 bp, 250 bp, 500 bp or 1000 bp.

[0271] In some cases, each of the window regions may be sized to contain approximately the same number of uniquely mappable bases. The mappability of each base that makes up the window region is determined and used to generate a mappability file that contains a representation of the reads from the references that map back to the references for each file. The mappability file contains a column for each position, indicating whether each position is uniquely mappable or not.

[0272] Additionally, predefined windows known throughout the genome to be difficult to sequence or contain substantially high GC bias can be excluded from the data set. For example, regions known to be near the centromeres of chromosomes (i.e., centromeric DNA) are known to contain highly repetitive sequences that may result in false positive results. These regions can be excluded. Other regions of the genome, such as regions containing unusually high concentrations of other highly repetitive sequences (e.g., microsatellite DNA), can also be excluded from the data set.

[0273] The number of windows analyzed can also vary. In some cases, at least 10, 20, 30, 40, 50, 100, 200, 500, 1000, 2000, 5,000, 10,000, 20,000, 50,000, or 100,000 windows are analyzed. In other cases, the number of windows analyzed is up to 10, 20, 30, 40, 50, 100, 200, 500, 1000, 2000, 5,000, 10,000, 20,000, 50,000, or 100,000 windows are analyzed.

[0274] In the case of an exemplary genome derived from a cell-free polynucleotide sequence, the next step involves determining the read coverage for each window region. This can be done with reads with or without barcodes. In the absence of barcodes, the previous mapping step can provide coverage of various base positions. Sequence reads that have sufficient mapping and quality scores and are included in the unsorted chromosome window can be counted. The number of coverage reads can be assigned a score per mappable position. In the case of including barcodes, all sequences with the same barcode, physical property, or combination of the two can be collapsed into one read, since they are derived from the sample parent molecule. This step reduces bias that may have been introduced during any of the previous steps, such as the step involving amplification. For example, if one molecule is amplified 10 times, but another molecule is amplified 1000 times, each molecule will only be represented once after collapsing, thereby negating the effects of uneven amplification. Only reads with unique barcodes can be counted for each mappable position, which can affect the assigned score.

[0275] Consensus sequence can be generated from a family of sequence reads by any method known in the art.Such methods include, for example, linear or nonlinear methods (such as voting, averaging, statistical, maximum a posteriori or maximum likelihood detection, dynamic programming, Bayesian, hidden Markov or support vector machine methods, etc.) that construct consensus sequence derived from digital communication theory, information theory or bioinformatics.

[0276] After the sequence read coverage is determined, a probabilistic modeling algorithm is applied to convert the normalized nucleic acid sequence read coverage for each window region into distinct copy number states. In some cases, this algorithm can include one or more of the following: hidden Markov model, dynamic programming, support vector machine, Bayesian network, trellis decoding, Viterbi decoding, expectation maximization, Kalman filtering, and neural network.

[0277] In step 112, copy number variation in chromosomal regions can be identified by utilizing the distinct copy number status of each window region.In some cases, all adjacent window regions with the same copy number can be merged into one segment to report the presence or absence of copy number variation status.In some cases, various windows can be sorted before merging them with other segments.

[0278] In step 114, the copy number polymorphisms may be reported as a graph showing various locations in the genome and the corresponding increase or decrease or maintenance of copy number polymorphism at each respective location. Additionally, the copy number polymorphisms may be used to report a percentage score indicating how much pathological material (or nucleic acid with copy number polymorphism) is present in the cell-free polynucleotide sample.

[0279] One method of determining copy number variation is shown in FIG. 10. In the method, sequence reads are grouped into families generated from a single parent polynucleotide (1010), and then the families are quantified, for example, by determining the number of families located at each site of a plurality of different reference sequences. CNV can be determined directly by comparing the quantitative measures of the families at each of a plurality of different loci (1016b). Alternatively, a quantitative measure of the families in a population of tagged parent polynucleotides can be inferred using both the quantitative measures of the families and the quantitative measures of the family members in each family, for example, as discussed above. CNV can then be determined by comparing the inferred measures of quantity at a plurality of loci. In other embodiments, a hybrid approach can be taken in which a similar inference of the original quantity can be performed after normalization for representation bias during the sequencing process, such as GC bias. B. Detection of copy number variation using paired samples

[0280] The detection of copy number variation of paired samples shares many of its steps and parameters with the single sample approach described herein.However, as shown in 200 of Fig. 2 of copy number variation detection, the use of paired samples requires the comparison of sequence coverage with control sample, rather than the comparison of sequence coverage with the predicted mappability of genome.This approach can be useful for window-wide normalization.

[0281] FIG. 2 is a flow chart 200 showing a strategy for detecting copy number variation in paired subjects. As shown herein, the method for detecting copy number variation can be carried out as follows. In step 204, after the extraction and isolation of the sample in step 202, a single unique sample can be sequenced by a nucleic acid sequencing platform known in the art. This step generates multiple genome fragment sequence reads. In addition, a sample or a control sample is taken from another subject. In some cases, the control subject can be a subject known not to have a disease, while the other subject can have or be at risk for a particular disease. In some cases, these sequence reads can include barcode information. In other examples, barcodes are not used. After sequencing, the reads are assigned a quality score. In some cases, some reads are not of sufficient quality or length to perform the subsequent mapping step. Sequencing reads with a quality score of at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% can be removed from the dataset. In other cases, sequencing reads that are assigned a quality score of less than 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be removed from the data set. In step 206, genome fragment reads that meet a specified quality score threshold are mapped to a reference genome, i.e., a template sequence that is known to be free of copy number variations. After mapping alignment, the sequence reads are assigned a mapping score. In some cases, the reads may be sequences that are not relevant to the analysis of copy number variations. For example, some sequence reads may originate from contaminant polynucleotides. Sequencing reads that have a mapping score of at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be removed from the data set. In other cases, sequencing reads that are assigned a mapping score of less than 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be removed from the data set.

[0282] After data sorting and mapping, the multiple sequence reads provide coverage of chromosomal regions for each of the test and control subjects. In step 208, these chromosomal regions can be divided into windows or bins of variable length. The windows or bins can be at least 5kb, 10kb, 25kb, 30kb, 35kb, 40kb, 50kb, 60kb, 75kb, 100kb, 150kb, 200kb, 500kb, or 1000kb. The windows or bins can also be less than 5kb, 10kb, 25kb, 30kb, 35kb, 40kb, 50kb, 60kb, 75kb, 100kb, 150kb, 200kb, 500kb, or 1000kb.

[0283] For coverage normalization in step 210, each window or bin is selected to contain approximately the same number of mappable bases for each of the test and control subjects. In some cases, each window or bin in a chromosomal region may contain the exact number of mappable bases. In other cases, each window or bin may contain a different number of mappable bases. Furthermore, each window or bin may not overlap with adjacent windows or bins. In other cases, a window or bin may overlap with another adjacent window or bin. In some cases, a window or bin may overlap by at least 1 bp, 2 bp, 3 bp, 4 bp, 5 bp, 10 bp, 20 bp, 25 bp, 50 bp, 100 bp, 200 bp, 250 bp, 500 bp, or 1000 bp. In other cases, the windows or bins may overlap by less than 1 bp, 2 bp, 3 bp, 4 bp, 5 bp, 10 bp, 20 bp, 25 bp, 50 bp, 100 bp, 200 bp, 250 bp, 500 bp or 1000 bp.

[0284] In some cases, each window region may be sized to include approximately the same number of uniquely mappable bases for each of the test and control subjects. The mappability of each base that constitutes the window region is determined and used to generate a mappability file that includes a representation of the reads from the references that map back to the references for each file. The mappability file includes a column for each position, indicating whether each position is uniquely mappable or not.

[0285] In addition, predefined windows known throughout the genome to be difficult to sequence or contain substantially high GC bias are excluded from the data set. For example, regions known to be near the centromeres of chromosomes (i.e., centromeric DNA) are known to contain highly repetitive sequences that may result in false positive results. These regions can be filtered out. Other regions of the genome, such as regions containing unusually high concentrations of other highly repetitive sequences (e.g., microsatellite DNA), can also be excluded from the data set.

[0286] The number of windows analyzed can also vary. In some cases, at least 10, 20, 30, 40, 50, 100, 200, 500, 1000, 2000, 5,000, 10,000, 20,000, 50,000, or 100,000 windows are analyzed. In other cases, less than 10, 20, 30, 40, 50, 100, 200, 500, 1000, 2000, 5,000, 10,000, 20,000, 50,000, or 100,000 windows are analyzed.

[0287] In the case of an exemplary genome derived from a cell-free polynucleotide sequence, the next step involves determining the read coverage for each window region for each of the test and control subjects. This can be done with reads with or without barcodes. In the absence of barcodes, a previous mapping step can provide coverage of various base positions. Sequence reads that have sufficient mapping and quality scores and are included in the unsorted chromosomal window can be counted. The number of coverage reads can be assigned a score per mappable position. In the case of including barcodes, all sequences with the same barcode can be collapsed into one read since they are derived from the sample parent molecule. This step reduces bias that may have been introduced in any of the previous steps, such as the step involving amplification. Only reads with unique barcodes can be counted for each mappable position, which can affect the assigned score. For this reason, it is important to perform the barcode ligation step in an optimized manner to minimize the amount of bias.

[0288] When determining the nucleic acid read coverage for each window, the coverage for each window can be normalized by the average coverage for that sample.When using such an approach, it may be desirable to sequence both the test subject and the control under similar conditions.The read coverage for each window can then be expressed as a ratio over similar windows.

[0289] The nucleic acid read coverage ratio for each window in the test subject can be determined by dividing the read coverage of each window region in the test sample by the read coverage of the corresponding window region in the control ample.

[0290] After determining sequence read coverage ratio, apply probabilistic modeling algorithm to convert normalized ratio for each window region into distinct copy number state.In some cases, this algorithm can include hidden Markov model.In other cases, the probabilistic model can include dynamic programming, support vector machine, Bayesian modeling, probabilistic modeling, trellis decoding, Viterbi decoding, expectation maximization, Kalman filtering or neural network.

[0291] In step 212, copy number variation in chromosomal regions can be identified by utilizing the distinct copy number status of each window region. In some cases, all adjacent window regions with the same copy number can be merged into one segment to report the presence or absence of copy number variation status. In some cases, various windows can be sorted before they are merged with other segments.

[0292] In step 214, the copy number variation may be reported as a graph showing various locations in the genome and the corresponding increase or decrease or maintenance of copy number variation at each respective location. Additionally, the copy number variation may be used to report a percentage score that indicates how much diseased material is present in the cell-free polynucleotide sample. VI. Rare Mutation Detection

[0293] Rare mutation detection shares similar features with both copy number variation approaches. However, as shown in FIG. 3, 300, rare mutation detection uses sequence coverage compared to control samples or reference sequences, rather than comparing sequence coverage to the relative mappability of the genome. This approach can be useful for normalization across windows.

[0294] Typically, detection of rare mutations can be performed on selectively enriched regions of the genome or transcriptome purified and isolated in step 302. As described herein, specific regions can be selectively amplified from the entire population of cell-free polynucleotides, including but not limited to genes, oncogenes, tumor suppressor genes, promoters, regulatory sequence elements, non-coding regions, miRNAs, snRNAs, and the like. This can be done as described herein. In one example, multiplex sequencing can be used with or without barcode labels for individual polynucleotide sequences. In another example, sequencing can be performed using any nucleic acid sequencing platform known in the art. As in step 304, this step generates multiple genome fragment sequence reads. Additionally, a reference sequence is obtained from a control sample taken from another subject. In some cases, the control subject can be a subject known to be free of known genetic abnormalities or diseases. In some cases, these sequence reads can include barcode information. In other examples, barcodes are not used. After sequencing, the reads are assigned a quality score. The quality score may be a representation of the reads that suggests that they may be useful in subsequent analysis based on a threshold. In some cases, some reads are not of sufficient quality or length to perform the subsequent mapping step. Sequencing reads with a quality score of at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be excluded from the data set. In other cases, sequencing reads that are assigned a quality score that is at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be excluded from the data set. In step 306, the genome fragment reads that meet the defined quality score threshold are mapped to a reference genome, i.e., a reference sequence that is known not to contain rare mutations. After the mapping alignment, the sequence reads are assigned a mapping score. The mapping score may be a representation or a read that is mapped back to a reference sequence that suggests whether each position is uniquely mappable or not.In some cases, the reads may be sequences that are not related to the analysis of rare mutations.For example, some sequence reads may originate from contaminant polynucleotides.The sequencing reads that have a mapping score of at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be excluded from the data set.In other cases, the sequencing reads that are assigned a mapping score of less than 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be excluded from the data set.

[0295] For each mappable base, bases that do not meet a minimum threshold for mappability, i.e., low quality bases, can be replaced by the corresponding base as found in the reference sequence.

[0296] After data sorting and mapping, variant bases found between sequence reads obtained from the subject and the reference sequence are analyzed.

[0297] In the case of an exemplary genome derived from a cell-free polynucleotide sequence, the next step involves determining the read coverage for each mappable base position. This can be done using reads with or without barcodes. In the absence of barcodes, the previous mapping step can provide coverage of different base positions. Sequence reads with sufficient mapping scores and quality scores can be counted. The number of coverage reads can be assigned a score for each mappable position. In the case of including barcodes, all sequences with the same barcode can be collapsed into one consensus read since they are derived from the sample parent molecule. The sequence for each base is aligned with the most dominant nucleotide read for that particular site. Also, by counting the number of unique molecules at each position, simultaneous quantification can be obtained at each position. This step reduces bias that may have been introduced during any of the previous steps, such as those involving amplification. Only reads with unique barcodes can be counted for each mappable position, which can affect the assigned score.

[0298] Once read coverage can be ascertained and variant bases relative to the control sequence in each read identified, the frequency of the variant base can be calculated as the number of reads containing the variant divided by the total number of reads, which can be expressed as a ratio for each mappable position in the genome.

[0299] For each base position, the frequency of all four nucleotides, cytosine, guanine, thymine, and adenine, is analyzed relative to the reference sequence. By applying a probabilistic or statistical modeling algorithm, the normalized ratio for each mappable position is transformed to reflect the state of frequency for each base variant. In some cases, the algorithm may include one or more of the following: hidden Markov model, dynamic programming, support vector machine, Bayesian or probabilistic modeling, trellis decoding, Viterbi decoding, expectation maximization, Kalman filtering, and neural networks.

[0300] In step 312, the separate rare mutation status of each base position can be utilized to identify base variants with high variance frequency compared to the baseline of the reference sequence. In some cases, the baseline can be at least 0.0001%, 0.001%, 0.01%, 0.1%, 1.0%, 2.0%, 3.0%, 4.0%, 5.0%, 10% or 25% frequency. In other cases, the baseline can be at least 0.0001%, 0.001%, 0.01%, 0.1%, 1.0%, 2.0%, 3.0%, 4.0%, 5.0%, 10% or 25% frequency. In some cases, all adjacent base positions with base variants or mutations can be merged into one segment to report the presence or absence of rare mutations. In some cases, various positions can be filtered before being merged with other segments.

[0301] After calculating the frequency of variance for each base position, the variant that has the largest deviation for a particular position in the sequence derived from subject compared to the reference sequence is identified as rare mutation.In some cases, rare mutation can be cancer mutation.In other cases, rare mutation can be correlated with disease state.

[0302] Rare mutations or variants may include genetic abnormalities, including but not limited to single base substitutions or small indels, transversions, translocations, inversions, deletions, truncations or gene truncations.In some cases, rare mutations may be at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15 or 20 nucleotides in length.In other cases, rare mutations may be at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15 or 20 nucleotides in length.

[0303] In step 314, the presence or absence of mutations can be reflected in the form of a graph, showing various positions in the genome and the corresponding increase or decrease or maintenance of the frequency of mutations at each respective position. Furthermore, by using rare mutations, a percentage score can be reported, indicating how much pathological material is present in the cell-free polynucleotide sample. Given the known statistics of representative variance at the reported positions in non-disease reference sequences, a confidence score can accompany each mutation detected. Mutations can also be ranked by abundance in the subject, or by clinically actionable importance.

[0304] FIG. 11 illustrates a method for inferring the frequency of a base or sequence of bases at a particular locus in a population polynucleotide. Sequence reads are grouped into families generated from the original tagged polynucleotides (1110). For each family, a confidence score is assigned to each of one or more bases at that locus. The confidence score may be assigned by any of a number of known statistical methods and may be based at least in part on the frequency with which a base appears in sequence reads belonging to that family (1112). For example, the confidence score may be the frequency with which a base appears in sequence reads. As another example, a hidden Markov model may be constructed such that for each family, a maximum likelihood or maximum a posteriori decision may be made based on the frequency of occurrence of a particular base in a single family. As part of this model, the probability of error for a particular decision and the resulting confidence score may be output as well. The frequency of the base in the original population may then be assigned based on the confidence scores between families (1114). VII. Application Methods A. Early detection of cancer

[0305] Numerous cancers can be detected using the methods and systems described herein. Cancer cells, like most cells, can be characterized by the rate of turnover, where old cells die and are replaced by new cells. Normally, dead cells in contact with vasculature in a given subject can release DNA or DNA fragments into the bloodstream. This is also true for cancer cells at various stages of the disease. Depending on the stage of the disease, cancer cells can also be characterized by various genetic abnormalities, such as copy number polymorphisms and rare mutations. This phenomenon can be used to detect the presence or absence of cancer individuals using the methods and systems described herein.

[0306] For example, blood from a subject at risk of cancer can be collected and prepared as described herein to generate a population of cell-free polynucleotides.In one example, this can be cell-free DNA.The system and method of the present disclosure can be used to detect rare mutations or copy number variations that may be present in certain cancers that exist.The method can help detect the presence of cancerous cells in the body, even though there are no symptoms or other characteristics of disease.

[0307] The types and number of cancers that may be detected may include, but are not limited to, blood cancer, brain cancer, lung cancer, skin cancer, nasal cancer, throat cancer, liver cancer, bone cancer, lymphoma, pancreatic cancer, skin cancer, intestinal cancer, rectal cancer, thyroid cancer, bladder cancer, kidney cancer, oral cancer, stomach cancer, solid tumors, heterogeneous tumors, homogenous tumors, and the like.

[0308] In the early detection of cancer, any system or method described herein, including detection of rare mutation or detection of copy number variation, can be used to detect cancer.These systems and methods can be used to detect any number of genetic abnormalities that can cause cancer or can be caused by cancer.These can include, but are not limited to, mutation, rare mutation, indel, copy number variation, transversion, translocation, inversion, deletion, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, chromosomal structural change, gene fusion, chromosomal fusion, gene truncation, gene amplification, gene duplication, chromosomal damage, DNA damage, abnormal change of nucleic acid chemical modification, abnormal change of epigenetic pattern, abnormal change of nucleic acid methylation, infection and cancer.

[0309] Furthermore, the systems and methods described herein can also be used to help characterize certain cancers. The genetic data generated from the systems and methods of the present disclosure can help experts to better characterize certain forms of cancer. In many cases, cancers are heterogeneous in both composition and stage. Genetic profile data can enable characterization of specific subtypes of cancer, which can be important in diagnosing or treating that subtype. This information can also provide clues to subjects or experts regarding the prognosis of certain types of cancer. B. Cancer Monitoring and Prognosis

[0310] The systems and methods provided herein can be used to monitor already known cancer or other diseases in a particular subject. This can allow the subject or specialist to adapt treatment options to the progression of the disease. In this example, the systems and methods described herein can be used to build a genetic profile of the disease course of a particular subject. In some cases, the cancer can progress and become more aggressive and genetically unstable. In other examples, the cancer can remain benign, inactive, dormant or in remission. The systems and methods of the present disclosure can be useful in determining the progression, remission or recurrence of the disease.

[0311] The systems and methods described herein may also be useful in determining the effectiveness of a particular treatment option. In one example, if the treatment is successful so that more cancers can die and shed DNA, the successful treatment option may actually increase the amount of copy number polymorphisms or rare mutations detected in the subject's blood. In other examples, this may not occur. In another example, perhaps a particular treatment option may correlate with the genetic profile of the cancer over time. This correlation may be useful in selecting a treatment. Furthermore, if the cancer is found to be in remission after treatment, the systems and methods described herein may be useful in monitoring for residual disease or recurrence of the disease.

[0312] For example, mutations occurring at a frequency within a range starting from a threshold level can be determined from DNA in a sample from a subject, e.g., a patient. The mutations can be, for example, cancer-associated mutations. The frequency can be, for example, at least 0.1%, at least 1%, or at least 5% to 100%. The sample can be, for example, cell-free DNA or a tumor sample. Treatment progression can be directed, for example, based on any or all of the mutations occurring within the above frequency ranges, including those frequencies. Samples can be taken from the subject at any time thereafter. Mutations occurring at a frequency within the original range or within a different range can be determined. Treatment progression can be adjusted based on subsequent measurements. C. Early detection and monitoring of other diseases or disease states

[0313] The methods and systems described herein may not be limited to the detection of rare mutations and copy number variations associated with cancer alone. Various other diseases and infections may result in other types of conditions that may be suitable for early detection and monitoring. For example, in certain cases, genetic disorders or infections may cause certain genetic mosaicism in a subject. This genetic mosaicism may cause copy number variations and rare mutations that may be observed. In another example, the systems and methods of the present disclosure may also be used to monitor the genome of immune cells in the body. Immune cells, such as B cells, may undergo rapid clonal expansion in the presence of certain diseases. Clonal expansion may be monitored using detection of copy number variations, and certain immune conditions may be monitored. In this example, analysis of copy number variations may be performed over time to generate a profile of how a certain disease may progress.

[0314] The disclosed systems and methods may also be used to monitor systemic infections themselves, such as those caused by pathogens such as bacteria or viruses. Detection of copy number variation or rare mutations may be used to determine how the pathogen population is changing during the course of an infection. This may be particularly important in chronic infections, such as HIV / AIDs or hepatitis infections, where viruses may change life cycle states and / or mutate to more virulent forms during the course of an infection.

[0315] Yet another example in which the system and method of the present disclosure can be used is the monitoring of transplant subjects. Generally, transplanted tissues undergo a certain degree of rejection by the body when transplanted. The method of the present disclosure can be used to determine or profile the rejection activity of the host body when immune cells attempt to destroy the transplanted tissue. This can be useful in monitoring the status of the transplanted tissue and in modifying the course of treatment or prevention of rejection.

[0316] The disclosed method may also be used to characterize heterogeneity of an abnormal condition in a subject, comprising generating a genetic profile of extracellular polynucleotides in the subject, the genetic profile comprising a plurality of data resulting from copy number variation and rare mutation analysis. In some cases, diseases, including but not limited to cancer, may be heterogeneous. The cells of the disease may not be identical. In the example of cancer, some tumors are known to contain different types of tumor cells, some cells being at different stages of the cancer. In other examples, the heterogeneity may include multiple foci. Also, in the example of cancer, there may be multiple tumor foci, perhaps one or more of which are the result of metastasis spreading from the primary site.

[0317] The methods of the present disclosure can be used to generate or profile fingerprints or datasets that are summaries of genetic information from different cells in a heterogeneous disease, which may include copy number variation and rare mutation analyses, either alone or in combination. D. Early detection and monitoring of other diseases or disease conditions of fetal origin

[0318] Additionally, the disclosed systems and methods may be used to diagnose, prognose, monitor or observe cancer or other diseases of fetal origin, i.e., the methods may be used in pregnant subjects to diagnose, prognose, monitor or observe cancer or other diseases in unborn subjects whose DNA and other polynucleotides may be circulating simultaneously with maternal molecules. VIII. Terminology

[0319] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosed systems and methods. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. Also, to the extent the terms "including," "includes," "having," "has," "with," or variations thereof are used in the detailed description and / or claims, such terms are intended to be inclusive in a manner similar to the term "comprising."

[0320] Some aspects of the system and method of the present disclosure have been described above in the context of example applications for illustration. It should be understood that numerous specific details, relationships and methods are set forth to provide a thorough understanding of the system and method. However, a person skilled in the relevant field will readily recognize that the system and method can be implemented without one or more of the specific details or in other ways. The present disclosure is not limited by the illustrated order of acts or events, as some acts may be performed in different orders and / or simultaneously with other acts or events. Also, not all illustrated acts or events are required to perform the method according to the present disclosure.

[0321] Ranges may be expressed herein as from "about" one particular value and / or to "about" another particular value. When such a range is expressed, another embodiment includes from the one particular value and / or to the other particular value. Similarly, when values ​​are expressed as approximations by use of the antecedent "about," it will be understood that the particular value forms a separate embodiment. It will be further understood that the endpoints of each of these ranges are significant in relation to, and independently of, the other endpoint. As used herein, the term "about" refers to a range that is + or - 15% from the stated numerical value within the context of the particular usage. For example, about 10 can include a range of 8.5 to 11.5. Computer Systems

[0322] The methods of the present disclosure may be carried out using or with the aid of a computer system. Figure 15 shows a computer system 1501 programmed or otherwise configured to carry out the methods of the present disclosure. The computer system 1501 may control various aspects of sample preparation, sequencing and / or analysis. In some examples, the computer system 1501 is configured to perform sample preparation and sample analysis, including nucleic acid sequencing.

[0323] The computer system 1501 comprises a central processing unit (CPU, also referred to herein as "processor" and "computer processor") 1505, which may be a single-core or multi-core processor or multiple processors for parallel processing. The computer system 1501 also comprises memory or storage 1510 (e.g., random access memory, read-only memory, flash memory), electronic storage 1515 (e.g., hard disk), communication interface 1520 (e.g., network adapter) for communicating with one or more other systems, and peripherals 1525 (e.g., cache, other memory, data storage, and / or electronic display adapter). The memory 1510, storage 1515, interface 1520, and peripherals 1525 are in communication with the CPU 1505 via a communication bus (solid lines), such as a motherboard. The storage 1515 may be a data storage device (or data repository) for storing data. The computer system 1501 may be operatively coupled to a computer network ("network") 1530 with the aid of the communication interface 1520. The network 1530 may be the Internet, an Internet and / or an extranet, or an intranet and / or an extranet in communication with the Internet. In some cases, the network 1530 is a telecommunications and / or data network. The network 1530 may comprise one or more computer servers that may enable distributed computing, such as cloud computing. The network 1530 may implement a peer-to-peer network, possibly with the aid of the computer system 1501, that may enable devices coupled to the computer system 1501 to operate as clients or servers.

[0324] The CPU 1505 may execute a series of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a storage location, such as the memory 1510. Examples of operations performed by the CPU 1505 may include fetch, decode, execute, and writeback.

[0325] The storage device 1515 may store files (e.g., drivers, libraries, and saved programs). The storage device 1515 may store programs and recorded sessions generated by users, as well as output associated with those programs. The storage device 1515 may store user data, such as user preferences and user programs. The computer system 1501 may optionally include one or more additional data storage devices external to the computer system 1501 (e.g., located on a remote server in communication with the computer system 1501 over an intranet or the Internet).

[0326] The computer system 1501 may communicate with one or more remote computer systems through the network 1530. For example, the computer system 1501 may communicate with a remote computer system of a user (e.g., an operator). Examples of remote computer systems include a personal computer (e.g., a portable PC), a slate or tablet PC (e.g., an Apple® iPad, a Samsung® Galaxy Tab), a phone, a smartphone (e.g., an Apple® iPhone, an Android-enabled device, a Blackberry®), or a personal digital assistant. A user may access the computer system 1501 through the network 1530.

[0327] Methods as described herein may be performed by machine (e.g., computer processor) executable code stored on an electronic memory location (e.g., memory 1510 or electronic storage 1515) of the computer system 1501. The machine executable or machine readable code may be provided in the form of software. During use, the code may be executed by the processor 1505. In some cases, the code may be retrieved from storage 1515 and stored on memory 1510 for ready access by the processor 1505. Sometimes, the electronic storage 1515 may be disabled and machine executable instructions stored on memory 1510.

[0328] The code may be pre-compiled and configured for use on a machine having a processor adapted to execute the code, or it may be compiled during run-time. The code may be provided in a programming language that may be selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[0329] Aspects of the systems and methods provided herein, such as the computer system 1501, may be embodied in programming. Various aspects of the technology may be considered as "products" or "articles of manufacture" in the form of machine (or processor) executable code and / or associated data, typically carried or embodied in some type of machine-readable medium. The machine-executable code may be stored in electronic storage devices, such memory (e.g., read-only memory, random access memory, flash memory) or hard disks. "Storage" type media may include any or all of the tangible memory of a computer, processor, etc. or their associated modules (e.g., various semiconductor memories, tape drives, disk drives, etc.), which may provide non-transient storage at any time for software programming. All or portions of the software may be communicated from time to time over the Internet or various other telecommunications networks. Such communication may enable, for example, loading of the software from one computer or processor to another, for example, from a management server or host computer to the computer platform of an application server. Thus, other types of media that may bear software elements include light waves, radio waves, and electromagnetic waves (e.g., those used across physical interfaces between local devices), via wired and optical land-line networks, and by various air-links. The physical elements that carry such waves (e.g., wired or wireless links, optical links, etc.) may also be considered media bearing software. As used herein, unless limited to non-transitory tangible "storage" media, terms such as computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

[0330] Thus, machine-readable media such as computer executable code may take many forms, including but not limited to tangible storage media, carrier wave media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks (such as any storage device in any computer, such as may be used to implement databases, etc., as shown in the drawings). Volatile storage media include dynamic memory (such as the main memory of such a computer platform). Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise the bus in a computer system. Carrier wave transmission media may take the form of electric or electromagnetic signals, or sound or light waves (such as those generated during radio frequency (RF) and infrared (IR) data communications). Thus, common forms of computer readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punch cards paper tape, any other physical recording medium having a pattern of holes, RAM, ROM, PROMs and EPROMs, FLASH-EPROMs, any other memory chips or cartridges, carrier waves carrying data or instructions, cables or links carrying such carrier waves, or any other medium from which a computer may read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0331] The computer system 1501 may, for example, include or be in communication with an electronic display that includes a user interface (UI) for providing one or more results of a sample analysis. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces. EXAMPLES

[0332] Example 1 - Prostate Cancer Prognosis and Treatment A blood sample is taken from a subject with prostate cancer. An oncologist has previously determined that the subject has stage II prostate cancer and has recommended treatment. Every six months after initial diagnosis, cell-free DNA is extracted, isolated, sequenced, and analyzed.

[0333] Cell-free DNA is extracted and isolated from blood using the Qiagen Qubit kit protocol. Carrier DNA is added to increase yield. DNA is amplified using PCR and universal primers. 10 ng of DNA is sequenced using a massively parallel sequencing approach using an Illumina MiSeq personal sequencer. Sequencing cell-free DNA covers 90% of the subject's genome.

[0334] Sequence data are assembled and analyzed for copy number variation. Sequence reads are mapped and compared to healthy individuals (controls). Based on the number of sequence reads, chromosomal regions are divided into 50 kb non-overlapping regions. Sequence reads are compared to each other and a ratio is determined for each mappable position.

[0335] A hidden Markov model is applied to convert the copy numbers into distinct states for each window.

[0336] A report is generated with the mapping genomic locations and copy number variations shown in FIG. 4A (for healthy individuals) and FIG. 4B for subjects with cancer.

[0337] These reports, compared to other profiles of subjects with known outcomes, suggest that this particular cancer is aggressive and resistant to treatment. The cell-free tumor burden is 21%. The subject is monitored for 18 months. At month 18, the copy number polymorphism profile begins to dramatically increase the cell-free tumor burden from 21% to 30%. Comparison is made to the genetic profiles of other prostate cancer subjects. This increase in copy number polymorphism is found to suggest that the prostate cancer is progressing from stage II to stage III. The original treatment regimen as prescribed is no longer treating the cancer. A new treatment is prescribed.

[0338] These reports are also transmitted and accessed electronically via the Internet. Analysis of the sequence data is performed at a location other than the subject's location. Reports are generated and transmitted to the subject's location. Through an Internet-enabled computer, subjects access reports reflecting their tumor burden (FIG. 4C). Example 2 - Prostate cancer remission and recurrence.

[0339] Blood samples are taken from prostate cancer survivors. The subjects have previously undergone multiple chemotherapy and radiation treatments. At the time of the study, the subjects exhibit no symptoms or health problems related to cancer. Standard scans and assays reveal that the subjects are cancer-free.

[0340] Cell-free DNA is extracted and isolated from blood using the Qiagen TruSeq kit protocol. Carrier DNA is added to increase yield. DNA is amplified using PCR and universal primers. 10 ng of DNA is sequenced using a massively parallel sequencing approach using an Illumina MiSeq personal sequencer. A ligation method is used to add 12-mer barcodes to individual molecules.

[0341] Sequence data are assembled and analyzed for copy number variation. Sequence reads are mapped and compared to healthy individuals (controls). Based on the number of sequence reads, chromosomal regions are divided into 40 kb non-overlapping regions. Sequence reads are compared to each other and a ratio is determined for each mappable position.

[0342] To help normalize bias from amplification, non-unique barcoded sequences are collapsed into a single read.

[0343] A hidden Markov model is applied to convert the copy numbers into distinct states for each window.

[0344] A report was generated showing the mapping genomic locations and copy number variations in Figure 5A for subjects whose cancer is in remission and Figure 5B for subjects whose cancer is in relapse.

[0345] This report, compared with other profiles of subjects with known outcomes, suggests that analysis of rare mutations for copy number variation detects a 5% cell-free tumor burden at month 18. The oncologist again prescribes treatment. Example 3 - Thyroid Cancer and Treatment

[0346] The subject is found to have stage IV thyroid cancer and undergoes standard treatment, including radiation therapy with I-131. CT scans are inconclusive as to whether the radiation therapy is destroying the cancerous mass. Blood is drawn before and after the final radiation session.

[0347] Extract and isolate cell-free DNA from blood using the Qiagen Qubit kit protocol. To increase yield, add a sample of non-specific bulk DNA to the sample preparation reaction.

[0348] In this thyroid cancer, the BRAF gene is known to be mutated at amino acid 600. Primers specific to the gene are used to selectively amplify BRAF DNA from a population of cell-free DNA. A 20-mer barcode is added to the parent molecule as a control for counting reads.

[0349] 10 ng of DNA is sequenced using a massively parallel sequencing approach using an Illumina MiSeq personal sequencer.

[0350] The sequence data is assembled and analyzed for the detection of copy number variations. The sequence reads are mapped and compared to healthy individuals (controls). The chromosomal regions are divided into 50 kb non-overlapping regions based on the number of sequence reads determined by counting the barcode sequences. The sequence reads are compared to each other and a ratio is determined for each mappable position.

[0351] A hidden Markov model is applied to convert the copy numbers into distinct states for each window.

[0352] A report is generated mapping genomic locations and copy number variations.

[0353] The reports generated before and after treatment are compared. The percentage of tumor cell mass increases sharply from 30% to 60% after the radiation session. The increase in tumor mass is determined to be an increase in necrosis of cancerous tissue relative to normal tissue as a result of treatment. The oncologist recommends that the subject continue with the prescribed treatment. Example 4 - Sensitivity of Rare Mutation Detection

[0354] To determine the detection range of rare mutations present in a population of DNA, mixing experiments are performed. Sequences of DNA (some containing wild-type copy numbers of genes TP53, HRAS and MET, and some containing copies with rare mutations in those same genes) are mixed in different ratios. DNA mixtures are prepared such that the ratio or percentage of mutant DNA to wild-type DNA ranges from 100% to 0.01%.

[0355] 10 ng of DNA is sequenced for each mixing experiment using a massively parallel sequencing approach using an Illumina MiSeq personal sequencer.

[0356] Assemble sequence data and analyze for rare mutation detection. Map sequence reads and compare them to a reference sequence (control). Determine frequency of variance for each mappable position based on number of sequence reads.

[0357] A hidden Markov model is applied to convert the frequency of variance for each mappable position into distinct states for the base position.

[0358] A report is generated (Figure 6A), which is the mapping genomic base position and the detection percentage of rare variants above the baseline determined by the reference sequence.

[0359] The results of various mixing experiments, ranging from 0.1% to 100%, are shown on a logarithmic scale graph, where the measured percentage of DNA with rare mutations is graphed as a function of the actual percentage of DNA with rare mutations (Figure 6B). Three genes are represented: TP53, HRAS and MET. A strong linear correlation is seen between the measured and expected rare mutation populations. Furthermore, a lower sensitivity threshold of approximately 0.1% DNA with rare mutations in the population of unmutated DNA is found using these experiments (Figure 6B). Example 5 - Detection of rare mutations in prostate cancer subjects

[0360] A subject is believed to have early stage prostate cancer. Other laboratory tests provide equivocal results. Blood is drawn from the subject and cell-free DNA is extracted, isolated, prepared, and sequenced.

[0361] A panel of various oncogenes and tumor suppressor genes are selected for selective amplification using TaqMan© PCR kits (Invitrogen) using gene-specific primers. The DNA regions to be amplified include DNA encompassing the PIK3CA and TP53 genes.

[0362] 10 ng of DNA is sequenced using a massively parallel sequencing approach using an Illumina MiSeq personal sequencer.

[0363] Sequence data are assembled and analyzed for rare mutation detection. Sequence reads are mapped and compared to a reference sequence (control). Based on the number of sequence reads, the frequency of variance for each mappable position is determined.

[0364] A hidden Markov model is applied to convert the frequency of distributions for each mappable position into distinct states for each base position.

[0365] A report is generated (Figure 7A) of the mapping genomic base position and the detection percentage of rare mutations above the baseline determined by the reference sequence. Rare mutations are found in two genes, PIK3CA and TP53, with an incidence of 5%, suggesting that the subject has early stage cancer. Treatment is initiated.

[0366] These reports are also transmitted and accessed electronically via the Internet. Analysis of the sequence data is performed at a location other than the subject's location. Reports are generated and transmitted to the subject's location. Through an Internet-enabled computer, subjects access reports reflecting their tumor burden (FIG. 7B). Example 6 - Detection of rare mutations in subjects with colorectal cancer

[0367] A subject is believed to have mid-stage colorectal cancer. Other laboratory tests provide equivocal results. Blood is drawn from the subject and cell-free DNA is extracted.

[0368] A single tube of 10 ng of cell-free genetic material extracted from plasma is used. The initial genetic material is converted into a set of tagged parent polynucleotides. This tagging included attaching tags necessary for sequencing, as well as non-unique identifiers to trace the parent nucleic acid of the progeny molecules back. This conversion is performed by an optimized ligation reaction as described above, and the conversion yield is confirmed by examining the size profile of the molecules after ligation. The conversion yield is measured as a percentage of the starting initial molecules with tags ligated at both ends. Conversion using this approach is performed with high efficiency, for example, at least 50%.

[0369] The tagged library is PCR amplified and enriched for genes most associated with colorectal cancer (e.g., KRAS, APC, TP53, etc.), and the resulting DNA is sequenced using a massively parallel sequencing approach using an Illumina MiSeq personal sequencer.

[0370] The sequence data is assembled and analyzed for the detection of rare mutations. The sequence reads are collapsed (and errors corrected during collapsing) into family groups belonging to the parent molecule and mapped with a reference sequence (control). Based on the number of sequence reads, the frequency of rare mutations (substitutions, insertions, deletions, etc.) and copy number variations and heterozygosity (when appropriate) are determined for each mappable position.

[0371] A report is generated, mapping genomic base positions and the percentage of rare mutations detected above baseline as determined by the reference sequence. Rare mutations are found in two genes, KRAS and FBXW7, with an incidence of 0.3-0.4%, suggesting that the subject has residual cancer. Treatment is initiated.

[0372] Additionally, these reports are transmitted and accessed electronically via the Internet. Analysis of the sequence data is performed at a location other than the subject's location. Reports are generated and transmitted to the subject's location. Through an Internet-enabled computer, subjects access reports reflecting their tumor burden.

[0373] Example 7 - Digital Sequencing Technology

[0374] The concentration of tumor-shedded nucleic acids is typically so low that current next-generation sequencing techniques can detect such signals only sporadically or only in patients with terminally high tumor burden. The main reason is that such techniques suffer from error rates and biases that can be orders of magnitude higher than those required to reliably detect novel genetic alterations associated with cancer in circulating DNA. A new sequencing technique, digital sequencing technology (DST), is presented here that enhances the sensitivity and specificity of detecting and quantifying tumor-derived rare nucleic acids among germline fragments by at least one to two orders of magnitude.

[0375] The structure of DST was inspired by state-of-the-art digital communication systems that strive to suppress the high noise and distortion caused by modern communication channels, allowing perfect transmission of digital information at very high data rates. Similarly, current next-generation workflows suffer from extremely high noise and distortion (due to sample preparation, PCR-based amplification and sequencing). Digital sequencing can eliminate the errors and distortions introduced by these processes, resulting in a near-perfect representation of all rare variants (including CNVs).

[0376] High diversity library preparation

[0377] Unlike conventional sequencing library preparation protocols, where most of the extracted circulating DNA fragments are lost due to inefficient library conversion, the workflow of our digital sequencing technology allows for conversion and sequencing of the majority of starting molecules. This is crucial for the detection of rare variants, since only a small number of somatically mutated molecules may be present in an entire 10 mL tube of blood. The developed efficient molecular biology conversion process allows for the highest possible sensitivity for the detection of rare variants.

[0378] Comprehensive, actionable cancer gene panels

[0379] The workflow designed based on the DST platform is adaptable and highly adjustable as the targeted region can be as small as a single exon or as large as an entire exome (or even the whole genome). The standard panel consists of coverage of all exons of 15 actionable cancer-related genes and "hot" exons (e.g., exons containing at least one or more reported somatic mutations in COSMIC) of an additional 36 onco- / tumor-suppressor genes.

[0380] Example 8: Analytical Studies

[0381] To study the performance of our technology, we evaluated its sensitivity in analytical samples. We added various amounts of LNCaP cancer cell line DNA to a background of normal cfDNA and were able to successfully detect somatic mutations down to a sensitivity of 0.1% (see Figure 13).

[0382] Preclinical trials

[0383] We investigated the concordance between circulating DNA and tumor gDNA in a mouse human xenograft model. In seven CTC-negative mice (each harboring one of two different human breast cancer tumors), all somatic mutations detected in tumor gDNA were also detected in mouse blood cfDNA using DST, further validating the utility of cfDNA for non-invasive tumor genetic profiling.

[0384] Pilot Clinical Study

[0385] Correlation of somatic mutations in tumor biopsies versus circulating DNA

[0386] We initiated a pilot study in human samples across a range of cancer types to investigate the concordance between tumor mutation profiles derived from circulating cell-free DNA and matched tumor biopsy samples. We found >93% concordance between tumor and cfDNA somatic mutation profiles in both colorectal and melanoma cancers across 14 patients (Table 1). [Table 1B-1] [Table 1B-2]

[0387] It should be understood from the foregoing that, although specific embodiments have been illustrated and described, various modifications thereto can be made and are contemplated herein. It is also intended that the present invention is not limited to the specific examples provided herein. Although the present invention has been described in light of the above specification, the description and illustration of the preferred embodiments herein are not meant to be construed in a limiting sense. It is also to be understood that all aspects of the present invention are not limited to the specific depictions, configurations or relative proportions shown herein, which depend upon a variety of conditions and variables. Various modifications in form and details of the embodiments of the present invention will be apparent to those skilled in the art. It is therefore contemplated that the present invention shall encompass any such modifications, variations and equivalents.

Claims

[Claim 1] A composition as described in the specification.

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