Methods for detecting nucleic acid variants

Abstract

The method described herein is for detecting short genetic variants in test sample.In some exemplary methods, the short genetic variants are determined or called by using one or more sequencing data sets obtained from test nucleic acid molecules or by using match scores, and the test sequencing data sets are determined by sequencing test nucleic acid molecules using non-terminal nucleotides that are provided in separate nucleotide flows according to flow cycle order.Also described herein is the method of sequencing test nucleic acid molecules using two or more different flow cycle orders and / or an extended flow cycle order that has five or more nucleotide flows per flow cycle.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Patent Application No. 62 / 842,534, filed March 3, 2019, and U.S. Provisional Patent Application No. 62 / 971,530, filed February 7, 2020, the contents of each of which are incorporated by reference in their entirety herein.

[0002] Submitting a sequence listing as an ASCII text file The following submission in an ASCII text file is incorporated by reference in its entirety herein: Sequence Listing in Computer Readable Form (CRF) (Filename: 165272000540SEQLIST.TXT, Recorded: April 27, 2020, Size: 5KB).

[0003] FIELD OF THEINVENTION Described herein are methods for sequencing polynucleotides, including methods for generating and / or analyzing sequencing data, including detection of genetic variants. [Background technology]

[0004] background Genetic variants in DNA samples can be detected by sequencing the DNA in the sample, aligning the sequence with a reference sequence, and evaluating the difference.High-confidence differences between the sequenced DNA and the reference sequence are called as variants of the organism from which the DNA sample is derived.Next-generation sequencing has provided research and clinical laboratories with the tools necessary for simultaneous sequencing of many different nucleic acid molecules in a single sample, generating a large amount of data to be analyzed.

[0005] In addition, sequencing by synthesis using reversible terminators (e.g., reversibly terminated, dye-labeled sequencing methods) provides a single distinct signal for each base, and therefore a single signal sequencing error can result in an erroneous variant call. In some cases, this can be overcome by high-depth sequencing, which effectively overwhelms erroneous calls with true positive signals, but sequencing at such high depths is costly and time-consuming. Summary of the Invention [Means for solving the problem]

[0006] There remains a need in the art for highly efficient and accurate base and variant calling protocols.

[0007] A brief summary Described herein is a method for detecting short genetic variants in a test sample containing nucleic acid molecules, which in certain embodiments can be a computer-implemented method.Described herein is also a system for carrying out such a method.Furthermore, described herein is a method for sequencing nucleic acid molecules.

[0008] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) selecting a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the flow positions correspond to nucleotide flows, when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order; (b) obtaining one or more test sequencing datasets, where each test sequencing dataset is associated with a test nucleic acid molecule, and each test nucleic acid molecule contains a gene associated with the short target genetic variant. The method includes the steps of: (a) determining, for each of the test nucleic acid molecules associated with the test sequencing dataset, a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence, or a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (b) calling the presence or absence of the short target gene variant in the test sample using the one or more determined match scores.

[0009] In some embodiments of the above methods, the obtaining step includes sequencing the test nucleic acid molecule using non-terminating nucleotides provided in a separate nucleotide flow according to a flow cycle order.

[0010] In some embodiments of the above method, the short target gene variants are preselected before calling the presence or absence of the short target gene variants in the test sample. In some embodiments, the short target gene variants are selected after calling the presence or absence of the short target gene variants in the test sample based on the confidence of the call. In some embodiments, the method includes generating a personalized biomarker panel for a subject associated with the test sample, the biomarker panel includes the short target gene variants.

[0011] In some embodiments of the above methods, the method further comprises selecting a flow cycle order.

[0012] In some embodiments, the target sequencing dataset is a predicted target sequencing dataset or the reference sequencing dataset is a predicted reference sequencing dataset. In some embodiments, the target sequencing dataset and the predicted reference sequencing dataset are obtained by in silico sequencing of the target sequence and the reference sequence.

[0013] In some embodiments of the above method, the target sequencing data set and the reference sequencing data differ at more than two non-contiguous flow positions. In some embodiments, the target sequencing data set and the reference sequencing data differ at more than two contiguous flow positions. In some embodiments, the target sequence and the reference sequence differ at X base positions, and the target sequencing data set and the reference sequencing data differ at (X+2) or more contiguous flow positions. In some embodiments, the difference at (X+2) flow positions comprises a difference between a value substantially equal to zero and a value substantially greater than zero. In some embodiments, the target sequencing data set and the reference sequencing data set differ over one or more flow cycles. In some embodiments, the flow signal comprises a base count indicating the number of bases of the test nucleic acid molecule sequenced at each flow position.

[0014] In some embodiments of the above method, the flow signal comprises a statistical parameter indicative of the likelihood of at least one base count at each flow position, where the base count represents the number of bases of the sequenced test nucleic acid molecule at the flow position. In some embodiments, the flow signal comprises a statistical parameter indicative of the likelihood of a plurality of base counts at each flow position, where each base count represents the number of bases of the sequenced test nucleic acid molecule at the flow position.

[0015] In some embodiments of the above method, step (c) comprises: (i) selecting a statistical parameter at each flow location in the test sequencing data set, the statistical parameter corresponding to the base count of the target sequence at that flow location, and determining a match score indicating the likelihood that the test sequencing data set matches the target sequence; or (ii) selecting a statistical parameter at each flow location in the test sequencing data set, the statistical parameter corresponding to the base count of the reference sequence at that flow location, and determining a match score indicating the likelihood that the test sequencing data set matches the reference sequence. In some embodiments, the match score determined in step (c) is the combined value of the selected statistical parameters across the flow locations in the test sequencing data set. In some embodiments, step (c) comprises determining a match score indicating the likelihood that the test sequencing data set matches the target sequence. In some embodiments, step (c) comprises determining a match score indicating the likelihood that the test sequencing data set matches the reference sequence.

[0016] In some embodiments of the above method, the one or more test sequencing data sets comprise a plurality of test sequencing data sets.In some embodiments, the presence or absence of short target gene variants is called separately for each of the one or more test sequencing data sets.In some embodiments, at least a portion of the plurality of test sequencing data sets are related to different test nucleic acid molecules that have different test sequencing start positions.

[0017] In some embodiments of the above method, the flow cycle sequence includes four separate flows repeated in the same order, hi some embodiments, the flow cycle sequence includes five or more separate flows.

[0018] In some embodiments of the above method, the method is a computer-implemented method.For example, in some embodiments, the computer-implemented method includes: using one or more processors to select short target gene variants; receiving one or more test sequencing data sets in one or more processors to obtain one or more test sequencing data sets; using one or more processors to determine one or more match scores; and using one or more processors to call the presence or absence of short target gene variants in test samples.

[0019] Also provided herein is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs that include instructions for performing the methods described above.

[0020] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) obtaining one or more first test sequencing datasets, each first test sequencing dataset associated with a different test nucleic acid molecule from the test sample, the first test sequencing dataset being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (b) obtaining one or more second test sequencing datasets, each second test sequencing dataset associated with the same test nucleic acid molecule as the first test sequencing dataset, the second test sequencing dataset being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; wherein a sequence of a non-terminating nucleotide sequence is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a second flow cycle order, wherein the first flow cycle order and the second flow cycle order are different, and the test sequencing dataset comprises flow signals at flow positions corresponding to the nucleotide flows; (c) determining a match score for one or more candidate sequences for each of the first sequencing dataset and the second sequencing dataset, wherein the match score indicates a likelihood that the candidate sequence from the first test sequencing dataset, the second test sequencing dataset, or both matches; and (d) calling the presence or absence of the short genetic variant in the test sample using the determined match score.

[0021] In some embodiments of the above methods, the method includes sequencing the test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a first flow cycle order, and sequencing the test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a second flow cycle order.

[0022] In some embodiments of the above method, the match score indicates the likelihood that the first test sequencing dataset matches the candidate sequence, or the likelihood that the second test sequencing dataset matches the candidate sequence. In some embodiments, the match score indicates the likelihood that both the first test sequencing dataset and the second sequencing dataset match the candidate sequence.

[0023] In some embodiments of the above method, the one or more candidate sequences include two or more different candidate sequences, and the method includes, for each of the nucleic acid molecules associated with the first sequencing data set and the second sequencing data set: selecting a candidate sequence from the two or more different candidate sequences, the selected candidate sequence having the highest likelihood match with the first test sequencing data set, the second test sequencing data set, or both; and calling the presence or absence of a short genetic variant in the test sample using the selected candidate sequence. In some embodiments, at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ at two or more flow positions according to the first flow cycle order or the second flow cycle order. In some embodiments, at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ at two or more flow positions according to both the first flow cycle order and the second flow cycle order. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more non-consecutive flow positions according to the first flow cycle order or the second flow cycle order. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more non-consecutive flow positions according to both the first flow cycle order and the second flow cycle order. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more consecutive flow positions according to the first flow cycle order or the second flow cycle order. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more consecutive flow positions according to both the first flow cycle order and the second flow cycle order.In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at three or more flow positions according to the first flow cycle order or the second flow cycle order. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at three or more flow positions according to both the first flow cycle order and the second flow cycle order. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at X base positions, and the test sequencing data set associated with the test nucleic acid molecule and at least one non-selected candidate sequence from two or more different candidate sequences differ at (X+2) or more flow positions according to the first flow cycle order or the second flow cycle order. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at X base positions, and the test sequencing data set associated with the test nucleic acid molecule and at least one non-selected candidate sequence from two or more different candidate sequences differ at (X+2) or more flow positions according to both the first flow cycle order and the second flow cycle order. In some embodiments, the difference at (X+2) flow positions comprises a difference between a value substantially equal to zero and a value substantially greater than zero. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ over one or more flow cycles according to the first flow cycle order or the second flow cycle order. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ over one or more flow cycles according to both the first flow cycle order and the second flow cycle order.

[0024] In some embodiments of the method, the flow signal includes a base count indicating the number of bases of the sequenced test nucleic acid molecule at each flow position. In some embodiments, the flow signal includes a statistical parameter indicating the likelihood of at least one base count at each flow position, the base count indicating the number of bases of the sequenced test nucleic acid molecule at the flow position. In some embodiments, the flow signal includes a statistical parameter indicating the likelihood of a plurality of base counts at each flow position, each base count indicating the number of bases of the sequenced test nucleic acid molecule at the flow position. In some embodiments, the step of determining the match score includes selecting a statistical parameter at each flow position in the first test sequencing data set and the second test sequencing data set, the statistical parameter corresponding to the base count of the candidate sequence at that flow position, for each of the one or more different candidate sequences. In some embodiments, the method includes generating a candidate sequencing data set for the one or more different candidate sequences, the candidate sequence including the base count of the candidate sequence at each flow position. In some embodiments, the candidate sequencing data set is generated in silico. In some embodiments, the match score is the combined value of a selected statistical parameter across flow positions in the first test sequencing data set and the second test sequencing data set.

[0025] In some embodiments of the above methods, at least a portion of the test nucleic acid molecules have different sequencing start positions.

[0026] In some embodiments of the above method, the method further comprises a step of selecting a short target gene variant, wherein the target sequencing dataset associated with the target sequence comprising the short target gene variant and the reference sequencing dataset associated with the reference sequence differ at two or more flow positions when obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order, the first flow cycle order and the second flow cycle order are different, and the flow positions correspond to the nucleotide flows, and the one or more candidate sequences comprise the target sequence and the reference sequence. In some embodiments, the short target gene variant is preselected before calling the presence or absence of the short target gene variant in the test sample. In some embodiments, the short target gene variant is selected based on the confidence of the call after calling the presence or absence of the short target gene variant in the test sample. In some embodiments, the method comprises a step of generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprises the short target gene variant present in the test sample. In some embodiments, the reference sequencing data set is obtained by determining the expected reference sequencing data set when the reference sequence is sequenced using non-terminating nucleotides provided in separate flows according to the first flow cycle order or the second flow cycle order.In some embodiments, the reference sequencing data set is obtained by determining the expected reference sequencing data set when the reference sequence is sequenced using non-terminating nucleotides provided in separate flows according to both the first flow cycle order and the second flow cycle order.In some embodiments, the target sequence and the reference sequence are different at two or more flow positions according to both the first flow cycle order and the second flow cycle order.In some embodiments, the target sequence and the reference sequence differ at two or more non-consecutive flow positions according to the first flow cycle order or the second flow cycle order. In some embodiments, the target sequence and the reference sequence differ at two or more non-consecutive flow positions according to both the first flow cycle order and the second flow cycle order. In some embodiments, the target sequence and the reference sequence differ at two or more consecutive flow positions according to the first flow cycle order or the second flow cycle order. In some embodiments, the target sequence and the reference sequence differ at two or more consecutive flow positions according to both the first flow cycle order and the second flow cycle order. In some embodiments, the target sequence and the reference sequence differ at three or more flow positions according to the first flow cycle order or the second flow cycle order. In some embodiments, the target sequence and the reference sequence differ at three or more flow positions according to both the first flow cycle order and the second flow cycle order. In some embodiments, the target sequence and the reference sequence differ over two or more flow cycles according to the first flow cycle order or the second flow cycle order. In some embodiments, the target sequence and the reference sequence differ over two or more flow cycles according to both the first flow cycle order and the second flow cycle order.

[0027] In some embodiments of the above method, the first flow cycle sequence or the second flow cycle sequence includes four separate flows repeated in the same order, hi some embodiments, the first flow cycle sequence or the second flow cycle sequence includes five or more separate flows repeated in the same order.

[0028] In some embodiments of the above methods, the method includes the steps of sequencing a test nucleic acid molecule comprising providing non-terminating nucleotides with separate nucleotide flows according to a first flow cycle order, extending the sequencing primer, and detecting the presence or absence of nucleotide incorporation into the sequencing primer after each nucleotide flow to generate a first test sequencing data set; removing the extended sequencing primer; and sequencing the same test nucleic acid molecule comprising providing non-terminating nucleotides with separate nucleotide flows according to a second flow cycle order, extending the sequencing primer, and detecting the presence or absence of nucleotide incorporation into the sequencing primer after each nucleotide flow to generate a second test sequencing data set.

[0029] In some embodiments of the above method, the method is a computer-implemented method.For example, in some embodiments, the computer-implemented method includes: receiving one or more first sequencing data sets in one or more processors; receiving one or more first sequencing data sets in one or more processors; using one or more processors to determine match score; and using one or more processors to call the presence or absence of short target gene variants in test samples.

[0030] Also described herein are systems that include one or more processors and a non-transitory computer readable medium storing one or more programs that include instructions for performing any of the methods described above.

[0031] In some embodiments of any of the above methods or systems, the separate flows include a single base type.

[0032] In some embodiments of any of the above methods or systems, at least one of the separate flows includes two or three different base types.

[0033] In some embodiments of any of the above methods or systems, the method includes generating or updating a variant call file indicating the presence, identity or absence of short genetic variants in the test sample.

[0034] In some embodiments of any of the above methods or systems, the method includes generating a report indicating the presence, identity, or absence of the short genetic variants in the test sample. In some embodiments, the report includes a textual, probabilistic, numerical, or graphical output indicating the presence, identity, or absence of the short genetic variants in the test sample. In some embodiments, the method includes providing the report to the patient or to the patient's medical practitioner.

[0035] In some embodiments of any of the above methods or systems, the short genetic variant comprises a single nucleotide polymorphism or an indel.

[0036] In some embodiments of any of the above methods or systems, the short genetic variant comprises an indel.

[0037] In some embodiments of any of the above methods or systems, the test sample comprises fragmented DNA.

[0038] In some embodiments of any of the above methods or systems, the test sample comprises cell-free DNA. In some embodiments, the cell-free DNA comprises circulating tumor DNA (ctDNA).

[0039] In some embodiments, the method of sequencing a nucleic acid molecule comprises hybridizing the nucleic acid molecule with a primer to form a hybridized template; extending the primer using labeled non-terminating nucleotides provided in separate nucleotide flows according to a repetitive flow cycle sequence comprising five or more separate nucleotide flows; and detecting a signal from the incorporated labeled nucleotide or the absence of a signal when the primer is extended by the nucleotide flow. In some embodiments, the method comprises detecting a signal or the absence of a signal after each nucleotide flow. In some embodiments, the method comprises sequencing a plurality of nucleic acid molecules. In some embodiments, the nucleic acid molecules in the plurality have different sequencing start positions relative to a locus. In some embodiments, the test sample is cell-free DNA. In some embodiments, the cell-free DNA comprises circulating tumor DNA (ctDNA). In some embodiments, the flow cycle sequence induces signal changes for 50% or more of possible SNP permutations at 5% or more of random sequencing start positions at more than two flow positions. In some embodiments, the induced signal change is a change in signal intensity, or a new substantially zero (or new zero) or a new substantially non-zero (or new non-zero) signal. In some embodiments, the induced signal change is a new substantially zero (or new zero) or a new substantially non-zero (or new non-zero) signal. In some embodiments, the flow cycle sequence has an efficiency of base incorporation per flow of 0.6 or greater. In an embodiment of the present invention, for example, the following items are provided: (Item 1) 1. A method for detecting short genetic variants in a test sample, comprising: (a) selecting short target gene variants, wherein a target sequencing dataset associated with a target sequence comprising said short target gene variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions when said target sequencing dataset and said reference sequencing dataset were obtained by sequencing said target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order comprising a plurality of flow positions, said flow positions corresponding to said nucleotide flows; (b) obtaining one or more test sequencing datasets, each test sequencing dataset associated with a test nucleic acid molecule, each test nucleic acid molecule at least partially overlapping a locus associated with said short target gene variant and derived from said test sample, said one or more test sequencing datasets being determined by sequencing a respective test nucleic acid molecule using non-terminating nucleotides provided in separate nucleotide flows according to said flow cycle order, each test sequencing dataset comprising flow signals at said plurality of flow locations; (c) determining, for each test nucleic acid molecule associated with a test sequencing data set, a match score indicative of the likelihood that the test sequencing data set associated with the nucleic acid molecule matches the target sequence or the match score indicative of the likelihood that the test sequencing data set associated with the nucleic acid molecule matches the reference sequence; and (d) calling the presence or absence of said short target gene variants in said test sample using the one or more determined match scores. The method includes: (Item 2) 2. The method of claim 1, wherein the obtaining step comprises sequencing the test nucleic acid molecule using non-terminating nucleotides provided in a separate nucleotide flow according to the flow cycle order. (Item 3) 3. The method of claim 1 or 2, wherein the short target gene variant is preselected prior to calling the presence or absence of the short target gene variant in the test sample. (Item 4) 3. The method of claim 1 or 2, wherein the short target gene variants are selected after calling the presence or absence of the short target gene variants in the test sample based on the confidence of the call. (Item 5) 5. The method of any one of items 1 to 4, comprising generating an individualized biomarker panel for a subject associated with the test sample, wherein the biomarker panel comprises the short target gene variants. (Item 6) 6. The method according to any one of items 1 to 5, comprising a step of selecting the flow cycle order. (Item 7) 7. The method of any one of items 1 to 6, wherein the target sequencing dataset and the reference sequencing dataset are obtained by in silico sequencing of the target sequence and the reference sequence. (Item 8) 8. The method of any one of items 1 to 7, wherein the target sequencing data set and the reference sequencing data differ at more than two non-contiguous flow locations. (Item 9) 9. The method of any one of items 1 to 8, wherein the target sequencing data set and the reference sequencing data differ at more than two consecutive flow locations. (Item 10) 10. The method of any one of items 1 to 9, wherein the target sequence and the reference sequence differ at X base positions, and the target sequencing data set and the reference sequencing data set differ at (X+2) or more consecutive flow positions. (Item 11) 11. The method of any one of items 1 to 10, wherein the target sequencing dataset and the reference sequencing dataset differ over one or more flow cycles. (Item 12) 12. The method of any one of items 1 to 11, wherein the flow signal comprises a base count indicating the number of bases of the test nucleic acid molecule that were sequenced at each flow position. (Item 13) 13. The method of any one of items 1 to 12, wherein the flow signal comprises a statistical parameter indicative of the likelihood of at least one base count at each flow location, the base count indicative of the number of bases of the test nucleic acid molecule sequenced at that flow location. (Item 14) 14. The method of any one of items 1 to 13, wherein the flow signal comprises a statistical parameter indicative of the likelihood of a plurality of base counts at each flow location, each base count indicative of the number of bases of the test nucleic acid molecule sequenced at that flow location. (Item 15) Step (c) selecting a statistical parameter at each flow location in the test sequencing data set, the statistical parameter corresponding to a base count of the target sequence at that flow location, and determining the match score indicating the likelihood that the test sequencing data set matches the target sequence; or selecting a statistical parameter at each flow location in the test sequencing data set, the statistical parameter corresponding to a base count of the reference sequence at that flow location, and determining the match score indicating the likelihood that the test sequencing data set matches the reference sequence. Item 15. The method according to item 14, comprising: (Item 16) 16. The method of claim 15, wherein the match score determined in step (c) is a combined value of selected statistical parameters across the flow positions in the test sequencing dataset. (Item 17) 17. The method of any one of items 1 to 16, wherein step (c) comprises determining the match score indicating the likelihood that the test sequencing dataset matches the target sequence. (Item 18) 18. The method of any one of items 1 to 17, wherein step (c) comprises determining the match score indicating the likelihood that the test sequencing dataset matches the reference sequence. (Item 19) 19. The method of any one of items 1 to 18, wherein the one or more test sequencing datasets comprise a plurality of test sequencing datasets. (Item 20) 20. The method of claim 19, wherein the presence or absence of the short target gene variant is called separately for each of the one or more test sequencing datasets. (Item 21) 21. The method of claim 19 or 20, wherein at least a portion of the plurality of test sequencing datasets relate to different test nucleic acid molecules having different test sequencing start positions. (Item 22) 22. The method according to any one of the preceding claims, wherein the flow cycle sequence comprises four separate flows repeated in the same sequence. (Item 23) 23. The method according to any one of the preceding claims, wherein the flow cycle sequence comprises five or more separate flows. (Item 24) selecting said short target gene variants using one or more processors; obtaining the one or more test sequencing data sets by receiving the one or more test sequencing data sets at the one or more processors; determining the one or more match scores using the one or more processors; and calling the presence or absence of said short target gene variants in said test sample using said one or more processors. 24. The method of any one of items 1 to 23, wherein the method is a computer-implemented method comprising: (Item 25) 1. A method for detecting short genetic variants in a test sample, comprising: (a) obtaining one or more first test sequencing datasets, each first test sequencing dataset associated with a different test nucleic acid molecule from the test sample, the first test sequencing datasets being determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (b) obtaining one or more second test sequencing datasets, each second test sequencing dataset associated with the same test nucleic acid molecules as the first test sequencing dataset, the second test sequencing datasets being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order, the first flow cycle order and the second flow cycle order being different, and the test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (c) determining a match score for each of the first and second sequencing datasets with one or more candidate sequences, the match score indicating the likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences; and (d) calling the presence or absence of the short genetic variant in the test sample using the determined match score. The method includes: (Item 26) 26. The method of claim 25, comprising sequencing the test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to the first flow cycle order, and sequencing the test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to the second flow cycle order. (Item 27) 27. The method of claim 25 or 26, wherein the match score indicates the likelihood that the first test sequencing dataset matches the candidate sequence or the likelihood that the second test sequencing dataset matches the candidate sequence. (Item 28) 27. The method of claim 25 or 26, wherein the match score indicates the likelihood that both the first test sequencing dataset and the second sequencing dataset match the candidate sequence. (Item 29) The one or more candidate sequences include two or more different candidate sequences, and the method comprises, for each of the nucleic acid molecules associated with the first sequencing dataset and the second sequencing dataset: selecting a candidate sequence from the two or more different candidate sequences, wherein the selected candidate sequence has a highest likelihood match with the first test sequencing data set, the second test sequencing data set, or both; and using the selected candidate sequences to call the presence or absence of the short genetic variants in the test sample. 29. The method according to any one of items 25 to 28, comprising: (Item 30) 30. The method of claim 29, wherein at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ at two or more flow positions according to the first flow cycle order or the second flow cycle order. (Item 31) 30. The method of claim 29, wherein at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ at two or more non-consecutive flow positions according to the first flow cycle order or the second flow cycle order. (Item 32) 30. The method of claim 29, wherein at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ at three or more flow positions according to the first flow cycle order or the second flow cycle order. (Item 33) 30. The method of claim 29, wherein at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ at X base positions, and a test sequencing data set associated with the test nucleic acid molecule and at least one non-selected candidate sequence from the two or more different candidate sequences differ at (X+2) or more flow positions according to the first flow cycle order or the second flow cycle order. (Item 34) 30. The method of claim 29, wherein at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ over one or more flow cycles according to the first flow cycle order or the second flow cycle order. (Item 35) 35. The method of any one of items 22 to 34, wherein the flow signal comprises a base count indicating the number of bases of the test nucleic acid molecule that were sequenced at each flow position. (Item 36) 36. The method of any one of items 25 to 35, wherein the flow signal comprises a statistical parameter indicative of the likelihood of at least one base count at each flow position, the base count indicative of the number of bases of the test nucleic acid molecule sequenced at that flow position. (Item 37) 37. The method of any one of items 25 to 36, wherein the flow signal comprises a statistical parameter indicative of the likelihood of a plurality of base counts at each flow location, each base count indicative of the number of bases of the test nucleic acid molecule sequenced at that flow location. (Item 38) 38. The method of claim 37, wherein determining the match score comprises selecting, for each of the one or more different candidate sequences, a statistical parameter at each flow location in the first test sequencing data set and the second test sequencing data set, the statistical parameter corresponding to a base count at that flow location of the candidate sequence. (Item 39) 38. The method of claim 37, comprising generating, for the one or more different candidate sequences, a candidate sequencing data set comprising base counts of the candidate sequence at each flow location. (Item 40) 40. The method of claim 39, wherein the candidate sequencing dataset is generated in silico. (Item 41) 41. The method of any one of items 37 to 40, wherein the match score is a combined value of a selected statistical parameter across flow positions in the first test sequencing data set and the second test sequencing data set. (Item 42) 42. The method of any one of items 25 to 41, wherein at least a portion of the test nucleic acid molecules have different sequencing start positions. (Item 43) selecting short target gene variants, wherein a target sequencing dataset associated with a target sequence comprising said short target gene variant and a reference sequencing dataset associated with a reference sequence differ at two or more flow positions when said target sequencing dataset and said reference sequencing dataset were obtained by sequencing said target sequence using non-terminating nucleotides provided in separate nucleotide flows according to said first flow cycle order or said second flow cycle order, and wherein said first flow cycle order and said second flow cycle order are different and said flow positions correspond to said nucleotide flows; The one or more candidate sequences include the target sequence and the reference sequence. 43. The method according to any one of items 25 to 42. (Item 44) 44. The method of claim 43, wherein the short target gene variant is preselected prior to calling the presence or absence of the short target gene variant in the test sample. (Item 45) 44. The method of claim 43, wherein the short target gene variants are selected after calling the presence or absence of the short target gene variants in the test sample based on the confidence of the call. (Item 46) 46. ​​The method of claim 43, further comprising generating an individualized biomarker panel for a subject associated with the test sample, wherein the biomarker panel comprises the short target gene variants present in the test sample. (Item 47) 47. The method of any one of items 43 to 46, wherein the reference sequencing dataset is obtained by determining an expected reference sequencing dataset when the reference sequence is sequenced using non-terminating nucleotides provided in a separate flow according to the first flow cycle order or the second flow cycle order. (Item 48) 47. The method of any one of items 43 to 46, wherein the target sequence and the reference sequence differ at two or more flow positions according to both the first flow cycle order and the second flow cycle order. (Item 49) 47. The method of any one of items 43 to 46, wherein the target sequence and the reference sequence differ at two or more non-consecutive flow positions according to the flow cycle order of the first flow. (Item 50) 47. The method of any one of items 43 to 46, wherein the target sequence and the reference sequence differ at three or more flow positions according to the first flow cycle order or the second flow cycle order. (Item 51) 47. The method of any one of items 43 to 46, wherein the target sequence and the reference sequence differ over one or more flow cycles according to the first flow cycle order or the second flow cycle order. (Item 52) 52. The method according to any one of items 43 to 51, wherein the first flow cycle sequence or the second flow cycle sequence comprises four separate flows repeated in the same sequence. (Item 53) 52. The method according to any one of items 43 to 51, wherein the first flow cycle sequence or the second flow cycle sequence comprises five or more separate flows repeated in the same sequence. (Item 54) sequencing the test nucleic acid molecule, the step comprising providing non-terminating nucleotides with separate nucleotide flows according to the first flow cycle order, extending a sequencing primer, and detecting the presence or absence of nucleotide incorporation into the sequencing primer after each nucleotide flow to generate the first test sequencing data set; removing the extended sequencing primer; and Sequencing the same test nucleic acid molecule, comprising providing non-terminating nucleotides in separate nucleotide flows according to the second flow cycle order, extending a sequencing primer, and detecting the presence or absence of nucleotide incorporation into the sequencing primer after each nucleotide flow to generate the second test sequencing data set. 54. The method according to any one of items 25 to 53, comprising: (Item 55) receiving the one or more first sequencing datasets at one or more processors; receiving the one or more first sequencing datasets at the one or more processors; determining the match score using the one or more processors; and calling the presence or absence of said short target gene variants in said test sample using said one or more processors. 55. The method of any one of items 25 to 54, wherein the method is a computer-implemented method comprising: (Item 56) 56. The method or system of any one of the preceding claims, wherein the separate flows comprise a single base type. (Item 57) 57. The method or system of any one of the preceding claims, wherein at least one of the separate flows comprises two or three different base types. (Item 58) 58. The method or system of any one of the preceding claims, comprising generating or updating a variant call file indicating the presence, identity or absence of the short genetic variants in the test sample. (Item 59) 59. The method or system of any one of the preceding items, comprising generating a report indicating the presence, identity or absence of said short genetic variants in said test sample. (Item 60) 60. The method or system of claim 59, wherein the report comprises a textual, probabilistic, numerical or graphical output indicating the presence, identity or absence of the short genetic variants in the test sample. (Item 61) 61. The method or system of claim 59 or 60, further comprising providing the report to a patient or to a medical representative of the patient. (Item 62) 62. The method or system of any one of the preceding claims, wherein the short genetic variants comprise single nucleotide polymorphisms or indels. (Item 63) 1. A method for sequencing a nucleic acid molecule, comprising: hybridizing the nucleic acid molecule with a primer to form a hybridized template; extending the primer using labeled non-terminating nucleotides provided in separate nucleotide flows according to a repetitive flow cycle sequence comprising five or more separate nucleotide flows; and detecting a signal from the incorporated labeled nucleotide, or the absence of a signal when the primer is extended by the nucleotide flow. A method comprising: (Item 64) 64. The method of claim 63, comprising detecting the signal or the absence of the signal after each nucleotide flow. (Item 65) 65. The method of claim 63 or 64, comprising sequencing a plurality of nucleic acid molecules. (Item 66) 66. The method of claim 65, wherein the nucleic acid molecules of the plurality have different sequencing start positions for a locus. (Item 67) 67. The method of any one of items 63 to 66, wherein the flow cycle sequence induces signal changes for 50% or more of the possible SNP permutations at 5% or more of the random sequencing start positions at more than two flow positions. (Item 68) 68. The method of any one of items 63 to 67, wherein the flow cycle sequence has an efficiency of base incorporation per flow of 0.6 or more. (Item 69) one or more processors; A non-transitory computer-readable medium storing one or more programs including instructions for carrying out the method according to any one of items 1 to 62. A system including: [Brief description of the drawings]

[0040] [Figure 1A] 1A shows sequencing data obtained by extending a primer with the sequence TATGGTCGTCGA (SEQ ID NO: 1) using a repeated flow cycle sequence of TACG. This sequencing data is representative of the extended primer strand, and the sequence information of the complementary template strand can be easily determined, which is effectively equivalent.

[0041] [Figure 1B] FIG. 1B shows the sequencing data shown in FIG. 1A with the most likely sequences selected based on highest likelihood at each flow location and from which the sequencing data was obtained (as indicated by an asterisk).

[0042] [Figure 1C] Figure 1C shows the sequencing data shown in Figure 1A with traces representing two different candidate sequences: TATGGTCATCGA (SEQ ID NO:2) (filled circles) and TATGGTCGTCGA (SEQ ID NO:1) (open circles). The likelihood that the sequencing data matches a given sequence can be determined as the product of the likelihood that each flow position matches a candidate sequence.

[0043] [Figure 2-1] FIG. 2A shows an alignment of sequencing reads R1 (SEQ ID NO: 1), R2 (SEQ ID NO: 3) and R3 (SEQ ID NO: 4) (each represented by the sequence of an extended primer) aligned with two candidate sequences H1 (SEQ ID NO: 5) and H2 (SEQ ID NO: 6) (each represented by their complementary sequence). FIG. 2B shows sequencing data corresponding to R1, with traces representing H1 (filled circle) and H2 (open circle). FIG. 2C shows sequencing data corresponding to R2, with traces representing H1 (filled circle) and H2 (open circle). FIG. 2D shows sequencing data corresponding to R3, with traces representing H1 (filled circle) and H2 (open circle). [Figure 2-2] FIG. 2A shows an alignment of sequencing reads R1 (SEQ ID NO: 1), R2 (SEQ ID NO: 3) and R3 (SEQ ID NO: 4) (each represented by the sequence of an extended primer) aligned with two candidate sequences H1 (SEQ ID NO: 5) and H2 (SEQ ID NO: 6) (each represented by their complementary sequence). FIG. 2B shows sequencing data corresponding to R1, with traces representing H1 (filled circle) and H2 (open circle). FIG. 2C shows sequencing data corresponding to R2, with traces representing H1 (filled circle) and H2 (open circle). FIG. 2D shows sequencing data corresponding to R3, with traces representing H1 (filled circle) and H2 (open circle).

[0044] [Diagram 3] FIG. 3 shows a flow chart of an exemplary method for detecting short genetic variants in a test sample.

[0045] [Figure 4]FIG. 4A shows sequencing data from a nucleic acid molecule having an extended primer sequence of TATGGTCGTCGA (SEQ ID NO: 1) obtained by sequencing the nucleic acid molecule using a first flow cycle order (TACG), and FIG. 4B shows sequencing data obtained by sequencing the same nucleic acid molecule using a second flow cycle order (AGCT). Additionally, each of FIG. 4A and FIG. 4B shows traces from a first candidate sequence TATGGTCGTCGA (SEQ ID NO: 1) (solid circle) and a second candidate sequence TATGGTCATCGA (SEQ ID NO: 2) (open circle). As shown in FIG. 4A and FIG. 4B, differences in flow cycle order can greatly change the signal detected at a given flow position, so using a flow cycle that is better for the context of the variant can detect more significant signal differences.

[0046] [Diagram 5] FIG. 5 shows another exemplary method for detecting the presence or absence of short genetic variants in a test sample.

[0047] [Figure 6] FIG. 6 shows another exemplary method for detecting the presence or absence of short genetic variants in a test sample.

[0048] [Figure 7] FIG. 7 illustrates an example of a computing device, according to one embodiment, that can be used to perform the methods described herein.

[0049] [Figure 8]8 shows sequencing data from a hypothetical nucleic acid molecule sequenced using the ATGC flow cycle order. Potential haplotype sequences TATGGTCG-TCGA (SEQ ID NO: 7) (H1) and TATGGTCGATCG (SEQ ID NO: 8) (H2) can be used to generate a trace, where H1 has a one-base deletion compared to H2. The sequencing data has a better match with the H2 candidate sequence, and no indels are called in this sequence.

[0050] [Figure 9] Figure 9 shows the sensitivity of detection of SNP permutations resulting in random sequencing initiation positions for four exemplary flow cycle sequences (including three of these that are extended flow cycle sequences). In Figure 9, the x-axis shows the fraction of flow phases (or fragmentation initiation positions) and the y-axis shows the fraction of SNP permutations that induced signal changes at more than two flow positions. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0051] Described herein is a method for detecting one or more short genetic variants, such as single nucleotide polymorphisms (SNPs), multi-nucleotide polymorphisms (MNPs) or indels, in a test sample derived from a subject.The test sequence data related to the test nucleic acid molecule from the test sample is analyzed to determine the match between the test sequence data and another sequence (e.g., test sequence, candidate sequence (or candidate haplotype sequence and / or reference sequence)), and can be expressed by determining a match score indicating the closeness of the match (e.g., given the test sequence data, the likelihood that the test sequence data is generated from the nucleic acid molecule of the comparison sequence).Therefore, the match score can be used to call the presence or identity, or the absence, of the short genetic variant in the test sample.

[0052] The test sequencing data set is uniquely structured to provide for computationally efficient analysis. For example, a test sequencing data set can be generated by sequencing a test nucleic acid molecule using non-terminating nucleotides provided in separate nucleotide flows according to flow cycle order. Thus, a test sequencing data set of a nucleic acid molecule includes flow signals at flow positions each corresponding to a flow of a particular nucleotide. Using this uniquely structured data set, a nucleic acid molecule (singular) [or molecule (s)] can be analyzed in "flow space" rather than "base space" (also called "nucleotide space" or "sequence space"). Flow space data relies on additional information about the flow cycle order that base space data does not possess. Analysis of data collected in flow space provides at least two advantages over analysis of data converted to or collected in base space. First, the most prevalent variant types (substitution SNPs) in the test nucleic acid molecule result in two or more distinctly different flow signals (which may propagate through or beyond the entire flow cycle) when compared to the reference sequence in flow space, but only one data signal is available when analyzing the sequence in base space. That is, in base space, each base position is associated with a single signal, and a variant base affects only the signal of the variant base and not the neighboring signals. In flow space, a variant can affect multiple flow positions, and for a particular variant, the variant can induce a shift in the subsequent flowgram signal compared to the reference sequence, thereby substantially enhancing the continuous detection of the variant. Second, the flow space data can be analyzed to determine a match with one or more candidate flow space sequences without directly aligning the sequence of the test nucleic acid molecule with one or more candidate sequences. Sequence alignment is computationally expensive, but it can be simplified using the match analysis described herein.

[0053] The multiple signal indicators in flow space for a given genetic variant increase variant calling accuracy compared to a single signal indicator that can be identified in base space analysis. Furthermore, the greater the number of flow signal differences, the greater the likelihood that a variant call will be detected. As further discussed herein, in certain situations, it is desirable to call preselected variants with high confidence, and these variants and / or flow orders can be selected to ensure that a desired number of flow signal differences are generated to call genetic variants with high confidence. The sequencing data set of nucleic acid molecules can be compared with the candidate sequence to determine a match score that indicates the likelihood that the test sequencing data set matches the candidate sequence.

[0054] Alignment of determined sequences with candidate sequences (e.g., candidate haplotype sequences) in base space is computationally expensive and is currently the predominant computationally intensive step in the Genome Analysis Toolkit (GATK) HaplotypeCaller. The PairHMM module in HaplotypeCaller aligns each sequencing read with each haplotype and uses the base quality as an estimate of error to determine the likelihood of the haplotype for which the sequencing read was derived. However, the structure of the dataset used in the methods described herein preserves error mode likelihood, which makes variant calling computationally more efficient. For example, the likelihood of a given genotype can be simply determined as the product of the likelihoods at each flow position that align with a sequence with that genotype. This flow space determination likelihood can replace the PairHMM module in HaplotypeCaller with a more computationally efficient variant call.

[0055] The flow signal at any flow position in a sequencing data set is flow order dependent, since the flow order used to sequence a nucleic acid molecule at any base position can affect the flow signal at that position. As further described herein, this discovery can be exploited in one or more ways. First, random fragmentation (in vivo fragmentation, e.g., cell-free DNA, or in vitro fragmentation, e.g., by sonication or enzymatic digestion) of overlapping nucleic acid molecules at the same locus results in multiple different sequencing start sites (relative to the locus) for those nucleic acid molecules. In some cases, different flow contexts are available at the locus (e.g., when resequencing with different flow orders, or when using quasi-periodic flow orders). Thus, variants at loci can be accurately detected based on single nucleic acid molecules using high sensitivity flow signals for the variants (e.g., using two or more flow signal differences compared to a reference or non-selected candidate sequence), even if other nucleic acid molecules result in less reliable signals (e.g., a single flow signal change). Second, a given nucleic acid molecule can be sequenced using a first flow order and resequenced using a second (different) flow order, resulting in a different flow sequence context across the nucleic acid molecule. Even if a nucleic acid molecule with a variant has a low likelihood match with a candidate sequence with the variant using one flow order, the nucleic acid molecule can have a high likelihood match with the candidate sequence using a second flow order. Third, the flow order can be an extended flow cycle (e.g., there are more than four base types in the cycle), meaning that the flow order is not simply a periodic four-flow repeat of the four base types A, C, T and G.In some cases, the repeat unit is longer than four bases, for example, a pattern that includes all possible two-base flow sequences (i.e., all XY pairs within the repeat unit, where X is all four bases and Y is each of the non-X bases) or three-base flow sequences (i.e., all possible XYZ permutations within the repeat unit). Fourth, flow sequencing sequences can be selected to target specific genetic variants.

[0056] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) obtaining one or more test sequencing datasets, each test sequencing dataset associated with a test nucleic acid molecule derived from the test sample, the test sequencing datasets being generated by sequencing the test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a flow order, and the test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (b) determining, for each of the test nucleic acid molecules associated with the test sequencing datasets, a match score indicative of the likelihood that the test sequencing dataset matches one or more candidate sequences; and (c) calling the presence or absence of a short target genetic variant in the test sample using the one or more determined match scores.

[0057] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) selecting a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the flow positions correspond to nucleotide flows, when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order; (b) obtaining one or more test sequencing datasets, where each test sequencing dataset is associated with a test nucleic acid molecule, and each test nucleic acid molecule contains a gene associated with the short target genetic variant. The method includes the steps of: (a) determining, for each of the test nucleic acid molecules associated with the test sequencing dataset, a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence, or a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (b) calling the presence or absence of the short target gene variant in the test sample using the one or more determined match scores.

[0058] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) obtaining one or more first test sequencing datasets, each first test sequencing dataset associated with a different test nucleic acid molecule from the test sample, the first test sequencing dataset being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (b) obtaining one or more second test sequencing datasets, each second test sequencing dataset associated with the same test nucleic acid molecule as the first test sequencing dataset, the second test sequencing dataset being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; wherein a sequence of a non-terminating nucleotide sequence is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a second flow cycle order, wherein the first flow cycle order and the second flow cycle order are different, and the test sequencing dataset comprises flow signals at flow positions corresponding to the nucleotide flows; (c) determining a match score for one or more candidate sequences for each of the first sequencing dataset and the second sequencing dataset, wherein the match score indicates a likelihood that the candidate sequence from the first test sequencing dataset, the second test sequencing dataset, or both matches; and (d) calling the presence or absence of the short genetic variant in the test sample using the determined match score.

[0059] The methods described herein may be computer-implemented methods, where one or more steps of the methods may be performed using, for example, one or more computer processors.

[0060] Also provided herein is a non-transitory computer readable storage medium storing one or more programs that, when executed by one or more processors of an electronic device, include instructions that cause the electronic device to perform any one or more of the methods described herein.

[0061] Further described herein is an electronic device including one or more processors, a memory, and one or more programs stored in the memory, the one or more programs configured to be executed by the one or more processors. The one or more programs may include instructions for performing any one or more of the methods described herein.

[0062] Also described herein is the method of sequencing nucleic acid molecule.For example, the method of sequencing nucleic acid molecule can include: hybridizing nucleic acid molecule with primer to form hybridized template; extending primer using labeled non-terminal nucleotide provided in separate nucleotide flow according to a repeating flow cycle sequence comprising 5 or more separate nucleotide flow; and detecting the signal from the incorporated labeled nucleotide, or detecting the absence of signal when primer is extended by nucleotide flow. definition

[0063] As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0064] Reference herein to "about" a value or parameter includes (and describes) a variation about that value or parameter itself. For example, a reference to "about X" includes the description of "X."

[0065] "Expected sequencing data" or "expected sequencing data set" for a given sequence refers to the calculated sequencing data that would be generated if the sequence were sequenced using non-terminating nucleotides provided in separate nucleotide flows according to flow order.Expected sequencing data set or expected sequencing data set can be determined, for example, by computer modeling (i.e., in silico).

[0066] "Flow order" refers to the order of the separate nucleotide flows used to sequence nucleic acid molecules using non-terminating nucleotides. The flow order can be divided into cycles of repeating units, and the flow order of the repeating units is called "flow cycle order". "Flow position" refers to the sequential position of a given separate nucleotide flow during the sequencing process.

[0067] The terms "individual," "patient," and "subject" are used synonymously and refer to animals, including humans.

[0068] The term "label" as used herein refers to a detectable moiety that is coupled or can be coupled with another moiety, such as a nucleotide or nucleotide analog. The label can emit or change the signal delivered to the label, and thus the presence or absence of the label can be detected. In some cases, the coupling can be via a linker, and the linker can be cleavable, for example, photocleavable (e.g., cleavable under ultraviolet light), chemically cleavable (e.g., by a reducing agent such as dithiothreitol (DTT), tris(2-carboxyethyl)phosphine (TCEP)), or enzymatically cleavable (e.g., by an esterase, lipase, peptidase, or protease). In some embodiments, the label is a fluorophore.

[0069] "Non-terminating nucleotide" is a nucleic acid moiety that can be attached to the 3' end of a polynucleotide using polymerase or transcriptase, and can have another non-terminating nucleic acid attached to it using polymerase or transcriptase, and does not need to remove a protecting group or a reversible terminator from the nucleotide.Naturally occurring nucleic acid is a type of non-terminating nucleic acid.Non-terminating nucleic acid can be labeled or unlabeled.

[0070] A "nucleotide flow" refers to a series of one or more non-terminating nucleotides (which may be labeled, or portions of which may be labeled).

[0071] "Short genetic variants" refer to genetic polymorphisms (i.e., mutations) that are 10 or fewer consecutive bases in length (i.e., 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 base in length). This term includes single nucleotide polymorphisms (SNPs), multinucleotide polymorphisms (MNPs), and indels that are 10 or fewer consecutive bases in length.

[0072] It will be understood that the embodiments and variations of the invention described herein include "consisting of" and / or "consisting essentially of" the embodiments and variations.

[0073] When a range of values ​​is provided, it is to be understood that each intervening value between the upper and lower limit of that range, and any other stated or intervening value within that stated range, is encompassed within the scope of the disclosure. When a stated range includes an upper or lower limit, ranges that do not include either of those included limits are also included in the disclosure.

[0074] Some of the analytical methods described herein include mapping a sequence to a reference sequence, determining sequence information, and / or analyzing sequence information.It is well understood in the art that complementary sequences can be easily determined and / or analyzed, and the description provided herein encompasses analytical methods performed on complementary sequences.

[0075] The section headings used herein are for organizational purposes only and should not be construed as limiting the subject matter described. This description is provided to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a patent application and its requirements. Various modifications of the described embodiments will be readily apparent to those skilled in the art, and the general principles herein may be applied to other embodiments. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features described herein.

[0076] The figures illustrate processes according to various embodiments. In these example processes, some blocks are combined as needed, the order of some blocks is changed as needed, and some blocks are omitted as needed. In some instances, additional steps may be performed in combination with the example processes. Thus, the operations as illustrated (and described in more detail below) are exemplary in nature and, therefore, should not be considered limiting.

[0077] The disclosures of all publications, patents, and patent applications mentioned herein are hereby incorporated by reference in their entirety. In the event that any reference incorporated by reference conflicts with the present disclosure, the present disclosure shall control. Flow Sequencing

[0078] Flow sequencing methods can be used to generate sequencing data, including extending a primer attached to a template polynucleotide molecule according to a predetermined flow cycle in which a single type of nucleotide can reach the extension primer at any given flow position. In some embodiments, at least a portion of the nucleotides of a particular type include a label, and when the labeled nucleotide is incorporated into the extension primer, the label provides a detectable signal. The sequence obtained by incorporating such a nucleotide into the extended primer should be the reverse complement of the sequence of the template polynucleotide molecule. In some embodiments, for example, sequencing data is generated using a flow sequencing method that includes extending a primer using a labeled nucleotide and detecting the presence or absence of the labeled nucleotide incorporated into the extension primer. Flow sequencing methods are sometimes referred to as "sequencing by natural synthesis" or "non-terminating sequencing by synthesis" methods. Exemplary methods are described in U.S. Patent No. 8,772,473, the entirety of which is incorporated herein by reference. Although the following description is provided with respect to flow sequencing methods, it will be understood that other sequencing methods may be used to sequence all or a portion of the sequenced region. For example, the sequencing data discussed herein may be generated using pyrosequencing methods.

[0079] Flow sequencing involves the use of nucleotides to extend a primer hybridized to a polynucleotide. Nucleotides of a given base type (e.g., A, C, G, T, U, etc.) can be mixed with the hybridized template to extend the primer if a complementary base is present in the template strand. The nucleotide can be, for example, a non-terminating nucleotide. When a nucleotide is a non-terminating nucleotide, more than one consecutive base can be incorporated into the extended primer strand if more than one consecutive complementary base is present in the template strand. A non-terminating nucleotide contrasts with a nucleotide that has a 3' reversible terminator, where the blocking group is generally removed before the consecutive nucleotides are bound. If a complementary base is not present in the template strand, primer extension stops until a nucleotide that is complementary to the next base in the template strand is introduced. At least a portion of the nucleotide can be labeled, so that incorporation can be detected. Most commonly, only a single nucleotide type is introduced at a time (i.e., added individually), although in certain embodiments, two or three different types of nucleotides may be introduced simultaneously. This methodology can be contrasted with sequencing methods that use reversible terminators, in which after every single base extension, primer extension is halted until the terminator is flipped to allow incorporation of the next subsequent base.

[0080] Nucleotides can be introduced in flow order during primer extension, and this process can be further divided into flow cycles. A flow cycle is a repeated nucleotide flow order and can be of any length. Nucleotides are added stepwise, which allows the added nucleotide to be incorporated into the end of the sequencing primer of the complementary base present in the template strand. Simply by way of example, the flow order of the flow cycle can be ATGC, or the flow cycle order can be ATCG. Alternative orders can be easily envisioned by those skilled in the art. The flow cycle order can be of any length, but flow cycles containing four unique base types (A, T, C and G in any order) are the most common. In some embodiments, the flow cycle comprises 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more separate nucleotide flows in a flow cycle order. By way of example only, the flow cycle sequence can be TCACGATGCATGCTAG, where these 16 separately provided nucleotides are provided in this flow cycle sequence over several cycles. Between introductions of different nucleotides, unincorporated nucleotides can be removed, for example, by washing the sequencing platform with a wash solution.

[0081] Polymerase can be used to extend sequencing primer by incorporating one or more nucleotides to the end of primer in a template-dependent manner.In some embodiments, polymerase is a DNA polymerase.Polymerase can be naturally occurring or synthetic (e.g., mutant) polymerase.Polymerase can be added at the first step of primer extension, but supplementary polymerase can be added during sequencing, for example, by using stepwise addition of nucleotides or after several flow cycles, if necessary. Exemplary polymerases include DNA polymerases, RNA polymerases, thermostable polymerases, wild-type polymerases, modified polymerases, Bst DNA polymerase, Bst 2.0 DNA polymerase, Bst 3.0 DNA polymerase, Bsu DNA polymerase, E. coli DNA polymerase I, T7 DNA polymerase, bacteriophage T4 DNA polymerase Φ29 (phi 29) DNA polymerase, Taq polymerase, Tth polymerase, Tli polymerase, Pfu polymerase, and SeqAmp DNA polymerase.

[0082] When determining the sequence of the template strand, the introduced nucleotide can include a labeled nucleotide, and the presence or absence of the incorporated labeled nucleic acid can be detected to determine the sequence. The label can be, for example, an optically active label (e.g., a fluorescent label) or a radioactive label, and the signal emitted or altered by the label can be detected using a detector. The presence or absence of the labeled nucleotide incorporated into the primer hybridized to the template polynucleotide can be detected, which allows the sequence to be determined (e.g., by generating a flowgram). In some embodiments, the labeled nucleotide is labeled with a fluorescent moiety, a luminescent moiety, or other light-emitting moiety. In some embodiments, the label is attached to the nucleotide via a linker. In some embodiments, the linker is cleavable, for example, by a photochemical or chemical cleavage reaction. For example, the label can be cleaved after detection and before incorporation of the successive nucleotide. In some embodiments, the label (or linker) is attached to the nucleotide base or another site on the nucleotide that does not interfere with the extension of the nascent DNA strand. In some embodiments, the linker comprises a disulfide or PEG-containing moiety.

[0083] In some embodiments, the nucleotide introduced comprises only unlabeled nucleotides, and in some embodiments, the nucleotide comprises a mixture of labeled and unlabeled nucleotides.For example, in some embodiments, the portion of labeled nucleotides compared to the total nucleotides is about 90% or less, about 80% or less, about 70% or less, about 60% or less, about 50% or less, about 40% or less, about 30% or less, about 20% or less, about 10% or less, about 5% or less, about 4% or less, about 3% or less, about 2.5% or less, about 2% or less, about 1.5% or less, about 1% or less, about 0.5% or less, about 0.25% or less, about 0.1% or less, about 0.05% or less, about 0.025% or less, or about 0.01% or less. In some embodiments, the portion of labeled nucleotides compared to total nucleotides is about 100%, about 95% or more, about 90% or more, about 80% or more, about 70% or more, about 60% or more, about 50% or more, about 40% or more, about 30% or more, about 20% or more, about 10% or more, about 5% or more, or or more, about 4% or more, about 3% or more, about 2.5% or more, about 2% or more, about 1.5% or more, about 1% or more, about 0.5% or more, about 0.25% or more, about 0.1% or more, about 0.05% or more, about 0.025% or more, or about 0.01% or more.In some embodiments, the portion of labeled nucleotides compared to total nucleotides is from about 0.01% to about 100%, e.g., from about 0.01% to about 0.025%, from about 0.025% to about 0.05%, from about 0.05% to about 0.1%, from about 0.1% to about 0.25%, from about 0.25% to about 0.5%, from about 0.5% to about 1%, from about 1% to about 1.5%, from about 1.5% to about 2%, from about 2% to about 2.5%, about 2.5% to about 3%, about 3% to about 4%, about 4% to about 5%, about 5% to about 10%, about 10% to about 20%, about 20% to about 30%, about 30% to about 40%, about 40% to about 50%, about 50% to about 60%, about 60% to about 70%, about 70% to about 80%, about 80% to about 90%, about 90% to less than 100%, or about 90% to about 100%.

[0084] Prior to generating sequencing data, the polynucleotide is hybridized with a sequencing primer to generate a hybridized template. The polynucleotide can be ligated to an adaptor during sequencing library preparation. The adaptor can include a hybridization sequence that hybridizes with the sequencing primer. For example, the hybridization sequence of the adaptor can be a uniform sequence across multiple different polynucleotides, and the sequencing primer can be a uniform sequencing primer. This allows multiplex sequencing of different polynucleotides in a sequencing library.

[0085] Polynucleotides can be attached to a surface (e.g., a solid support) for sequencing. Polynucleotides can be amplified (e.g., by bridge amplification or other amplification techniques) to generate polynucleotide sequencing colonies. The amplified polynucleotides in a cluster are substantially identical or complementary (some errors may be introduced during the amplification process, so that a portion of the polynucleotide may not necessarily be identical to the original polynucleotide). Colony formation allows for signal amplification such that a detector can accurately detect labeled nucleotide incorporation for each colony. In some cases, colonies are formed on beads using emulsion PCR, and the beads are distributed across the sequencing surface. Examples of systems and methods for sequencing can be found in U.S. Patent Application No. 10,344,328, the entirety of which is incorporated herein by reference.

[0086] The primers hybridized to the polynucleotides are extended through the nucleic acid molecule using separate nucleotide flows according to a flow order (which may be periodic according to a flow cycle order), and incorporation of nucleotides can be detected as described above, thereby generating a sequencing data set for the nucleic acid molecule.

[0087] Primer extension using flow sequencing allows long-range sequencing of hundreds or even thousands of bases in length. The number of flow steps or cycles can be increased or decreased to obtain the desired sequencing length. Primer extension can include one or more flow steps for stepwise extension of the primer using nucleotides with one or more different base types. In some embodiments, primer extension includes between 1 and about 1000 flow steps, for example, between 1 and about 10 flow steps, between about 10 and about 20 flow steps, between about 20 and about 50 flow steps, between about 50 and about 100 flow steps, between about 100 and about 250 flow steps, between about 250 and about 500 flow steps, or between about 500 and about 1000 flow steps. The flow steps can be divided into the same or different flow cycles. The number of bases incorporated into the primer depends on the sequence of the region to be sequenced and the flow order used to extend the primer. In some embodiments, the region to be sequenced is from about 1 base to about 4000 bases in length, e.g., from about 1 base to about 10 bases in length, from about 10 bases to about 20 bases in length, from about 20 bases to about 50 bases in length, from about 50 bases to about 100 bases in length, from about 100 bases to about 250 bases in length, from about 250 bases to about 500 bases in length, from about 500 bases to about 1000 bases in length, from about 1000 bases to about 2000 bases in length, or from about 2000 bases to about 4000 bases in length.

[0088] The polynucleotide used in the method described herein can be obtained from any suitable biological source, for example, tissue sample, blood sample, plasma sample, saliva sample, fecal sample, or urine sample. The polynucleotide can be a DNA polynucleotide or an RNA polynucleotide. In some embodiments, the RNA polynucleotide is reverse transcribed into a DNA polynucleotide before hybridizing the polynucleotide with a sequencing primer. In some embodiments, the polynucleotide is cell-free DNA (cfDNA), for example, circulating tumor DNA (ctDNA) or fetal cell-free DNA. The nucleic acid molecule can be randomly fragmented, for example, in vivo (for example, as in cfDNA) or in vitro (for example, by sonication or enzymatic fragmentation).

[0089] The library of polynucleotides can be prepared by known methods. In some embodiments, the polynucleotides can be ligated to an adapter sequence. The adapter sequence can include a hybridization sequence that hybridizes with the extended primer during generation of coupled sequencing read pairs.

[0090] In some embodiments, the sequencing data is obtained without amplifying the nucleic acid molecules before establishing the sequencing colonies (also called sequencing clusters). Methods for generating sequencing colonies include bridge amplification or emulsion PCR. Shotgun sequencing and methods that rely on calling consensus sequences generally use unique molecular identifiers (UMIs) to label nucleic acid molecules and amplify the nucleic acid molecules to generate a large number of copies of the same nucleic acid molecule that are sequenced independently. The amplified nucleic acid molecules can then be bound to a surface and bridge amplified to generate sequencing clusters that are sequenced independently. The independently sequenced nucleic acid molecules can then be associated using UMIs. However, the amplification process can introduce errors into the nucleic acid molecules, for example due to the limited fidelity of DNA polymerase. In some embodiments, the nucleic acid molecules are not amplified prior to amplification to generate colonies for obtaining sequencing data. In some embodiments, the nucleic acid sequencing data is obtained without using unique molecular identifiers (UMIs). Sequencing datasets and variant detection

[0091] Sequencing data can be generated based on the detection of incorporated nucleotides and the order of nucleotide incorporation. Take the following extended sequences (i.e., each reverse complement of the corresponding template sequence): CTG, CAG, CCG, CGT, and CAT (assuming that neither the preceding nor the following sequences are subjected to the sequencing method), as well as the repeated flow cycles of TACG (i.e., sequential addition of T, A, C, and G nucleotides during the repeated cycles). A particular type of nucleotide at a given flow position will be incorporated into the primer only if the complementary base is present in the template polynucleotide. The resulting exemplary flowgram is shown in Table 1, where 1 indicates that the introduced nucleotide is incorporated and 0 indicates that the introduced nucleotide is not incorporated. The flowgram can be used to derive the sequence of the template strand. For example, the sequencing data (e.g., flowgram) discussed herein represents the extended primer strand and its reverse complement, which can be easily determined to represent the sequence of the template strand. An asterisk (*) in Table 1 indicates that a signal may be present in the sequencing data if additional nucleotides were incorporated into the extended sequencing strand (eg, the longer template strand). [Table 1]

[0092] Flowgrams can be binary or non-binary. Binary flowgrams detect the presence (1) or absence (0) of an incorporated nucleotide. Non-binary flowgrams can more quantitatively determine the number of nucleotides incorporated from each stepwise incorporation. For example, an extended sequence of CCG will contain incorporation of two C bases into the extension primer in the same C flow (e.g., at flow position 3), and the signal emitted by the labeled base will have a higher intensity than the intensity level corresponding to a single base incorporation. This is shown in Table 1. Non-binary flowgrams can also indicate the presence or absence of a base and provide additional information including the number of bases likely to be incorporated into each extension primer at a given flow position. The values ​​do not have to be integers. In some cases, the values ​​may reflect the uncertainty and / or probability of the number of bases incorporated into a given flow position.

[0093] In some embodiments, the sequencing data set includes flow signals representing base counts that indicate the number of bases in the sequenced nucleic acid molecule that are incorporated at each flow position. For example, as shown in Table 1, a primer extended with a CTG sequence using a TACG flow cycle sequence has a value of 1 at position 3, indicating a base count of 1 at that position (the 1 base is a C that is complementary to a G in the sequenced template strand). Also in Table 1, a primer extended with a CCG sequence using a TACG flow cycle sequence has a value of 2 at position 3, indicating a base count of 2 at that position of the extended primer while at this flow position. Here, the 2 bases refer to the first CC sequence of the CCG sequence in the extended primer sequence, which is complementary to the GG sequence in the template strand.

[0094] The flow signals in the sequencing data set may include one or more statistical parameters that indicate the likelihood or confidence interval for one or more base counts at each flow position. In some embodiments, the flow signals are determined from analog signals detected during the sequencing process, such as the fluorescent signal of one or more bases that are incorporated into the sequencing primer during sequencing. In some cases, the analog signals can be processed to generate statistical parameters. For example, machine learning algorithms can be used to correct for the context effect of analog sequencing signals, as described in published international patent application WO2019084158A1, the entirety of which is incorporated herein by reference. Although zero or more integer numbers of bases are incorporated into any given flow position, a given analog signal may not perfectly match that analog signal. Thus, a statistical parameter can be determined that indicates the likelihood of the number of bases that are incorporated into a flow position, given the detected signals. By way of example only, for the CCG sequence in Table 1, the likelihood that the flow signal indicates two bases were incorporated at flow position 3 may be 0.999, and the likelihood that the flow signal indicates one base was incorporated at flow position 3 may be 0.001. If the flow signal includes statistical parameters indicating the likelihood for multiple base counts at each flow position, the sequencing data set may be formatted as a sparse matrix. By way of example only, a primer extended with a sequence of TATGGTCGTCGA (SEQ ID NO: 1) using a repeated flow cycle sequence of TACG may result in the sequencing data set shown in FIG. 1A. The statistical parameters or likelihood values ​​may vary, for example, depending on noise or other artifacts present during detection of the analog signal during sequencing.In some embodiments, if a statistical parameter or likelihood falls below a predetermined threshold, the parameter may be set to a predetermined non-zero value that is substantially zero (i.e., some very small or negligible value) to aid in statistical analyses, as discussed further herein, where using a true zero value may result in calculation errors or may not adequately distinguish between levels of unlikeliness, e.g., very unlikely (0.0001) from unlikely (0).

[0095] A value indicating the likelihood of a sequencing data set for a given sequence can be determined from the sequencing data set without sequence alignment. For example, the most likely sequence from which data is obtained can be determined by selecting the base count with the highest likelihood at each flow position, as shown by an asterisk in FIG. 1B (using the same data as shown in FIG. 1A). Thus, the sequence of primer extension can be determined according to the most likely base count at each flow position: TATGGTCGTCGA (SEQ ID NO: 1). From this, the reverse complement sequence (i.e., the template strand) can be easily determined. Furthermore, the likelihood of this sequencing data set obtaining the TATGGTCGTCGA (SEQ ID NO: 1) sequence (or the reverse complement sequence) can be determined as the product of the selection likelihoods at each flow position.

[0096] A sequencing dataset associated with a nucleic acid molecule can be compared to one or more (e.g., 2, 3, 4, 5, 6 or more) possible candidate sequences. A close match (based on a match score, as discussed below) between a sequencing dataset and a candidate sequence indicates that the sequencing dataset is likely to have arisen from a nucleic acid molecule having the same sequence as the close-matching candidate sequence. In some embodiments, the sequence of a sequenced nucleic acid molecule can be mapped to a reference sequence (e.g., using the Burrows-Wheeler Alignment (BWA) algorithm or other suitable alignment algorithm) to determine a locus (or loci or loci) for the sequence. As discussed above, a sequencing dataset in flow space can be easily converted to base space (or vice versa, if the flow order is known), and mapping can be performed in flow space or base space. The locus (or loci) corresponding to the mapped sequence can be associated with one or more variant sequences that can act as candidate sequences (or haplotype sequences) for the analysis methods described herein. One advantage of the method described herein is that it does not require the sequence of sequenced nucleic acid molecules and each candidate sequence to be aligned, which is generally computationally expensive in some cases, using alignment algorithm.Instead, it can use the sequencing data in flow space to determine the match score for each candidate sequence, which is computationally more efficient.

[0097] The match score indicates how well the sequencing data set supports the candidate sequence. For example, the match score, which indicates the likelihood that the sequencing data set matches the candidate sequence, can be determined by selecting a statistical parameter (e.g., likelihood) at each flow position that corresponds to the base count at the flow position where the expected sequencing data for the candidate sequence was obtained. The product of the selected statistical parameters can give the match score. For example, the sequencing data set shown in FIG. 1A for the extended primer, and TATGGTC A Assume a candidate primer extension sequence of TCGA (SEQ ID NO:2). Figure 1C (showing the same sequencing data set in Figure 1A) shows a trace for the candidate sequence (solid circle). For comparison, TATGGTC G The trace of the TCGA (SEQ ID NO:1) sequence (see FIG. 1B) is shown in FIG. 1C using open circles. There is a large difference between the match score indicating the likelihood that the sequencing data corresponds to the first candidate sequence TATGGTCATCGA (SEQ ID NO:2) and the match score indicating the likelihood that the sequencing data matches the second candidate sequence TATGGTCGTCGA (SEQ ID NO:1), even though these sequences only vary by a single base variation. As can be seen in FIG. 1C, the difference between the traces is seen at flow position 12 and propagates over at least nine flow positions (and potentially longer if the sequencing data extends over additional flow positions). This propagation, which continues over one or more flow cycles, may be referred to as a "flow shift" or "cycle shift" and is generally a very unlikely event if the sequencing data set matches a candidate sequence.

[0098] Thus, a match score between each sequencing data set and the candidate sequence (or each candidate sequence) can be determined. For example, the likelihood L (R j |H i) can be determined using the likelihood (e.g., the product) of the selected base counts at each flow position for a given candidate sequence.

[0099] The match score can be used to classify the test sequencing data and / or the nucleic acid molecule associated with the test sequencing data. The classifier can indicate that the nucleic acid molecule contains a variant (e.g., a variant contained in the candidate sequence), can indicate that the nucleic acid molecule does not contain a variant, or can indicate a null call. A null call indicates that the presence or absence of a variant in the nucleic acid molecule associated with the test sequencing data is not present, but rather indicates that the match score cannot be used to make a call with a desired statistical confidence. The test sequencing data or nucleic acid molecule can be classified as having a variant, for example, if the match score is above a desired confidence threshold. Conversely, the test sequencing data or nucleic acid molecule can be classified as not having a variant, for example, if the match score is below a desired confidence threshold.

[0100] The above analysis can be applied to select candidate sequences from two or more different candidate sequences. A match score can be determined that indicates the likelihood that a sequencing data set matches each candidate sequence. For example, a statistical parameter at each flow position in a sequencing data set can be selected for each candidate sequence, the statistical parameter corresponding to the base count of the candidate sequence at that flow position. In some embodiments, the analysis includes generating predicted sequencing data for the candidate sequence, assuming that the candidate sequence is sequenced using the same flow order used to generate the sequencing data set for the sequenced test nucleic acid molecule. This data can be generated by sequencing a nucleic acid molecule having the candidate sequence, or by generating the candidate sequencing data set in silico based on the candidate sequence and the flow order. An exemplary candidate sequencing data set is shown under the test data sequencing data set in FIG. 1C, where the first candidate sequence [TATGGTCATCGA (SEQ ID NO: 2)] corresponds to the filled circle trace and the second candidate sequence [TATGGTCGTCGA (SEQ ID NO: 1)] corresponds to the open circle trace. In some embodiments, for example, if match scores are determined for two or more different candidate sequences, the test sequencing data or nucleic acid molecule may be classified as having a variant for one of the two or more candidate sequences, having no variant for one of the two or more candidate sequences, or a null call may be obtained between the two or more candidate sequences (e.g., if no call could be obtained for any of the candidate sequences or if the match scores indicate two or more different variants at the same locus).

[0101] Once the match score of the sequencing data set is determined for the candidate sequence, the candidate sequence having short genetic variants can be selected based on the match score (e.g., the candidate sequence that produces the highest likelihood match among two or more candidate sequences). The short genetic variants can be, for example, variants or mutations found in a subpopulation of individuals, or variants or mutations that are specific to a single or specific individual. The short genetic variants can be germline variants or somatic variants. The sequencing data resulting from the sequence of the nucleic acid molecule having short genetic variants will match the candidate sequence having short genetic variants, and the candidate sequence can be selected, while the rejected (or non-selected) candidate sequence does not contain short genetic variants, as indicated by a smaller likelihood match (based on the match score determined for these candidate sequences). The non-selected candidate sequence and the selected candidate sequence (that best matches the sequenced nucleic acid molecule sequencing data set) may differ at two or more flow positions, which may be two or more consecutive flow positions, or two or more non-consecutive flow positions. In some embodiments, the non-selected candidate sequence and the selected candidate sequence differ at three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or ten or more flow positions. In some embodiments, the non-selected candidate sequence and the selected candidate sequence differ over one or more, two or more, three or more, four or more, or five or more flow cycles. In some embodiments, the non-selected candidate sequence and the selected candidate sequence differ at X base positions, where the sequencing data set associated with the sequence of the nucleic acid molecule and the non-selected candidate sequence differ at (X+2) or more flow positions.Increasing the number of flow positions at which the sequenced nucleic acid molecule sequencing dataset best matches the selected candidate sequence and the non-selected candidate sequence reduces the likelihood that the sequenced nucleic acid molecule sequencing dataset was derived from sequencing a nucleic acid molecule having the non-selected candidate sequence.

[0102] The likelihood that the sequence data set of the sequenced nucleic acid molecule matches the non-selected candidate sequence is preferably low, for example, less than 0.05, less than 0.04, less than 0.03, less than 0.02, less than 0.01, less than 0.005, less than 0.001, less than 0.0005, or less than 0.0001. The likelihood that the sequence data set of the sequenced nucleic acid molecule matches the selected candidate sequence is preferably high, for example, greater than 0.95, greater than 0.96, greater than 0.97, greater than 0.98, greater than 0.99, greater than 0.995, or greater than 0.999.

[0103] A method for detecting short genetic variants in a test sample may, in some embodiments, include analyzing a plurality of test sequencing datasets, each test sequencing dataset being associated with a separate test nucleic acid molecule in the test sample. The nucleic acid molecules may, for example, overlap at least partially at a locus when the sequences of the nucleic acid molecules are aligned with a reference sequence. At least a portion of the nucleic acid molecules may have different sequencing start positions (relative to a locus), resulting in different flow positions and / or different flow order contexts for a given base in the sequence. In this manner, the same candidate sequence may be used to analyze test sequencing datasets from the plurality. For each candidate sequence, a match score may be determined indicating the likelihood that the plurality of test sequencing datasets match the candidate sequence, and the candidate sequence with the highest likelihood match (and thus containing the short genetic variant) may be selected. An exemplary analysis for detecting short genetic variants using a plurality of test sequencing datasets is shown in Figures 2A-2D. In Figure 2A, sequences corresponding to three sequenced test nucleic acid molecules (R1, R2, and R3, each represented by the sequence of an extended primer) are aligned with a reference sequence at overlapping loci associated with two candidate sequences (H1 and H2). Figures 2B, 2C, and 2D show exemplary sequencing data sets for R1, R2, and R3, respectively, along with selection statistical parameters at each flow position within the sequencing data set that corresponds to the bases of H1 (filled circle) or H2 (open circle).

[0104] The one or more determined match scores can be used to call the presence (or identity) or absence of short gene variants for a test sample. In some embodiments, for example, a single nucleic acid molecule (or associated test sequencing dataset) classified as having a variant can be sufficient to call the presence, identity, or absence of a variant, for example, if the match score indicates a match with a candidate sequence with a desired or preset confidence level. In some embodiments, a predetermined number (e.g., one or more, two or more, three or more, four or more, five or more, etc.) of nucleic acid molecules (or test sequencing datasets associated with the nucleic acid molecules) are classified as having a variant before the variant is called for the test sample. In some embodiments, the number of nucleic acid molecules (or test sequencing datasets associated with the nucleic acid molecules) is dynamically selected depending on the match score, for example, a single nucleic acid molecule classified as having a variant with a high confidence match score may be used to call a variant, or two or more nucleic acid molecules classified as having a variant with a lower confidence match score may be used to call a variant.

[0105] If necessary, the separate match scores for sequencing data sets are analyzed together to determine the match scores of multiple test sequencing data sets.For example, after the match scores for each test sequencing data set for each candidate sequence are determined using the method described herein, a known Bayesian method can be used, for example, the HaplotypeCaller algorithm included in Genome Analysis Toolkit (GATK) can be used to determine the match scores that indicate the likelihood that multiple test sequencing data sets match with the candidate sequence, and the candidate sequence with the highest likelihood match can be selected. See, e.g., DePristo et al., A framework for variation discovery and genotyping using next-generation DNA sequencing data, Nature Genetics 43, 491-498 (2011); and Poplin et al., Scaling accurate genetic variant discovery to tens of thousands of samples, bioRxiv, www.biorxiv.org / content / 10.1101 / 201178v3 (July 24, 2018); Hwang et al., Systematic comparison of variant calling pipelines using gold standard personal exome variants, Scientific Reports, vol. 5, no. 17875 (2015), the contents of each of which are incorporated herein by reference. Selection of target variants and / or flow cycle order

[0106] The short target gene variants can be selected, for example, as a basis for selecting flow sequences and / or candidate sequences (i.e., by preselecting short target gene variants), or to serve as a basis for downstream analysis. The downstream analysis can include, for example, assembling a biomarker panel that includes the identified short gene variants. The biomarker panel can be personalized for an individual subject associated with the test sample. As an example, the biomarker panel can include one or more short gene variants, e.g., variant signatures, associated with a disease (e.g., cancer). In another example, the biomarker panel is personalized for a subject and includes one or more short gene variants previously detected in a sample from the subject, which variants can be attributed to a disease (e.g., cancer) in the subject.

[0107] The method for identifying short genetic variants described herein may be particularly useful when one or more short target genetic variants are preselected. The limit of detection (LOD) of a given short genetic variant may depend on the sequence context of the short genetic variant (e.g., the sequence of the nucleic acid molecule adjacent to the short target genetic variant locus) and the flow order (or flow cycle order) used to sequence the nucleic acid molecule and generate the sequencing data set of the nucleic acid molecule. That is, a given flow order, short genetic variant, and short genetic variant context can be used to determine the number of flow position variances in flow space between a nucleic acid molecule with a short genetic variant and a nucleic acid molecule without a short genetic variant (e.g., a reference sequence). This allows for the selection of particularly sensitive variants or the selection of a flow order that can detect a particular variant with high sensitivity. A target sequencing data set associated with a target sequence that includes a short target genetic variant can be compared to a reference sequencing data set associated with a reference sequence that does not have a short target genetic variant to determine the number of flow position differences that exist between the target sequence and the reference sequence. That is, the reference sequence is identical to the target sequence except for short target gene variants. A higher number of flow position differences indicates a higher sensitivity (i.e., a lower detection limit) for that variant. The target and reference sequencing data sets can be determined by actually sequencing the nucleic acid molecule having the target sequence and / or the nucleic acid molecule having the reference sequence, or the data sets can be predicted sequencing data sets (e.g., as determined in silico).

[0108] In one example, the genetic fingerprint of a particular subject or cancer may be desired, but it is not necessary to detect each and every short genetic variant in the genome of the subject or cancer.Instead, one or more short genetic variants that are particularly sensitive for a given flow order can be preselected.By preselecting the variant with high sensitivity, it is possible to use a lower sequencing depth for test sample to call variants with high confidence.

[0109] In some embodiments, the method for detecting short target gene variants in test samples may include the step of selecting short target gene variants, and the target sequencing data set related to the target sequence comprising the short target gene variant and the reference sequencing data set related to the reference sequence are different at two or more flow positions, and the flow positions correspond to the nucleotide flows, when the target sequencing data set is obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to the flow cycle order.In some embodiments, the target sequencing data set and the reference sequencing data are different at two or more non-contiguous flow positions.In some embodiments, the target sequencing data set and the reference sequencing data are different at two or more consecutive flow positions.In some embodiments, the target sequencing data set and the reference sequencing data are different at three or more flow positions, and these flow positions can be consecutive or non-consecutive. In some embodiments, the target sequence and the reference sequence differ at X base positions, and the target and reference sequencing data sets differ at (X+2) or more consecutive flow positions. In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0110] In some embodiments, the method for detecting short target gene variants in test samples may include the step of selecting short target gene variants, and the target sequencing data set related to the target sequence comprising the short target gene variant and the reference sequencing data set related to the reference sequence are different at two or more flow positions, and the flow positions correspond to the nucleotide flows, when the target sequencing data set and the reference sequencing data set are obtained by sequencing the target sequence and the reference sequence using non-terminating nucleotides provided in separate nucleotide flows according to the flow cycle order.In some embodiments, the target sequencing data set and the reference sequencing data are different at two or more non-contiguous flow positions.In some embodiments, the target sequencing data set and the reference sequencing data are different at two or more consecutive flow positions.In some embodiments, the target sequencing data set and the reference sequencing data are different at three or more flow positions, and these flow positions can be consecutive or non-consecutive. In some embodiments, the target sequence and the reference sequence differ at X base positions, and the target and reference sequencing data sets differ at (X+2) or more consecutive flow positions. In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0111] The detection of the selected target short genetic variant can generally proceed as discussed above.For example, in some embodiments, a test sequencing data set related to the test nucleic acid molecule having the locus of the short target genetic variant can be obtained.The sequencing data is generated by sequencing the test nucleic acid molecule using non-terminal nucleotides provided in separate nucleotide flows according to the same flow cycle order used to generate the target and reference sequencing data sets.A match score indicating the likelihood that the test sequencing data set matches the target sequence having the short genetic variant (or alternatively or additionally, a match score indicating the likelihood that the test sequencing data set matches the reference sequence) is determined, and the determined match score can be used to call the presence or absence of the short target genetic variant in the test sample.

[0112] In some embodiments, the short target gene variants in test samples are detected using multiple test sequencing data sets, each test sequencing data set being related to different test nucleic acid molecules in test samples.The test nucleic acid molecules analyzed overlap at the short target gene variant locus, and the data sets are generated by sequencing the target nucleic acid molecules using the same flow cycle order used to select the short target gene variants.A match score is determined that indicates the likelihood that the multiple test sequencing data sets match the target sequence with short gene variants (or alternatively or additionally, the match score indicates the likelihood that the multiple test sequencing data sets match the reference sequence), and the determined match score can be used to call the presence or absence of the short target gene variants in test samples.

[0113] In some embodiments, the flow order or flow cycle order used to generate sequencing data is preselected.As discussed herein, the context of variant in flow order can affect the signal difference between variant sequence and comparison (e.g., reference) sequence.The flow order or flow cycle order can be preselected to increase the likelihood of detecting the selected target variant.

[0114] FIG. 3 shows a flow chart of an exemplary method for detecting short gene variants in a test sample. In step 302, a short target gene variant is selected. The short target gene variant is selected such that the target sequencing data associated with the target sequence containing the short target gene variant and the reference sequencing data set associated with the reference sequence are different at more than two flow positions, when the target sequencing data set and the reference sequencing data set are obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, and the flow positions correspond to the nucleotide flows. In step 304, one or more test sequencing data sets are obtained, for example, by sequencing one or more test nucleic acid molecules to obtain one or more test sequencing data sets, or by receiving one or more test sequencing data sets. Each of the test sequencing data sets is associated with a test nucleic acid molecule derived from a test sample. For the analysis of the selected short target gene variant, the test nucleic acid molecule at least partially overlaps the locus associated with the short target gene variant. If the test sequencing data set includes flow signals at multiple flow locations, the sequencing data set can be determined (or may be pre-determined) by sequencing the test nucleic acid molecule using non-terminating nucleotides provided in separate nucleotide flows according to the flow cycle order. At step 306, a match score is determined for each test nucleic acid molecule associated with the test sequencing data set. The match score indicates the likelihood that the test sequencing data set associated with the nucleic acid molecule matches the target sequence. Alternatively, the match score may indicate the likelihood that the test sequencing data set associated with the nucleic acid molecule matches the reference sequence. At step 308, the one or more determined match scores are used to call the presence or absence of the short target gene variant in the test sample.

[0115] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) selecting a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the flow positions correspond to nucleotide flows, when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order; (b) obtaining one or more test sequencing datasets, where each test sequencing dataset is associated with a test nucleic acid molecule, and each test nucleic acid molecule contains a gene associated with the short target genetic variant. The method includes the steps of: (a) determining, for each of the test nucleic acid molecules associated with the test sequencing dataset, a match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence, or a match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (b) calling the presence or absence of the short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprising the short target gene variant. In some embodiments, the target sequencing dataset and the reference sequencing dataset differ at more than two flow locations (e.g., at more than two consecutive flow locations or at more than two non-consecutive flow locations).In some embodiments, the target sequencing dataset and the reference sequencing dataset differ over one or more flow cycles.

[0116] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) selecting a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the flow positions corresponding to the nucleotide flows; (b) sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order; The method includes the steps of: (a) obtaining one or more test sequencing data sets comprising flow signals at a plurality of flow positions, each test sequencing data set being associated with a test nucleic acid molecule, each test nucleic acid molecule at least partially overlapping a locus associated with a short target gene variant, and derived from a test sample; (c) determining a match score for each test nucleic acid molecule associated with the test sequencing data sets, the match score indicating the likelihood that the test sequencing data set associated with the nucleic acid molecule matches a target sequence, or the match score indicating the likelihood that the test sequencing data set associated with the nucleic acid molecule matches a reference sequence; and (d) calling the presence or absence of the short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprising the short target gene variant. In some embodiments, the target sequencing data set and the reference sequencing data set differ at more than two flow positions (e.g., more than two consecutive flow positions, or more than two non-consecutive flow positions).In some embodiments, the target sequencing dataset and the reference sequencing dataset differ over one or more flow cycles.

[0117] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) preselecting a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the preselected short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the flow positions correspond to nucleotide flows, when the target sequencing dataset and the reference sequencing dataset are obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order; (b) obtaining one or more test sequencing datasets, where each test sequencing dataset is associated with a test nucleic acid molecule, and each test nucleic acid molecule is associated with a preselected short target genetic variant. The method includes the steps of: (a) determining a test sequence data set that at least partially overlaps the associated locus and is derived from the test sample by sequencing the test nucleic acid molecule using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the test sequence data set comprising flow signals at a plurality of flow positions; (b) determining a match score for each of the test nucleic acid molecules associated with the test sequence data set, the match score indicating the likelihood that the test sequence data set associated with the nucleic acid molecule matches a target sequence, or the match score indicating the likelihood that the test sequence data set associated with the nucleic acid molecule matches a reference sequence; and (c) calling the presence or absence of the preselected short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprising the short target gene variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0118] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) preselecting a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the preselected short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the flow positions corresponding to the nucleotide flows; (b) sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order. (c) for each of the test nucleic acid molecules associated with the test sequencing dataset, determine a match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence or the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (d) use the one or more determined match scores to call the presence or absence of the preselected short target gene variant in the test sample. In some embodiments, the method further comprises generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprising the short target gene variant. In some embodiments, the target sequencing dataset and the reference sequencing dataset differ at more than two flow positions (e.g., at more than two consecutive flow positions or at more than two non-consecutive flow positions).In some embodiments, the target sequencing dataset and the reference sequencing dataset differ over one or more flow cycles.

[0119] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) preselecting a short target genetic variant and a flow cycle order, where a target sequencing dataset associated with a target sequence comprising the preselected short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the flow positions correspond to nucleotide flows, when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to the preselected flow cycle order; (b) obtaining one or more test sequencing datasets, where each test sequencing dataset is associated with a test nucleic acid molecule, and each test nucleic acid molecule comprises a preselected short target genetic variant and a reference sequencing dataset associated with a reference sequence; The method includes the steps of: (a) determining, for each of the test nucleic acid molecules associated with the test sequencing dataset, a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence or a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (b) calling the presence or absence of the preselected short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprising the short target gene variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0120] In some embodiments, a method for detecting short genetic variants in a test sample includes the steps of: (a) preselecting a short target genetic variant and a flow cycle order, where a target sequencing dataset associated with a target sequence comprising the preselected short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to the preselected flow cycle order, the flow positions corresponding to the nucleotide flows; (b) sequencing one or more test nucleic acids using non-terminating nucleotides provided in separate nucleotide flows according to the preselected flow cycle order. The method includes the steps of: (a) sequencing the molecules to obtain one or more test sequencing data sets comprising flow signals at a plurality of flow locations, each test sequencing data set being associated with a test nucleic acid molecule, each test nucleic acid molecule at least partially overlapping a locus associated with a short target gene variant, and derived from a test sample; (b) determining, for each test nucleic acid molecule associated with the test sequencing data sets, a match score indicating the likelihood that the test sequencing data set associated with the nucleic acid molecule matches a target sequence, or a match score indicating the likelihood that the test sequencing data set associated with the nucleic acid molecule matches a reference sequence; and (c) using the one or more determined match scores to call the presence or absence of the preselected short target gene variant in the test sample. In some embodiments, the method further includes generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprising the short target gene variant. In some embodiments, the target sequencing dataset and the reference sequencing dataset differ at more than two flow locations (e.g., at more than two consecutive flow locations or at more than two non-consecutive flow locations).In some embodiments, the target sequencing dataset and the reference sequencing dataset differ over one or more flow cycles. Selection of target variants and / or flow cycle order

[0121] The flow cycle sequence does not necessarily have to be limited to a four-base flow cycle (e.g., one each of A, G, C, and T in any repeating order), but may be an extension flow cycle with more than four types of base types in the cycle. The extension cycle sequence can be repeated for a desired number of cycles to extend the sequencing primer. By way of example, in some embodiments, the extension flow sequence includes 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more separate nucleotide flows in the flow cycle sequence. The cycle can include at least one each of A, G, C, and T, but repeats one or more base types in the cycle before the cycle is repeated.

[0122] Extended flow cycle sequences can be useful for detecting a larger proportion of genomic variants (e.g., SNPs) that are smaller than flow cycle sequences with four repeated bases. For example, there are 192 valid arrangements for substitution SNPs of the form XYZ→XQZ, where Q≠Y (and Q, X, Y, and Z are each one of A, C, G, and T). Of these, 168 can generate new signals (i.e., new non-zero signals or new zero signals) in the sequencing data set (e.g., flowgram). New zero or non-zero signals combined with sensitive flow sequences can generate signals that are propagated across multiple flow positions (e.g., flow shifts or cycle shifts that can extend beyond the length of the cycle), resulting in identical tail sequences in variants compared to the reference. It is noted that homopolymer insertions or deletions, rather than homopolymer length changes, can result in propagation of signal differences. The remaining 24 variants cause homopolymer length changes at the affected flow positions, but such changes do not cause propagation of signal changes. Thus, a theoretical maximum of 87.5% of SNPs can result in new signals that differ from the reference (or candidate) sequence for more than two flow positions. As discussed above, propagation of signal differences increases the likelihood difference between the test sequencing data set and the mismatched candidate sequence. Furthermore, propagation of signal changes depends on the flow order that the variant spans.

[0123] Sequencing of nucleic acid molecules in randomly fragmented test samples results in random shifts of the flow sequence context of variants when sequencing primers are extended using flow sequences. That is, the flow position of variants can vary depending on the starting position of the nucleic acid molecule to be sequenced. Even if all sequencing starting positions in the nucleic acid molecule sequence are used, not all flow cycle combinations can detect signal changes at more than two flow positions for all 87.5% of SNPs. For example, the four-base flow cycle sequence TACG can result in test sequencing data sets that differ from the reference sequencing data set at more than two flow positions for 41.7% of SNPs. As discussed further herein, the extended flow cycle sequence is designed to allow all of the theoretically maximal amount of SNPs (i.e., 87.5% of possible SNPs, or all SNPs other than those that result in homopolymer length changes) to differ between the test and reference sequencing datasets at more than two positions, thereby providing a sufficiently high sequencing depth (i.e., sampling a sufficiently large number of starting positions).

[0124] Extension sequencing flow orders may have different efficiencies (i.e., average number of incorporations per flow when used for the human reference genome sequence). In some embodiments, the flow order has an efficiency of about 0.6 or greater (e.g., about 0.62 or greater, about 0.64 or greater, about 0.65 or greater, about 0.66 or greater, or about 0.67 or greater). In some embodiments, the flow order has an efficiency of about 0.6 to about 0.7. Examples of flow cycle orders and corresponding estimated efficiencies are shown in Table 2.

[0125] In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets associated with a nucleic acid molecule (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) where about 50%-87.5% of the SNP permutations for at least 5% of the random sequencing start positions differ by SNPs. In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets associated with a nucleic acid molecule (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) where about 60%-87.5% of the SNP permutations for at least 5% of the random sequencing start positions (i.e., the "flow phase") differ by SNPs. In some embodiments, the extended sequencing flow order is selected to generate signal differences at two or more flow positions between two sequencing datasets associated with a nucleic acid molecule (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) where about 70%-87.5% of the SNP permutations for at least 5% of the random sequencing start positions differ by SNPs. In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets associated with a nucleic acid molecule (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) where about 80%-87.5% of the SNP permutations for at least 5% of the random sequencing start positions differ by SNPs.

[0126] In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) associated with a nucleic acid molecule in which about 50%-87.5% of the SNP permutations for at least 10% of the random sequencing start positions differ by SNPs. In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) associated with a nucleic acid molecule in which about 60%-87.5% of the SNP permutations for at least 10% of the random sequencing start positions differ by SNPs. In some embodiments, the extended sequencing flow order is selected to generate signal differences at two or more flow positions between two sequencing datasets associated with a nucleic acid molecule (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) in which about 70%-87.5% of the SNP permutations for at least 10% of the random sequencing start positions differ by SNPs. In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets associated with a nucleic acid molecule (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) in which about 80%-87.5% of the SNP permutations for at least 10% of the random sequencing start positions differ by SNPs.

[0127] In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) associated with a nucleic acid molecule in which about 50%-87.5% of the SNP permutations for at least 20% of the random sequencing start positions differ by SNPs. In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) associated with a nucleic acid molecule in which about 60%-87.5% of the SNP permutations for at least 20% of the random sequencing start positions differ by SNPs. In some embodiments, the extended sequencing flow order is selected to generate signal differences at two or more flow positions between two sequencing datasets associated with a nucleic acid molecule (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) in which about 70%-87.5% of the SNP permutations for at least 20% of the random sequencing start positions differ by SNPs. In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets associated with a nucleic acid molecule (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) in which about 80%-87.5% of the SNP permutations for at least 20% of the random sequencing start positions differ by SNPs.

[0128] In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) associated with a nucleic acid molecule in which about 50% to 87.5% (or about 50% to about 80%) of the SNP permutations for at least 30% of the random sequencing start positions differ by SNP. In some embodiments, the extended sequencing flow order is selected to generate signal differences at more than two flow positions between two sequencing datasets (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) associated with a nucleic acid molecule in which about 60% to 87.5% (or about 60% to about 80%) of the SNP permutations for at least 30% of the random sequencing start positions differ by SNP. In some embodiments, the extended sequencing flow order is selected to generate signal differences at two or more flow positions between two sequencing datasets (e.g., a test or target sequencing dataset and a candidate or reference sequencing dataset) associated with a nucleic acid molecule, where about 70%-87.5% (or about 70%-80%) of the SNP permutations for at least 30% of the random sequencing start positions differ by SNPs.

[0129] In some embodiments, the extended sequencing flow order is any one of the extended sequencing flow orders in Table 2. "Shift sensitivity" refers to the maximum sensitivity that causes signal differences between two sequencing datasets (e.g., test or target sequencing dataset and candidate or reference sequencing dataset) at more than two flow positions across all possible SNP permutations. "Maximum shift sensitivity" refers to the maximum sensitivity that causes signal differences between two sequencing datasets (e.g., test or target sequencing dataset and candidate or reference sequencing dataset) at more than two flow positions across all possible SNP permutations with the highest fraction of flow phases that the sensitivity is maintained. [Table 2-1] [Table 2-2] [Table 2-3]

[0130] In some embodiments, a method for sequencing a nucleic acid molecule includes: (a) hybridizing a nucleic acid molecule with a primer to form a hybridized template; (b) extending the primer using a labeled non-terminating nucleotide provided in a separate nucleotide flow according to a repetitive flow cycle sequence comprising five or more separate nucleotide flows; and (c) detecting a signal from the incorporated labeled nucleotide or detecting the absence of a signal when the primer is extended by the nucleotide flow. In some embodiments, the flow cycle sequence induces a signal change for 50% or more of possible SNP permutations at 5% of random sequencing start positions at more than two flow positions. In some embodiments, the induced signal change is a change in signal intensity, or a new substantially zero (or new zero) or a new substantially non-zero (or new non-zero) signal. In some embodiments, the induced signal change is a new substantially zero (or new zero) or a new substantially non-zero (or new non-zero) signal. In some embodiments, the flow cycle sequence has an efficiency of base incorporation of 0.6 or more per flow. In some embodiments, the flow cycle is any one of the flow cycle sequences listed in Table 2. Resequencing with a different flow order

[0131] The sensitivity of the short genetic variants detected depends on the flow cycle order used for sequencing the nucleic acid molecule, so the method described herein can be adapted to the analysis of the test nucleic acid molecule (or multiple nucleic acid molecules with overlapping loci) sequenced using two or more different flow cycle orders.Match scores can be determined based on the match between two or more different sequencing data sets (resulting from different flow cycle orders) and one or more candidate sequences.Based on the match scores as discussed above, the presence or absence of variants can be called and / or the selected candidate sequence can be called.

[0132] The method may include obtaining a first test sequencing data set related to the test nucleic acid molecule derived from the test sample sequenced using a first flow cycle order, and a second test sequencing data set related to the same test nucleic acid molecule sequenced using a second flow cycle order.For example, the test nucleic acid molecule can be sequenced by providing non-terminating nucleic acid molecule with separate nucleotide flows according to a first flow cycle order, extending sequencing primer, and detecting the presence or absence of nucleotide incorporation into sequencing primer after each nucleotide flow to generate a first test sequencing data set; removing the extended sequencing primer; the same test nucleic acid molecule can be sequenced by providing non-terminating nucleotide with separate nucleotide flows according to a second flow cycle order, extending sequencing primer, and detecting the presence or absence of nucleotide incorporation into sequencing primer after each nucleotide flow to generate a second test sequencing data set.

[0133] The sequencing data sets are different because the nucleic acid molecules are sequenced using different flow cycle orders. Figures 4A and 4B show exemplary sequencing data sets for a nucleic acid molecule with an extended primer sequence of TATGGTCGTCGA (SEQ ID NO: 1) determined using a first flow cycle order (TACG) (Figure 4A) and a second flow cycle order (AGCT) (Figure 4B). As can be seen, the sequencing data sets of Figures 4A and 4B are different due to the difference in flow cycle order, even though the nucleic acid molecule sequence is unchanged. Within the sequencing data set, a statistical parameter at each flow position corresponding to the base count of the extended primer sequence of the first candidate TATGGTCGTCGA (SEQ ID NO: 1) (solid circle) and the extended primer sequence of the second candidate TATGGTCATCGA (SEQ ID NO: 2) (open circle) can be selected. Figures 4A and 4B demonstrate that the flow cycle order results in a significant change in variant detection sensitivity. For example, differences between the first and second candidate sequences using the first flow cycle order are apparent at flow positions 12-20 (Figure 4A), whereas differences between the first and second candidate sequences using the first flow cycle order are apparent only at positions 17 and 18 (Figure 4B).

[0134] A match score can be determined indicating the likelihood that the first sequencing dataset and the second sequencing dataset match one or more candidate sequences (e.g., a target sequence having a preselected short target gene variant, a reference sequence having a preselected sequence without the short target gene variant, or other possible candidate sequences (e.g., a haplotype)) and the presence or absence of a short target gene variant can be called or the selected candidate sequence can be called.

[0135] As discussed herein, this process can be used when sequencing multiple different test nucleic acid molecules that overlap at a common locus.For example, a multiple first test sequencing data set can be obtained, each of which is a test sequencing data set related to a test nucleic acid molecule sequenced using a first flow cycle order, and a multiple second test sequencing data set can be obtained, each of which is a test sequencing data set related to the same nucleic acid molecule sequenced using a second flow cycle order.The first flow cycle order and the second flow cycle order are different.A match score can be determined that indicates the likelihood that the multiple first sequencing data sets and the multiple second sequencing data sets match one or more candidate sequences (e.g., a target sequence with a preselected short target gene variant, a reference sequence with a preselected sequence without a short target gene variant, or other possible candidate sequences (e.g., haplotypes)), and the presence or absence of a short target gene variant can be called, or the selected candidate sequence can be called.

[0136] FIG. 5 shows an exemplary method for detecting the presence or absence of short gene variants in a test sample. In step 502, one or more first test sequencing data sets are obtained. The one or more first test sequencing data sets can be obtained, for example, by receiving one or more first sequencing data sets or by sequencing one or more nucleic acid molecules. Each of the first test sequencing data sets is associated with a different nucleic acid molecule from the test sample. The first sequencing data sets are determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order. Each of the obtained one or more first test sequencing data sets includes a flow signal at a flow position corresponding to the nucleotide flow. In step 504, one or more second test sequencing data sets are obtained. The one or more second test sequencing data sets can be obtained, for example, by receiving one or more second test sequencing data sets or by sequencing one or more nucleic acid molecules. Each of the second test sequencing data sets is associated with the same nucleic acid molecule as the first test sequencing data set. That is, the nucleic acid molecule is associated with both the first sequencing data set and the second sequencing data set. The second sequencing data set is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order different from the first flow cycle order. Each of the one or more obtained second test sequencing data sets includes a flow signal at a flow position corresponding to the nucleotide flow. In step 506, a match score is determined for each of the first sequencing data set and the second sequencing data set.The match score indicates that the first test sequencing dataset, the test sequencing dataset, or both match the candidate sequence from the one or more candidate sequences. In step 508, the determined match score is used to call the presence or absence of the short genetic variant in the test sample.

[0137] FIG. 6 shows another exemplary method for detecting the presence or absence of short gene variants in a test sample. In step 602, a short target gene variant is selected. The short target gene variant is selected such that the target sequencing data related to the target sequence including the short target gene variant and the sequencing data set related to the reference sequence are different at more than two flow positions, when the target sequencing data set and the reference sequencing data set are obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order or both, and the first flow cycle order and the second flow cycle order are different, and the flow positions correspond to the nucleotide flows. In step 604, one or more first test sequencing data sets are obtained. The one or more first test sequencing data sets can be obtained, for example, by receiving one or more first sequencing data sets or by sequencing one or more nucleic acid molecules. Each of the first test sequencing data sets is associated with a different nucleic acid molecule from the test sample. The first sequencing data set is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order. Each of the obtained one or more first test sequencing data sets includes a flow signal at a flow position corresponding to the nucleotide flow. In step 606, one or more second test sequencing data sets are obtained. The one or more second test sequencing data sets can be obtained, for example, by receiving one or more second test sequencing data sets or by sequencing one or more nucleic acid molecules. Each of the second test sequencing data sets is associated with the same nucleic acid molecule as the first test sequencing data set.That is, the nucleic acid molecule is related to both the first sequencing data set and the second sequencing data set. The second sequencing data set is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order different from the first flow cycle order. Each of the obtained one or more second test sequencing data sets includes a flow signal at a flow position corresponding to the nucleotide flow. In step 608, a match score is determined for each of the first sequencing data set and the second sequencing data set. The match score indicates that the first test sequencing data set, the test sequencing data set, or both match a candidate sequence from one or more candidate sequences (which may include a reference sequence). In step 610, the determined match score is used to call the presence or absence of short gene variants in the test sample.

[0138] In some embodiments, a method for detecting the presence or absence of a short genetic variant in a test sample includes the steps of: (a) obtaining one or more first test sequencing datasets, each first test sequencing dataset associated with a different test nucleic acid molecule from the test sample, the first test sequencing dataset being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (b) obtaining one or more second test sequencing datasets, each second test sequencing dataset associated with the same test nucleic acid molecule as the first test sequencing dataset, the second test sequencing dataset being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (c) determining a match score for one or more candidate sequences for each of the first and second sequencing datasets, wherein the match score indicates a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both match a candidate sequence from the one or more candidate sequences; and (d) calling the presence or absence of short genetic variants in the test sample using the determined match scores.

[0139] In some embodiments, a method for detecting the presence or absence of short genetic variants in a test sample includes the steps of: (a) sequencing one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order to obtain one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows, each first test sequencing dataset being associated with a different test nucleic acid molecule; (b) sequencing the same one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order different from the first flow cycle order to obtain one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows. (c) obtaining one or more second test sequencing datasets comprising flow signals at flow locations corresponding to one or more test nucleic acid molecules as one of the first test sequencing datasets; (b) determining a match score for one or more candidate sequences for each of the first and second sequencing datasets, the match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, match a candidate sequence from the one or more candidate sequences; and (c) calling the presence or absence of short genetic variants in the test sample using the determined match scores.

[0140] In some embodiments, a method for detecting the presence or absence of a short genetic variant in a test sample includes the steps of: (a) obtaining one or more first test sequencing datasets, each first test sequencing dataset associated with a different test nucleic acid molecule from the test sample, and the first test sequencing datasets are determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (b) obtaining one or more second test sequencing datasets, each second test sequencing dataset associated with the same test nucleic acid molecule as the first test sequencing dataset, and the second test sequencing datasets using non-terminating nucleotides provided in a separate nucleotide flow according to a second flow cycle order. (c) determining a match score for one or more candidate sequences for each of the first and second sequencing datasets, the match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences; (d) selecting a candidate sequence from two or more different candidate sequences, the selected candidate sequence having a highest likelihood match with the first test sequencing dataset, the second test sequencing dataset, or both; and (e) calling the presence or absence of short genetic variants in the test sample using the selected candidate sequences.In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more (or three or more, or across one or more flow cycles) flow positions (which positions may be consecutive or non-consecutive) according to the first flow cycle order and / or the second flow cycle order.

[0141] In some embodiments, a method for detecting the presence or absence of short genetic variants in a test sample includes the steps of: (a) sequencing one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order to obtain one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows, where each first test sequencing dataset is associated with a different test nucleic acid molecule; (b) sequencing the same one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order different from the first flow cycle order to obtain one or more second test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows. wherein each second test sequencing dataset is associated with the same test nucleic acid molecule as one of the first test sequencing dataset; (c) for each of the first and second sequencing datasets, determining a match score for one or more candidate sequences, the match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences; (d) selecting a candidate sequence from two or more different candidate sequences, the selected candidate sequence having a highest likelihood match with the first test sequencing dataset, the second test sequencing dataset, or both; and (e) using the selected candidate sequences to call the presence or absence of short genetic variants in the test sample.In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more (or three or more, or across one or more flow cycles) flow positions (which positions may be consecutive or non-consecutive) according to the first flow cycle order and / or the second flow cycle order.

[0142] In some embodiments, a method for detecting the presence or absence of a short genetic variant in a test sample includes the steps of: (a) selecting a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at two or more flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order, the first flow cycle order and the second flow cycle order being different, and the flow positions corresponding to the nucleotide flows; (b) obtaining one or more first test sequencing datasets, wherein each first test sequencing dataset comprises a different test nucleic acid derived from the test sample. (c) obtaining one or more second test sequencing datasets, each of which is associated with the same test nucleic acid molecule as the first test sequencing dataset, and each of which is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a second flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flow; (d) obtaining one or more second test sequencing datasets, each of which is associated with the same test nucleic acid molecule as the first test sequencing dataset, and each of which is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a second flow cycle order, the test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flow;(d) determining a match score for one or more candidate sequences for each of the first and second sequencing datasets, the match score indicating the likelihood that a candidate sequence from the first test sequencing dataset, the second test sequencing dataset, or both matches one or more candidate sequences; and (e) calling the presence or absence of short genetic variants in the test sample using the determined match scores;

[0143] In some embodiments, a method for detecting the presence or absence of a short genetic variant in a test sample includes the steps of: (a) selecting a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at two or more flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order, the first flow cycle order and the second flow cycle order being different, and the flow positions corresponding to the nucleotide flows; (b) sequencing one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to the first flow cycle order to generate one or more flow positions comprising flow signals at flow positions corresponding to the nucleotide flows. (c) sequencing the same one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order to obtain one or more second test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows, each second test sequencing dataset associated with the same test nucleic acid molecule as one of the first test sequencing datasets; (d) determining a match score for each of the first and second sequencing datasets to one or more candidate sequences, the match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences;and (e) calling the presence or absence of the short genetic variant in the test sample using the determined match score;

[0144] In some embodiments, a method for detecting the presence or absence of a short genetic variant in a test sample includes the steps of: (a) selecting a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at two or more flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order, the first flow cycle order and the second flow cycle order being different, and the flow positions corresponding to the nucleotide flows; (b) obtaining one or more first test sequencing datasets, wherein each first test sequencing dataset comprises a different test nucleic acid derived from the test sample. (c) obtaining one or more second test sequencing datasets, each of which is associated with the same test nucleic acid molecule as the first test sequencing dataset, and each of which is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a second flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flow; (d) obtaining one or more second test sequencing datasets, each of which is associated with the same test nucleic acid molecule as the first test sequencing dataset, and each of which is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a second flow cycle order, the test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flow;(d) determining a match score for one or more candidate sequences (which may include a reference sequence) for each of the first and second sequencing datasets, where the match score indicates the likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences; (e) selecting a candidate sequence from two or more different candidate sequences, where the selected candidate sequence has a highest likelihood match with the first test sequencing dataset, the second test sequencing dataset, or both; and (f) using the selected candidate sequences to call the presence or absence of short genetic variants in the test sample. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more (or three or more, or across one or more flow cycles) flow positions (which positions may be consecutive or non-consecutive) according to the first flow cycle order and / or the second flow cycle order;

[0145] In some embodiments, a method for detecting the presence or absence of a short genetic variant in a test sample includes the steps of: (a) selecting a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at two or more flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order, the first flow cycle order and the second flow cycle order being different, and the flow positions corresponding to the nucleotide flows; (b) sequencing one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to the first flow cycle order to generate one or more flow positions comprising flow signals at flow positions corresponding to the nucleotide flows. (c) sequencing the same one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order to obtain one or more second test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows, each second test sequencing dataset associated with the same test nucleic acid molecule as one of the first test sequencing datasets; (d) determining a match score for each of the first and second sequencing datasets to one or more candidate sequences, the match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences;(e) selecting a candidate sequence from two or more different candidate sequences (which may include a reference sequence), the selected candidate sequence having the highest likelihood match with the first test sequencing data set, the second test sequencing data set, or both; and (f) using the selected candidate sequence to call the presence or absence of a short genetic variant in the test sample. In some embodiments, at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ at two or more (or three or more, or over one or more flow cycles) flow positions (which positions may be consecutive or non-consecutive) according to the first flow cycle order and / or the second flow cycle order; Systems, Devices, and Reports

[0146] The operations described above, including those described in connection with the figures, are optionally performed by one or more of the components depicted in Figure 7. It will be apparent to one of ordinary skill in the art how other processes, e.g., combinations or subcombinations of all or some of the operations described above, can be performed based on the components depicted in Figure 7. It will also be apparent to one of ordinary skill in the art how the methods, techniques, systems and devices described herein can be combined with each other, in whole or in part, whether or not those methods, techniques, systems and / or devices are performed and / or provided by the components depicted in Figure 7.

[0147] FIG. 7 illustrates an example of a computing device according to one embodiment. The device 700 may be a host computer connected to a network. The device 700 may also be a client computer or a server. As shown in FIG. 7, the device 700 may be any suitable type of microprocessor-based device, such as a personal computer, a workstation, a server, or a handheld computing device (portable electronic device), such as a phone or tablet. The device may include, for example, one or more of a processor 710, an input device 720, an output device 730, a storage device 740, and a communication device 760. The input device 720 and the output device 730 may generally correspond to those described above and may be either connectable or integral with the computer.

[0148] The input device 720 may be any suitable device for providing input, such as a touch screen, a keyboard or keypad, a mouse, or a voice recognition device. The output device 730 may be any suitable device for providing output, such as a touch panel, a tactile device, or a speaker.

[0149] The storage device 740 may be any suitable device that provides storage, such as electronic, magnetic or optical memory, including RAM, cache memory, a hard drive or a removable storage disk. The communication device 760 may include any suitable device that can send and receive signals using a network, such as a network interface chip or device. The components of a computer may be connected in any suitable manner, such as via a physical bus or wirelessly.

[0150] Software 750, which may be stored in memory 740 and executed by processor 710, may include, for example, programming that embodies functionality of the present disclosure (e.g., as embodied in the devices described above).

[0151] The software 750 may also be stored and / or transported in any non-transitory computer-readable storage medium for use with or in connection with an instruction execution system, apparatus, or device, such as those described above, from which instructions associated with the software can be retrieved and executed. In the context of this disclosure, a computer-readable storage medium may be any medium capable of containing or storing programming, such as storage device 740, for use with or in connection with an instruction execution system, apparatus, or device.

[0152] The software 750 may also be propagated in any transport medium for use with or in connection with an instruction execution system, apparatus, or device, such as those described above, that can retrieve instructions associated with the software from and execute the instructions in the instruction execution system, apparatus, or device. In the context of this disclosure, a transport medium may be any medium that can convey, propagate, or transport programming for use with or in connection with an instruction execution system, apparatus, or device. Transport-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, or infrared wired or wireless propagation media.

[0153] The device 700 can be connected to a network, which can be any suitable type of interconnected communication system. The network can implement any suitable communication protocol and can be protected by any suitable security protocol. The network can include any suitable configuration of network links capable of communicating and receiving network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.

[0154] The device 700 can implement any operating system suitable for operation in a network. The software 750 can be written in any suitable programming language, for example, C, C++, Java, or Python. In various embodiments, application software embodying functionality of the present disclosure can be deployed, for example, in different configurations, for example, in a client / server configuration, or through a web browser as a web-based application or web service.

[0155] The methods described herein may further include, as required, a step of reporting the information determined using the analytical method and / or a step of generating a report that includes the information determined using the analytical method. For example, in some embodiments, the method may further include a step of reporting or generating a report that includes __ regarding the identification of variants in polynucleotides derived from a subject (e.g., in the subject's genome). The information reported or in the report may relate, for example, to the loci of coupled sequencing read pairs mapped to a reference sequence, the variants detected (e.g., the structural variants detected or the SNPs detected), one or more assembled consensus sequences, and / or the validation statistics for one or more assembled consensus sequences. The report may be distributed or the information may be reported to a recipient, for example, a clinician, a subject, or a researcher.

[0156] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) selecting, with the one or more processors, a short target gene variant, where a target sequencing dataset associated with a target sequence comprising the preselected short target gene variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the target sequencing dataset and the reference sequencing dataset are obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the flow positions corresponding to the nucleotide flows; (b) obtaining one or more test sequencing datasets, where each test sequencing dataset includes a test The method includes the steps of: (a) determining a sequence of a nucleic acid molecule associated with the nucleic acid molecule, each test nucleic acid molecule at least partially overlapping a locus associated with a short target gene variant and derived from a test sample, by sequencing the test nucleic acid molecule using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the test sequencing dataset comprising flow signals at a plurality of flow positions; (b) determining a match score for each of the test nucleic acid molecules associated with the test sequencing dataset, the match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence, or the match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (c) calling the presence or absence of the short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further comprises generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprising the short target gene variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0157] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) selecting short target gene variants, where a target sequencing dataset associated with a target sequence that includes the short target gene variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the flow positions corresponding to the nucleotide flows; (b) selecting short target gene variants using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the flow positions corresponding to the nucleotide flows; The method includes the steps of: (a) sequencing a plurality of test nucleic acid molecules to obtain one or more test sequencing data sets comprising flow signals at a plurality of flow locations, each test sequencing data set being associated with a test nucleic acid molecule, each test nucleic acid molecule at least partially overlapping a locus associated with a short target gene variant, and derived from a test sample; (b) determining, for each of the test nucleic acid molecules associated with the test sequencing data sets, a match score indicating the likelihood that the test sequencing data set associated with the nucleic acid molecule matches a target sequence, or a match score indicating the likelihood that the test sequencing data set associated with the nucleic acid molecule matches a reference sequence; and (c) calling the presence or absence of the short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for a subject associated with the test sample, the biomarker panel comprising the short target gene variant. In some embodiments, the target sequencing dataset and the reference sequencing dataset differ at more than two flow locations (e.g., at more than two consecutive flow locations or at more than two non-consecutive flow locations).In some embodiments, the target sequencing dataset and the reference sequencing dataset differ over one or more flow cycles.

[0158] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) preselecting a short target gene variant, where a target sequencing dataset associated with a target sequence comprising the preselected short target gene variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the flow positions correspond to nucleotide flows, when the target sequencing dataset and the reference sequencing dataset are obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order; (b) obtaining one or more test sequencing datasets, where each test sequencing dataset is associated with a test nucleic acid molecule, and each test nucleic acid The method includes the steps of: (a) determining, for each of the test nucleic acid molecules associated with the test sequencing dataset, a match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence, or a match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (b) calling the presence or absence of the preselected short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprising the short target gene variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0159] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) preselecting a short target gene variant, wherein a target sequencing dataset associated with a target sequence that includes the preselected short target gene variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the flow positions corresponding to the nucleotide flows; (b) preselecting a short target gene variant using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the flow positions corresponding to the nucleotide flows. The method includes the steps of: (a) sequencing one or more test nucleic acid molecules to obtain one or more test sequencing datasets comprising flow signals at a plurality of flow locations, each test sequencing dataset being associated with a test nucleic acid molecule, each test nucleic acid molecule at least partially overlapping a locus associated with a short target gene variant, and derived from a test sample; (b) determining, for each of the test nucleic acid molecules associated with the test sequencing datasets, a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence, or a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (c) calling the presence or absence of the preselected short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for a subject associated with the test sample, the biomarker panel comprising the short target gene variant. In some embodiments, the target sequencing dataset and the reference sequencing dataset differ at more than two flow locations (e.g., at more than two consecutive flow locations or at more than two non-consecutive flow locations).In some embodiments, the target sequencing dataset and the reference sequencing dataset differ over one or more flow cycles.

[0160] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) preselecting a short target gene variant and a flow cycle order, where a target sequencing dataset associated with a target sequence comprising the preselected short target gene variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the flow positions correspond to nucleotide flows, when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to the preselected flow cycle order; (b) obtaining one or more test sequencing datasets, where each test sequencing dataset includes a test nucleic acid molecule associated with a test (c) determining, for each of the test nucleic acid molecules associated with the test sequencing dataset, a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence or a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (d) calling the presence or absence of the preselected short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further comprises generating a personalized biomarker panel for a subject associated with the test sample, the biomarker panel comprising the short target gene variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0161] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) preselecting a short target gene variant and a flow cycle order, where a target sequencing dataset associated with a target sequence comprising the preselected short target gene variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to the preselected flow cycle order, the flow positions corresponding to the nucleotide flows; (b) preselecting a flow cycle order for a non-terminating nucleotide variant and a reference sequencing dataset associated with a reference sequence, the flow positions corresponding to the nucleotide flows when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to the preselected flow cycle order. The method further comprises the steps of: (a) sequencing one or more test nucleic acid molecules using a terminating nucleotide to obtain one or more test sequencing data sets comprising flow signals at a plurality of flow locations, each test sequencing data set being associated with a test nucleic acid molecule, each test nucleic acid molecule at least partially overlapping a locus associated with a short target gene variant and derived from a test sample; (b) determining, for each of the test nucleic acid molecules associated with the test sequencing data sets, a match score indicative of the likelihood that the test sequencing data set associated with the nucleic acid molecule matches a target sequence or a match score indicative of the likelihood that the test sequencing data set associated with the nucleic acid molecule matches a reference sequence; and (c) calling the presence or absence of the preselected short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further comprises generating a personalized biomarker panel for a subject associated with the test sample, the biomarker panel comprising the short target gene variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0162] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) obtaining one or more first test sequencing datasets, each first test sequencing dataset associated with a different test nucleic acid molecule from a test sample, the first test sequencing datasets being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (b) obtaining one or more second test sequencing datasets, each second test sequencing dataset associated with a different test nucleic acid molecule from the test sample, the first test sequencing datasets being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (c) determining a match score for one or more candidate sequences for each of the first and second sequencing datasets, the match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, match a candidate sequence from the one or more candidate sequences; and (d) calling the presence or absence of short genetic variants in the test sample using the determined match scores.

[0163] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) sequencing one or more test nucleic acid molecules from a test sample using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order to obtain one or more first test sequencing datasets including flow signals at flow positions corresponding to the nucleotide flows, each first test sequencing dataset being associated with a different test nucleic acid molecule; (b) sequencing the same one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order that is different from the first flow cycle order. to obtain one or more second test sequencing datasets comprising flow signals at flow positions corresponding to nucleotide flow, each second test sequencing dataset associated with the same test nucleic acid molecule as one of the first test sequencing datasets; (c) determining a match score for one or more candidate sequences for each of the first and second sequencing datasets, the match score indicating the likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, match a candidate sequence from the one or more candidate sequences; and (d) calling the presence or absence of short genetic variants in the test sample using the determined match scores.

[0164] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) obtaining one or more first test sequencing datasets, each first test sequencing dataset associated with a different test nucleic acid molecule from a test sample, the first test sequencing dataset being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (b) obtaining one or more second test sequencing datasets, each second test sequencing dataset associated with the same test nucleic acid molecule as the first test sequencing dataset, the second test sequencing dataset being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a second flow cycle order. (c) determining a match score for one or more candidate sequences for each of the first and second sequencing datasets, the match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences; (d) selecting a candidate sequence from two or more different candidate sequences, the selected candidate sequence having a highest likelihood match with the first test sequencing dataset, the second test sequencing dataset, or both; and (e) calling the presence or absence of a short genetic variant in the test sample using the selected candidate sequence.In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more (or three or more, or across one or more flow cycles) flow positions (which positions may be consecutive or non-consecutive) according to the first flow cycle order and / or the second flow cycle order.

[0165] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) sequencing one or more test nucleic acid molecules from a test sample using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order to obtain one or more first test sequencing data sets comprising flow signals at flow positions corresponding to the nucleotide flows, each first test sequencing data set being associated with a different test nucleic acid molecule; (b) sequencing the same one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order different from the first flow cycle order to obtain one or more second test sequencing data sets comprising flow signals at flow positions corresponding to the nucleotide flows. (c) determining a match score for one or more candidate sequences for each of the first and second sequencing datasets, the match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences; (d) selecting a candidate sequence from two or more different candidate sequences, the selected candidate sequence having a highest likelihood match with the first test sequencing dataset, the second test sequencing dataset, or both; and (e) calling the presence or absence of short genetic variants in the test sample using the selected candidate sequences.In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more (or three or more, or across one or more flow cycles) flow positions (which positions may be consecutive or non-consecutive) according to the first flow cycle order and / or the second flow cycle order.

[0166] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) selecting short target gene variants, wherein a target sequencing dataset associated with a target sequence comprising the short target gene variant and a reference sequencing dataset associated with a reference sequence differ at two or more flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order, and the first flow cycle order and the second flow cycle order are different, and the flow positions correspond to the nucleotide flows; (b) obtaining one or more first test sequencing datasets, wherein each first test sequencing dataset (c) obtaining one or more second test sequencing datasets, each of which is associated with the same test nucleic acid molecules as the first test sequencing dataset, and each of which is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (d) obtaining one or more second test sequencing datasets, each of which is associated with the same test nucleic acid molecules as the first test sequencing dataset, and each of which is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order, the test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows;(d) determining a match score for one or more candidate sequences for each of the first and second sequencing datasets, the match score indicating the likelihood that a candidate sequence from the first test sequencing dataset, the second test sequencing dataset, or both matches one or more candidate sequences; and (e) calling the presence or absence of short genetic variants in the test sample using the determined match scores.

[0167] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) selecting short target gene variants, wherein a target sequencing dataset associated with a target sequence comprising the short target gene variant and a reference sequencing dataset associated with a reference sequence differ at two or more flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order, the first flow cycle order and the second flow cycle order being different, and the flow positions corresponding to the nucleotide flows; (b) sequencing one or more test nucleic acid molecules from a test sample using non-terminating nucleotides provided in separate nucleotide flows according to the first flow cycle order, and selecting at the flow positions corresponding to the nucleotide flows. (c) sequencing the same one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order to obtain one or more second test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows, each second test sequencing dataset being associated with the same test nucleic acid molecule as one of the first test sequencing datasets; (d) determining a match score for each of the first and second sequencing datasets to one or more candidate sequences, the match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences;and (e) calling the presence or absence of the short genetic variant in the test sample using the determined match score;

[0168] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) selecting short target gene variants, wherein a target sequencing dataset associated with a target sequence comprising the short target gene variant and a reference sequencing dataset associated with a reference sequence differ at two or more flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order, and the first flow cycle order and the second flow cycle order are different, and the flow positions correspond to the nucleotide flows; (b) obtaining one or more first test sequencing datasets, wherein each first test sequencing dataset (c) obtaining one or more second test sequencing datasets, each of which is associated with the same test nucleic acid molecules as the first test sequencing dataset, and each of which is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (d) obtaining one or more second test sequencing datasets, each of which is associated with the same test nucleic acid molecules as the first test sequencing dataset, and each of which is determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order, the test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows;(d) determining a match score for one or more candidate sequences (which may include a reference sequence) for each of the first and second sequencing datasets, where the match score indicates the likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences; (e) selecting a candidate sequence from two or more different candidate sequences, where the selected candidate sequence has a highest likelihood match with the first test sequencing dataset, the second test sequencing dataset, or both; and (f) using the selected candidate sequences to call the presence or absence of short genetic variants in the test sample. In some embodiments, at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more (or three or more, or across one or more flow cycles) flow positions (which positions may be consecutive or non-consecutive) according to the first flow cycle order and / or the second flow cycle order;

[0169] In some embodiments, there is a system that includes one or more processors and a non-transitory computer readable medium storing one or more programs including instructions for: (a) selecting short target gene variants, wherein a target sequencing dataset associated with a target sequence comprising the short target gene variant and a reference sequencing dataset associated with a reference sequence differ at two or more flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order, the first flow cycle order and the second flow cycle order being different, and the flow positions corresponding to the nucleotide flows; (b) sequencing one or more test nucleic acid molecules from a test sample using non-terminating nucleotides provided in separate nucleotide flows according to the first flow cycle order, and selecting at the flow positions corresponding to the nucleotide flows. (c) sequencing the same one or more test nucleic acid molecules from the test sample using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order to obtain one or more second test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows, each second test sequencing dataset being associated with the same test nucleic acid molecule as one of the first test sequencing datasets; (d) determining a match score for each of the first and second sequencing datasets to one or more candidate sequences, the match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences;(e) selecting a candidate sequence from two or more different candidate sequences (which may include a reference sequence), the selected candidate sequence having the highest likelihood match with the first test sequencing data set, the second test sequencing data set, or both; and (f) using the selected candidate sequence to call the presence or absence of a short genetic variant in the test sample. In some embodiments, at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ at two or more (or three or more, or across one or more flow cycles) flow positions (which positions may be consecutive or non-consecutive) according to the first flow cycle order and / or the second flow cycle order;

[0170] In some embodiments, the methods described herein are computer-implemented methods that may be performed using one or more of the components illustrated in Figure 7. For example, in some embodiments, a computer-implemented method for detecting short genetic variants in a test sample includes the steps of: (a) using one or more processors to select a short target genetic variant, where a target sequencing dataset associated with a target sequence that includes the short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the flow positions correspond to nucleotide flows, when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order; (b) receiving, with the one or more processors, one or more test sequencing datasets, where each test sequencing dataset is associated with a test nucleic acid molecule, and each test nucleic acid molecule is associated with a short target genetic variant; the test sequence dataset comprises a plurality of preselected short target gene variants each of which at least partially overlaps a locus corresponding to a target sequence and is derived from a test sample, wherein one or more test sequencing datasets are determined by sequencing the test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a flow cycle order, the test sequencing dataset comprising flow signals at a plurality of flow locations; (c) using the one or more processors to determine, for each of the test nucleic acid molecules associated with the test sequencing dataset, a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence or a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (d) calling the presence or absence of the preselected short target gene variant in the test sample using the one or more processors and the one or more determined match scores.In some embodiments, the method further comprises generating a personalized biomarker panel for the subject related to the test sample, the biomarker panel comprises short target gene variants.In some embodiments, the target sequencing data set and the reference sequencing data set are different at more than two flow positions (e.g., more than two consecutive flow positions, or more than two non-consecutive flow positions).In some embodiments, the target sequencing data set and the reference sequencing data set are different over one or more flow cycles.

[0171] In some embodiments, a computer-implemented method for detecting short genetic variants in a test sample includes the steps of: (a) preselecting, using one or more processors, a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the preselected short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, the flow positions corresponding to nucleotide flows, when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order; (b) receiving, with the one or more processors, one or more test sequencing datasets, each test sequencing dataset associated with a test nucleic acid molecule, each test nucleic acid molecule comprising the preselected short target genetic variant; The method includes the steps of: (a) determining, by the one or more processors, a match score for each of the test nucleic acid molecules associated with the test sequencing dataset, the match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence or the match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (b) calling, by the one or more processors, the presence or absence of the preselected short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel including the short target gene variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0172] In some embodiments, a computer-implemented method for detecting short genetic variants in a test sample includes: (a) preselecting, using one or more processors, a short target genetic variant and a flow cycle order, where a target sequencing dataset associated with a target sequence comprising the preselected short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to the preselected flow cycle order, the flow positions corresponding to the nucleotide flows; (b) receiving, with the one or more processors, one or more test sequencing datasets, where each test sequencing dataset is associated with a test nucleic acid molecule, and each test nucleic acid molecule comprises a short target genetic variant and a flow cycle order, where the flow positions correspond to the nucleotide flows; The method includes the steps of: (a) determining, by the one or more processors, a match score for each of the test nucleic acid molecules associated with the test sequencing dataset, the match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence or the match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (b) calling, by the one or more processors, the presence or absence of the preselected short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for a subject associated with the test sample, the biomarker panel including the short target gene variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0173] In some embodiments, a computer-implemented method for detecting short genetic variants in a test sample includes the steps of: (a) selecting, with one or more processors, a short target genetic variant, where a target sequencing dataset associated with a target sequence comprising the short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the flow positions correspond to nucleotide flows, when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order; (b) receiving, with the one or more processors, one or more test sequencing datasets, where each test sequencing dataset is associated with a test nucleic acid molecule, and each test nucleic acid molecule is associated with a short target genetic variant; The method includes the steps of: (a) determining, by sequencing the test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, one or more test sequencing datasets that at least partially overlap the relevant locus and are derived from the test sample, the test sequencing datasets comprising flow signals at a plurality of flow locations; (b) determining, by the one or more processors, for each of the test nucleic acid molecules associated with the test sequencing datasets, a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence or a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (c) using the one or more determined match scores to call the presence or absence of the short target gene variant in the test sample. In some embodiments, the method further includes generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprising the short target gene variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0174] In some embodiments, a computer-implemented method for detecting short genetic variants in a test sample includes: (a) preselecting, with one or more processors, a short target genetic variant, wherein a target sequencing dataset associated with a target sequence comprising the preselected short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, the flow positions corresponding to the nucleotide flows, when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order; (b) receiving, with the one or more processors, one or more test sequencing datasets, each test sequencing dataset associated with a test nucleic acid molecule, each test nucleic acid molecule comprising the preselected short target genetic variant; The method includes the steps of: (a) determining, by the one or more processors, a match score for each of the test nucleic acid molecules associated with the test sequencing dataset, the match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence or the match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (b) calling, by the one or more processors, the presence or absence of the preselected short target gene variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for the subject associated with the test sample, the biomarker panel comprising the short target gene variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles.

[0175] In some embodiments, a computer-implemented method for detecting short genetic variants in a test sample includes: (a) preselecting, with one or more processors, a short target genetic variant and a flow cycle order, where a target sequencing dataset associated with a target sequence comprising the preselected short target genetic variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions, where the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to the preselected flow cycle order, the flow positions corresponding to the nucleotide flows; (b) receiving, with the one or more processors, one or more test sequencing datasets, where each test sequencing dataset is associated with a test nucleic acid molecule, and each test nucleic acid molecule comprises a preselected short target genetic variant and a flow cycle order, where the flow positions correspond to the nucleotide flows; The method includes the steps of: (a) determining, by the one or more processors, a match score for each of the test nucleic acid molecules associated with the test sequencing dataset, the match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence or the match score indicating the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (b) calling, by the one or more processors, the presence or absence of the preselected short target genetic variant in the test sample using the one or more determined match scores. In some embodiments, the method further includes generating a personalized biomarker panel for a subject associated with the test sample, the biomarker panel comprising the short target genetic variant.In some embodiments, the target and reference sequencing data sets differ at more than two flow locations (e.g., at more than two consecutive flow locations, or at more than two non-consecutive flow locations). In some embodiments, the target and reference sequencing data sets differ over one or more flow cycles. Exemplary embodiments

[0176] The following embodiments are illustrative and are not intended to limit the scope of the invention as claimed.

[0177] Embodiment 1. A method for detecting short genetic variants in a test sample, comprising: (a) selecting short target gene variants, wherein a target sequencing dataset associated with a target sequence comprising the short target gene variant and a reference sequencing dataset associated with a reference sequence differ at more than two flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the flow positions corresponding to the nucleotide flows; (b) obtaining one or more test sequencing datasets, each test sequencing dataset associated with a test nucleic acid molecule, each test nucleic acid molecule at least partially overlapping a locus associated with a short target gene variant and derived from a test sample, the one or more test sequencing datasets being determined by sequencing the test nucleic acid molecule using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order, the test sequencing dataset comprising flow signals at a plurality of flow locations; (c) determining, for each test nucleic acid molecule associated with the test sequencing dataset, a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a target sequence or a match score indicative of the likelihood that the test sequencing dataset associated with the nucleic acid molecule matches a reference sequence; and (d) calling the presence or absence of the short target gene variant in the test sample using the one or more determined match scores. The method includes:

[0178] Embodiment 2. The method of embodiment 1, wherein the obtaining step comprises sequencing the test nucleic acid molecule using non-terminating nucleotides provided in separate nucleotide flows according to a flow cycle order.

[0179] Embodiment 3. The method of embodiment 1 or embodiment 2, wherein the short target gene variant is preselected prior to calling the presence or absence of the short target gene variant in the test sample.

[0180] Embodiment 4. The method of embodiment 1 or embodiment 2, wherein the short target gene variants are selected based on the confidence of the call after the step of calling the presence or absence of the short target gene variants in the test sample.

[0181] Embodiment 5. The method of any one of embodiments 1 to 4, comprising generating a personalized biomarker panel for a subject associated with the test sample, wherein the biomarker panel comprises short target gene variants.

[0182] Embodiment 6. The method of any one of embodiments 1 to 5, comprising the step of selecting a flow cycle order.

[0183] Embodiment 7. The method of any one of embodiments 1 to 6, wherein the target sequencing dataset is a predicted target sequencing dataset or the reference sequencing dataset is a predicted reference sequencing dataset.

[0184] Embodiment 8. The method of embodiment 7, wherein the target sequencing dataset and the predicted reference sequencing dataset are obtained by in silico sequencing of the target sequence and the reference sequence.

[0185] Embodiment 9. The method of any one of embodiments 1 to 8, wherein the target sequencing data set and the reference sequencing data differ at more than two non-contiguous flow locations.

[0186] Embodiment 10. The method of any one of embodiments 1 to 9, wherein the target sequencing data set and the reference sequencing data differ at more than two consecutive flow locations.

[0187] Embodiment 11. The method of any one of embodiments 1 to 10, wherein the target sequence and the reference sequence differ at X base positions, and the target sequencing data set and the reference sequencing data set differ at (X+2) or more consecutive flow positions.

[0188] Embodiment 12. The method of embodiment 11, wherein the difference in the (X+2) flow positions comprises a difference between a value substantially equal to zero and a value substantially greater than zero.

[0189] Embodiment 13. The method of any one of embodiments 1 to 12, wherein the target sequencing dataset and the reference sequencing dataset differ over one or more flow cycles.

[0190] Embodiment 14. The method of any one of embodiments 1 to 13, wherein the flow signal comprises a base count indicating the number of bases of the test nucleic acid molecule sequenced at each flow location.

[0191] Embodiment 15. The method of any one of embodiments 1 to 14, wherein the flow signal includes a statistical parameter indicative of the likelihood of at least one base count at each flow position, the base count indicating the number of bases of the sequenced test nucleic acid molecule at the flow position.

[0192] Embodiment 16. The method of any one of embodiments 1 to 15, wherein the flow signal includes a statistical parameter indicative of the likelihood of a plurality of base counts at each flow location, each base count indicative of the number of bases of the sequenced test nucleic acid molecule at the flow location.

[0193] Embodiment 17. Step (c) comprises: selecting a statistical parameter at each flow location in the test sequencing data set that corresponds to a base count of the target sequence at that flow location, and determining a match score indicating the likelihood that the test sequencing data set matches the target sequence; or selecting a statistical parameter at each flow location in the test sequencing data set that corresponds to a base count of the reference sequence at that flow location, and determining a match score indicating the likelihood that the test sequencing data set matches the reference sequence. 17. The method of embodiment 16, comprising:

[0194] Embodiment 18. The method of embodiment 17, wherein the match score determined in step (c) is a combined value of the selected statistical parameters across the flow positions in the test sequencing dataset.

[0195] Embodiment 19. The method of any one of embodiments 1 to 18, wherein step (c) comprises determining a match score indicating the likelihood that the test sequencing dataset matches the target sequence.

[0196] Embodiment 20. The method of any one of embodiments 1 to 19, wherein step (c) comprises determining a match score indicating the likelihood that the test sequencing dataset matches the reference sequence.

[0197] Embodiment 21. The method of any one of embodiments 1 to 20, wherein the one or more test sequencing datasets comprise a plurality of test sequencing datasets.

[0198] Embodiment 22. The method of embodiment 21, wherein the presence or absence of short target gene variants is called separately for each of the one or more test sequencing datasets.

[0199] Embodiment 23 The method of embodiment 21 or 22, wherein at least a portion of the plurality of test sequencing data sets relate to different test nucleic acid molecules having different test sequencing start positions.

[0200] Embodiment 24. The method of any one of embodiments 1 to 23, wherein the flow cycle sequence includes four separate flows that are repeated in the same sequence.

[0201] Embodiment 25. The method of any one of embodiments 1 to 24, wherein the flow cycle sequence includes five or more separate flows.

[0202]

[0023] Embodiment 26. Selecting short target gene variants using one or more processors; obtaining one or more test sequencing data sets by receiving the one or more test sequencing data sets at one or more processors; determining one or more match scores using one or more processors; and calling the presence or absence of short target gene variants in the test sample using one or more processors. 26. The method of any one of embodiments 1 to 25, which is a computer-implemented method comprising:

[0203] 27. One or more processors; A non-transitory computer-readable medium storing one or more programs including instructions for performing the method of any one of claims 1 to 26. A system including:

[0204] Embodiment 28. A method for detecting short genetic variants in a test sample, comprising: (a) obtaining one or more first test sequencing datasets, each first test sequencing dataset associated with a different test nucleic acid molecule derived from a test sample, the first test sequencing datasets being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (b) obtaining one or more second test sequencing datasets, each second test sequencing dataset associated with the same test nucleic acid molecules as the first test sequencing dataset, the second test sequencing datasets being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided in separate nucleotide flows according to a second flow cycle order, the first flow cycle order and the second flow cycle order being different, and the test sequencing datasets comprising flow signals at flow positions corresponding to the nucleotide flows; (c) determining a match score for each of the first and second sequencing datasets for one or more candidate sequences, the match score indicating a likelihood that a candidate sequence from the first test sequencing dataset, the second test sequencing dataset, or both matches the one or more candidate sequences; and (d) calling the presence or absence of the short genetic variant in the test sample using the determined match score. The method includes:

[0205] Embodiment 29. The method of embodiment 28, comprising sequencing the test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a first flow cycle order, and sequencing the test nucleic acid molecules using non-terminating nucleotides provided in a separate nucleotide flow according to a second flow cycle order.

[0206] Embodiment 30. The method of embodiment 28 or 29, wherein the match score indicates the likelihood that the first test sequencing dataset matches the candidate sequence, or the likelihood that the second test sequencing dataset matches the candidate sequence.

[0207] Embodiment 31. The method of embodiment 28 or 29, wherein the match score indicates the likelihood that both the first test sequencing dataset and the second sequencing dataset match the candidate sequence.

[0208] Embodiment 32. The one or more candidate sequences include two or more different candidate sequences, and the method comprises, for each of the nucleic acid molecules associated with the first sequencing data set and the second sequencing data set: selecting a candidate sequence from two or more different candidate sequences, the selected candidate sequence having a highest likelihood match with the first test sequencing data set, the second test sequencing data set, or both; and Using the selected candidate sequences to call the presence or absence of short genetic variants in the test sample. 32. The method of any one of embodiments 28 to 31, comprising:

[0209] Embodiment 33. The method of embodiment 32, wherein at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more flow positions according to the first flow cycle order or the second flow cycle order.

[0210] Embodiment 34. The method of embodiment 32, wherein at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more flow positions according to both the first flow cycle order and the second flow cycle order.

[0211] Embodiment 35. The method of embodiment 32, wherein at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more non-consecutive flow positions according to the first flow cycle order or the second flow cycle order.

[0212] Embodiment 36. The method of embodiment 32, wherein at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more non-consecutive flow positions according to both the first flow cycle order and the second flow cycle order.

[0213] Embodiment 37. The method of embodiment 32, wherein at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more consecutive flow positions according to the first flow cycle order or the second flow cycle order.

[0214] Embodiment 38. The method of embodiment 32, wherein at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at two or more consecutive flow positions according to both the first flow cycle sequence and the second flow cycle sequence.

[0215] Embodiment 39. The method of embodiment 32, wherein at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at three or more flow positions according to the first flow cycle order or the second flow cycle order.

[0216] Embodiment 40. The method of embodiment 32, wherein at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ at three or more flow positions according to both the first flow cycle order and the second flow cycle order.

[0217] Embodiment 41. The method of embodiment 32, wherein at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ at X base positions, and the test sequencing data set associated with the test nucleic acid molecule and at least one non-selected candidate sequence from the two or more different candidate sequences differ at (X+2) or more flow positions according to the first flow cycle order or the second flow cycle order.

[0218] Embodiment 42. The method of embodiment 32, wherein at least one non-selected candidate sequence from the two or more different candidate sequences and the selected candidate sequence differ at X base positions, and the test sequencing data set associated with the test nucleic acid molecule and at least one non-selected candidate sequence from the two or more different candidate sequences differ at (X+2) or more flow positions according to both the first flow cycle order and the second flow cycle order.

[0219] Embodiment 43. The method of embodiment 41 or 42, wherein the difference in the (X+2) flow positions comprises a difference between a value substantially equal to zero and a value substantially greater than zero.

[0220] Embodiment 44. The method of embodiment 32, wherein at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ over one or more flow cycles according to the first flow cycle order or the second flow cycle order.

[0221] Embodiment 45. The method of embodiment 32, wherein at least one non-selected candidate sequence from two or more different candidate sequences and the selected candidate sequence differ over one or more flow cycles according to both the first flow cycle order and the second flow cycle order.

[0222] Embodiment 46 The method of any one of embodiments 28 to 45, wherein the flow signal comprises a base count indicating the number of bases of the test nucleic acid molecule sequenced at each flow location.

[0223] Embodiment 47. The method of any one of embodiments 28 to 46, wherein the flow signal includes a statistical parameter indicative of the likelihood of at least one base count at each flow position, the base count indicating the number of bases of the sequenced test nucleic acid molecule at the flow position.

[0224] Embodiment 48. The method of any one of embodiments 28 to 47, wherein the flow signal includes a statistical parameter indicating the likelihood of a plurality of base counts at each flow position, each base count indicating the number of bases of the sequenced test nucleic acid molecule at the flow position.

[0225] Embodiment 49. The method of embodiment 48, wherein the step of determining a match score includes selecting, for each of one or more different candidate sequences, a statistical parameter at each flow position in the first test sequencing data set and the second test sequencing data set, the statistical parameter corresponding to the base count at that flow position of the candidate sequence.

[0226] Embodiment 50. The method of embodiment 49, comprising generating, for one or more different candidate sequences, a candidate sequencing data set comprising base counts of the candidate sequences at each flow location.

[0227] Embodiment 51. The method of embodiment 50, wherein the candidate sequencing dataset is generated in silico.

[0228] Embodiment 52. The method of any one of embodiments 49 to 51, wherein the match score is a combined value of the selected statistical parameter across the flow positions in the first test sequencing data set and the second test sequencing data set.

[0229] Embodiment 53 The method of any one of embodiments 28 to 52, wherein at least a portion of the test nucleic acid molecules have different sequencing start positions.

[0230] Embodiment 54. A step of selecting short target gene variants, wherein a target sequencing dataset associated with a target sequence comprising the short target gene variant and a reference sequencing dataset associated with a reference sequence differ at two or more flow positions when the target sequencing dataset and the reference sequencing dataset were obtained by sequencing the target sequence using non-terminating nucleotides provided in separate nucleotide flows according to a first flow cycle order or a second flow cycle order, and the first flow cycle order and the second flow cycle order are different, and the flow positions correspond to the nucleotide flows. Including, The one or more candidate sequences include a target sequence and a reference sequence; The method of any one of embodiments 28 to 52.

[0231] Embodiment 55. The method of embodiment 54, wherein the short target gene variant is preselected prior to calling the presence or absence of the short target gene variant in the test sample.

[0232] Embodiment 56 The method of embodiment 54, wherein the short target gene variant is selected based on the confidence of the call after the step of calling the presence or absence of the short target gene variant in the test sample.

[0233] Embodiment 57. The method of embodiment 56, comprising generating an individualized biomarker panel for a subject associated with the test sample, wherein the biomarker panel comprises short target gene variants present in the test sample.

[0234] Embodiment 58. The method of any one of embodiments 54 to 57, wherein the reference sequencing data set is obtained by determining an expected reference sequencing data set when the reference sequence is sequenced using non-terminating nucleotides provided in separate flows according to the first flow cycle order or the second flow cycle order.

[0235] Embodiment 59. The method of any one of embodiments 54 to 57, wherein the reference sequencing data set is obtained by determining an expected reference sequencing data set when the reference sequence is sequenced using non-terminating nucleotides provided in separate flows according to both the first flow cycle order and the second flow cycle order.

[0236] Embodiment 60. The method of any one of embodiments 54 to 57, wherein the target sequence and the reference sequence differ at two or more flow positions according to both the first flow cycle sequence and the second flow cycle sequence.

[0237] Embodiment 61. The method of any one of embodiments 54 to 57, wherein the target sequence and the reference sequence differ at two or more non-consecutive flow positions according to the first flow cycle sequence or the second flow cycle sequence.

[0238] Embodiment 62. The method of any one of embodiments 54 to 57, wherein the target sequence and the reference sequence differ at two or more non-contiguous flow positions according to both the first flow cycle sequence and the second flow cycle sequence.

[0239] Embodiment 63. The method of any one of embodiments 54 to 57, wherein the target sequence and the reference sequence differ at two or more consecutive flow positions according to the first flow cycle sequence or the second flow cycle sequence.

[0240] Embodiment 64. The method of any one of embodiments 54 to 57, wherein the target sequence and the reference sequence differ at two or more consecutive flow positions according to both the first flow cycle sequence and the second flow cycle sequence.

[0241] Embodiment 65. The method of any one of embodiments 54 to 57, wherein the target sequence and the reference sequence differ at three or more flow positions according to the first flow cycle sequence or the second flow cycle sequence.

[0242] Embodiment 66. The method of any one of embodiments 54 to 57, wherein the target sequence and the reference sequence differ at three or more flow positions according to both the first flow cycle sequence and the second flow cycle sequence.

[0243] Embodiment 67. The method of any one of embodiments 54 to 57, wherein the target sequence and the reference sequence differ over two or more flow cycles according to the first flow cycle order or the second flow cycle order.

[0244] Embodiment 68. The method of any one of embodiments 54 to 57, wherein the target sequence and the reference sequence differ over two or more flow cycles according to both the first flow cycle order and the second flow cycle order.

[0245] Embodiment 69. The method of any one of embodiments 28 to 68, wherein the first flow cycle sequence or the second flow cycle sequence includes four separate flows repeated in the same sequence.

[0246] Embodiment 70. The method of any one of embodiments 28 to 68, wherein the first flow cycle sequence or the second flow cycle sequence includes five or more separate flows repeated in the same sequence.

[0247] Embodiment 71. Sequencing a test nucleic acid molecule, comprising providing non-terminating nucleotides in separate nucleotide flows according to a first flow cycle order, extending a sequencing primer, and detecting the presence or absence of nucleotide incorporation into the sequencing primer after each nucleotide flow to generate a first test sequencing data set; removing the extended sequencing primer; and Sequencing the same test nucleic acid molecule, comprising providing non-terminating nucleotides in separate nucleotide flows according to a second flow cycle order, extending the sequencing primer, and detecting the presence or absence of nucleotide incorporation into the sequencing primer after each nucleotide flow to generate a second test sequencing data set. 71. The method of any one of embodiments 28 to 70, comprising:

[0248] 72. Receiving one or more first sequencing data sets at one or more processors; receiving one or more first sequencing datasets at one or more processors; determining a match score using one or more processors; and calling the presence or absence of short target gene variants in the test sample using one or more processors. 72. The method of any one of embodiments 28 to 71, which is a computer-implemented method comprising:

[0249] 73. One or more processors; A non-transitory computer-readable medium storing one or more programs including instructions for performing the method of any one of embodiments 28 to 72. A system including:

[0250] Embodiment 74. The method or system of any one of embodiments 1 to 73, wherein the separate flows comprise a single base type.

[0251] Embodiment 75. The method or system of any one of embodiments 1 to 74, wherein at least one of the separate flows comprises two or three different base types.

[0252] Embodiment 76. The method or system of any one of embodiments 1 to 75, comprising generating or updating a variant call file indicating the presence, identity or absence of short genetic variants in the test sample.

[0253] Embodiment 77. The method or system of any one of embodiments 1 to 76, comprising generating a report indicating the presence, identity or absence of short genetic variants in the test sample.

[0254] Embodiment 78. The method or system of embodiment 77, wherein the report includes a textual, probabilistic, numerical or graphical output indicating the presence, identity or absence of the short genetic variant in the test sample.

[0255] Embodiment 79. The method or system of embodiment 77 or 78, including providing a report to the patient or to the patient's medical professional.

[0256] Embodiment 78. The method or system of any one of embodiments 1 to 77, wherein the short genetic variant comprises a single nucleotide polymorphism or an indel.

[0257] Embodiment 79. The method or system of any one of embodiments 1 to 77, wherein the short genetic variant comprises an indel.

[0258] Embodiment 80 The method or system of any one of embodiments 1 to 79, wherein the test sample comprises fragmented DNA.

[0259] Embodiment 81 The method or system of any one of embodiments 1 to 80, wherein the test sample comprises cell-free DNA.

[0260] Embodiment 82. The method or system of embodiment 81, wherein the cell-free DNA comprises circulating tumor DNA (ctDNA).

[0261] Embodiment 83. A method of sequencing a nucleic acid molecule, comprising: hybridizing the nucleic acid molecule with the primer to form a hybridized template; extending the primer using labeled non-terminating nucleotides provided in separate nucleotide flows according to a repetitive flow cycle sequence comprising five or more separate nucleotide flows; and Detecting a signal from the incorporated labeled nucleotide, or the absence of a signal when the primer is extended by the nucleotide flow. A method comprising:

[0262] Embodiment 84. The method of embodiment 83, comprising detecting a signal, or the absence of a signal, after each nucleotide flow.

[0263] Embodiment 85 The method of embodiment 83 or 84, comprising sequencing the plurality of nucleic acid molecules.

[0264] Embodiment 86 The method of embodiment 85, wherein the nucleic acid molecules of the plurality have different sequencing start positions for a locus.

[0265] Embodiment 87 The method of any one of embodiments 83 to 86, wherein the test sample is cell-free DNA.

[0266] Embodiment 88 The method of any one of embodiments 83 to 86, wherein the cell-free DNA comprises circulating tumor DNA (ctDNA).

[0267] Embodiment 89. The method of any one of embodiments 83 to 86, wherein the flow cycle sequence induces signal changes for 50% or more of the possible SNP permutations at 5% or more of the random sequencing start positions at more than two flow positions.

[0268] Embodiment 90 The method of any one of embodiments 83 to 86, wherein the flow cycle sequence has an efficiency of base incorporation per flow of 0.6 or greater. EXAMPLES

[0269] The present application can be better understood by reference to the following non-limiting examples, which are provided as exemplary embodiments of the present application. The following examples are presented to more fully explain the embodiments, but should not be construed as limiting the broad scope of the present application in any way. Although certain embodiments of the present application have been shown and described herein, it should be clear that such embodiments are provided merely as examples. Numerous variations, modifications and substitutions that do not depart from the spirit and scope of the present invention will occur to those skilled in the art. It should be understood that various alternatives to the embodiments described herein can be utilized in carrying out the methods described herein. Example 1 SNP detection

[0270] The virtual nucleic acid molecule was sequenced using non-terminating nucleotides provided in separate nucleotide flows according to the flow cycle order ATGC, resulting in the test sequencing data set shown in FIG. 1A. Each value in the sequencing data set indicates the likelihood that the indicated base count at each flow position is correct. Based on the sequencing data set, a preliminary sequence is determined as TATGGTCGTCGA (SEQ ID NO: 1) and is mapped to a locus in the reference genome. The locus in the reference genome is associated with possible haplotype sequences TATGGTCGTCGA (SEQ ID NO: 1) (H1) and TATGGTCATCGA (SEQ ID NO: 2) (H2). For each haplotype, a likelihood value associated with the base count of the haplotype sequence for each flow position is selected. The likelihood of the sequencing data set assigned to each haplotype is determined by multiplying the likelihood value associated with the base count of the haplotype sequence for each flow position. The log likelihood of the sequencing data set when H1 is the correct sequence is −0.015, and the log likelihood of the sequencing data set when H2 is the correct sequence is −27.008. Therefore, the sequence of H1 is selected for this nucleic acid molecule. Example 2 Indel detection

[0271] The virtual nucleic acid molecule was sequenced using non-terminating nucleotides provided in separate nucleotide flows according to the flow cycle order ATGC, resulting in the test sequencing data set shown in FIG. 8. Each value in the sequencing data set indicates the likelihood that the indicated base count at each flow position is correct. Based on the sequencing data set (i.e., by selecting the most likely base count at each flow position), a preliminary sequence is determined as TATGGTCGATCG (SEQ ID NO: 8) and mapped to a locus of the reference genome. The locus of the reference genome is associated with possible haplotype sequences TATGGTCG-TCGA (SEQ ID NO: 7) (H1) and TATGGTCGATCG (SEQ ID NO: 8) (H2). A likelihood value associated with the base count of the haplotype sequence for each flow position is selected for each haplotype. The likelihood of the sequencing data set assigned to each haplotype is determined by multiplying the likelihood value associated with the base count of the haplotype sequence for each flow position. The log likelihood of the sequencing data set when H1 is the correct sequence is −24.009, and the log likelihood of the sequencing data set when H2 is the correct sequence is −0.015. Therefore, the sequence of H2 is selected for this nucleic acid molecule. Example 3 Extended sequencing flow order

[0272] More than one million extended sequencing flow sequences were tested in silico for their likelihood of inducing signal changes [XYZ→XQZ, where Q≠Y (and Q, X, Y, and Z are each one of A, C, G, and T)] at more than two flow positions across all possible sets of SNPs. Extended flow sequences were designed to have a minimum of 12 bases of sequence at all valid two-base flow permutations, and flow sequences with sequential base repeats were removed. All possible start positions of the flow sequences were tested to assess the sensitivity of extended flow sequences to induce signal changes at more than two flow positions. Figure 9 and Table 2 show exemplary results of this analysis. The x-axis in Figure 9 shows the fraction of flow phases (or fragmentation start positions), and the y-axis shows the fraction of SNP permutations that induced signal changes at more than two flow positions. Some flow sequences induce two or more signal differences at all possible (87.5%) SNP permutations for roughly 10% of the reads (or flow starting positions). Four-base cyclic flow induces cycle shifts at only 42% of possible SNPs, but does so at all reads or flow phases. A final evaluation of efficiency was performed on a 1 million base subset of the human reference genome to establish feasibility. This is a practical measure of how efficiently the flow sequence extends sequences that have patterns and biases in real organisms. Example 4 SNP detection accuracy

[0273] The genome of DNA sample NA12878 (sample available from the Coriell Institute for Medical Research) was sequenced using non-terminating fluorescently labeled nucleotides following four flow cycles (TACG). The sequencing run generated 415,900,002 reads with an average length of 176 bases. 399,804,925 reads were aligned (with BWA, version 0.7.17-r1188) to the hg38 reference genome.

[0274] After alignment, we selected reads that were either perfectly aligned with the reference genome (178,634,625 reads) or had a single mismatch with the reference genome and aligned with a mapping quality score of 20 or higher (27,265,661 reads). That is, 193,904,639 were not included in further analysis because they had, for example, indels, multiple mismatches, or potentially incorrect (artifactual) alignments with the reference genome. Therefore, we estimated that the 27,265,661 reads included not only true positive NA12878 SNPs, but also any false positive SNPs resulting from sequencing errors. From this pool of 27,265,661 reads, we removed sequencing reads that spanned the mismatch locus more than once, which reduces the effect of true positive NA12878 SNP variants, resulting in a total of 3,413,700 reads with mismatches at depth 1.

[0275] Each of the remaining 3,413,700 reads contained a mismatch that was (1) expected to induce a cycle shift if the flowgram flow signal was shifted one full cycle (e.g., 4 flow positions) relative to the reference based on the flow cycle order, (2) a mismatch that could potentially induce a cycle shift if a different flow cycle was used (e.g., it would generate a new zero or a new nonzero signal in the flowgram), or (3) a mismatch that would not be able to induce a cycle shift regardless of the flow cycle order. Of the 3,413,700 mismatches, 1,184,954 (34%) induced a cycle shift, whereas 1,546,588 (43%) could induce a cycle shift with a different flow order (i.e., "possible cycle shift"). In comparison, theoretical expectations for random mismatches suggested a nominal cycle shift of 42% and a possible cycle shift of 46%. Overall, the mismatch rate inducing a cycle shift was 3.7 × 10 -5 The mismatch rate for inducing a potential cycle shift, in events / base, is 4.8 × 10 -5Table 3 shows the 10 most frequent single mismatches that induce cycle shifts and the relative percentage of occurrence. [Table 3]

[0276] The performance of mismatch-based variant calling in each of the three different classes (i.e., inducing cycle shift, potentially inducing cycle shift, or not inducing cycle shift and not capable of inducing cycle shift) was then evaluated. Reads were aligned to the reference genome using BWA, and variant calling was performed using the HaplotypeCaller tool in GATK (version 4). The resulting mismatch calls were filtered by discarding variant calls in homopolymers longer than 10 bases or within 10 bases adjacent to homopolymers having a length of 10 bases or more.

[0277] Mismatch calls were compared to calls generated for the same NA12878 by the genome-in-the-bottle (GIAB) project to determine the accuracy #TP / (#FP+#FN+#TP) for each class of mismatch. Sequencing data were randomly downsampled to the average genomic depths indicated. Cycle-shift-inducing and potential cycle-shift-inducing mismatches had higher accuracy than non-cycle-shift-inducing mismatches, as demonstrated in Table 4. [Table 4]

Claims

1. 1. A method for detecting short genetic variants in a test sample, comprising: selecting short target gene variants associated with a disease such that, when a target sequencing dataset associated with a target sequence comprising the short target gene variant and a reference sequencing dataset associated with a reference sequence were obtained by sequencing the target sequence and the reference sequence, respectively, using non-terminating nucleotides provided at separate flow positions according to a flow cycle order, the target sequencing dataset associated with the target sequence comprising the short target gene variant and the reference sequencing dataset associated with the reference sequence differ in four or more consecutive flow positions; obtaining one or more test sequencing datasets, each test sequencing dataset associated with a test nucleic acid sequence, each test nucleic acid sequence at least partially overlapping a locus associated with said short target gene variant and derived from said test sample, said one or more test sequencing datasets being determined by sequencing said test sample using non-terminating nucleotides provided at separate flow positions according to said flow cycle order; determining, for each of the test nucleic acid sequences, a respective match score indicating the likelihood that the test sequencing dataset matches the target sequencing dataset or a respective match score indicating the likelihood that the test sequencing dataset matches the reference sequencing dataset; and calling the presence or absence of said short target gene variants in said test sample using one or more of said respective match scores. Including, The method, wherein the short target gene variant is selected prior to calling the presence or absence of the short target gene variant in the test sample.

2. 2. The method of claim 1, wherein the obtaining step comprises sequencing test nucleic acid molecules from the test sample using non-terminating nucleotides provided at separate flow positions according to the flow cycle order, each test nucleic acid molecule associated with a respective test sequencing data set.

3. 3. The method of claim 1 or 2, further comprising generating a personalized biomarker panel for a subject associated with the test sample, wherein the personalized biomarker panel comprises the short target gene variants.

4. The method of claim 1 , further comprising the step of selecting the flow cycle order.

5. 5. The method of claim 1, wherein the target sequencing dataset and the reference sequencing dataset are obtained by in silico sequencing of the target sequence and the reference sequence.

6. 3. The method of claim 2, wherein each of the one or more test sequencing data sets comprises flow signals at the distinct flow locations, the flow signals comprising base counts indicative of the number of respective bases of the test nucleic acid molecules sequenced at each flow location.

7. 7. The method of claim 6, wherein the flow signal comprises a statistical parameter indicative of the likelihood of at least one base count at each flow location, the base count indicating the number of each base of the test nucleic acid molecule sequenced at that flow location.

8. 8. The method of claim 6 or claim 7, wherein the flow signal comprises a statistical parameter indicative of the likelihood of a plurality of base counts at each flow location, each base count indicative of the number of respective bases of the test nucleic acid molecule sequenced at that flow location.

9. The determining step further comprises: selecting a statistical parameter at each flow location in the test sequencing dataset, the statistical parameter corresponding to a base count of the target sequence at that flow location, and determining the respective match scores indicating the likelihood that the test sequencing dataset matches the target sequencing dataset; or 9. The method of claim 8, comprising selecting a statistical parameter at each flow location in the test sequencing dataset that corresponds to a base count of the reference sequence at that flow location, and determining the respective match scores that indicate the likelihood that the test sequencing dataset matches the reference sequencing dataset.

10. 10. The method of claim 9, wherein the respective match scores determined in the determining step are combined values ​​of selected statistical parameters across the flow positions in the test sequencing dataset.

11. 11. The method of claim 1, wherein the determining step comprises determining the respective match scores indicating the likelihood that the test sequencing dataset matches the target sequencing dataset.

12. 12. The method of claim 1, wherein the determining step comprises determining the respective match scores indicating the likelihood that the test sequencing dataset matches the reference sequencing dataset.

13. 13. The method of claim 1, wherein the one or more test sequencing datasets comprises a plurality of test sequencing datasets.

14. 14. The method of claim 13, wherein the presence or absence of the short target gene variant is called separately for each of the one or more test sequencing datasets.

15. 15. The method of claim 13 or 14, wherein at least a portion of the plurality of test sequencing data sets relate to different test nucleic acid molecules having different test sequencing start positions.

16. 16. The method of claim 1, wherein the flow cycle sequence comprises four separate flows repeated in the same sequence.

17. 17. The method of any one of claims 1 to 16, wherein the flow cycle sequence comprises five or more separate flows.

18. A method for detecting disease-associated short genetic variants in a test sample, comprising: obtaining one or more first test sequencing datasets, each first test sequencing dataset associated with a different test nucleic acid molecule from the test sample, the first test sequencing datasets determined by sequencing one or more test nucleic acid molecules using non-terminating nucleotides provided at separate flow positions according to a first flow cycle order, the one or more first test sequencing datasets comprising flow signals at the separate flow positions; obtaining one or more second test sequencing datasets, each second test sequencing dataset associated with the same respective test nucleic acid molecule as a corresponding first test sequencing dataset, the second test sequencing dataset being determined by sequencing the one or more test nucleic acid molecules using non-terminating nucleotides provided at separate flow positions according to a second flow cycle order, the first flow cycle order and the second flow cycle order being different, the one or more second test sequencing datasets comprising flow signals at the separate flow positions; determining a match score for each of the first and corresponding second sequencing datasets with one or more candidate sequences, each match score indicating a likelihood that the first test sequencing dataset, the second test sequencing dataset, or both, matches a candidate sequence from the one or more candidate sequences; and using the determined match score to call the presence or absence of said short genetic variant in said test sample. The method includes:

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