METHOD AND APPARATUS FOR CALLING SINGLE-NUCLEOTIDE VARIATIONS AND OTHER VARIATIONS

• US 20160026757A1
• Filed: 02/13/2014
• Published: 01/28/2016
• Est. Priority Date: 11/01/2013
• Status: Active Grant

• Current Assignee: Accurascience, LLC
• Original Assignee: Accura Science LLC
• Inventors: LI, Tongbin, GONG, Wuming, RAO, Jiang
• Granted Patent: US 10,089,436 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/10 Ploidy or copy number detection
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Abstract

Base calls for a target sequence may be identified relative to a reference sequence by using values from sequencing reads at locations satisfying a high-confidence condition to identify base calls at a given location not satisfying the high-confidence condition. The high-confidence condition may relate to the level of coverage by the sequencing reads at a location of the reference sequence. The quality of measurements of the sequencing reads may be incorporated into the base-call process.

Claims

1. A method of identifying at least one base call for a target sequence, the method comprising:

accessing the reference sequence, the reference sequence including a plurality of base values that define the reference sequence;

accessing a plurality of sequencing reads, each sequencing read including a plurality of base values for a corresponding portion of the target sequence;

identifying a plurality of high-confidence locations in the sequencing reads, a high-confidence location being identified with a corresponding location in the reference sequence and satisfying a high-confidence condition for using base values of the sequencing reads at the high-confidence location to identify one or more base calls for the target sequence at the high-confidence location; and

identifying one or more base calls for the target sequence at a given location not satisfying the high-confidence condition by using base values of the sequencing reads at the high-confidence locations with base values of the sequencing reads at the given location and a base value of the reference sequence at the given location to identify the one or more base calls for the target sequence at the given location.

2. The method of claim 1, wherein the high-confidence condition is satisfied at the high-confidence location

if at least a threshold number of the sequencing reads include the high-confidence location, and

If a distribution of sequencing-read base values at the high-confidence location satisfies a statistical confidence criterion.

3. The method of claim 1, wherein identifying the one or more base calls for the target sequence at the given location includes:

using the base values of the sequencing reads at the high-confidence locations to evaluate likelihoods of combinations of the base value of the reference sequence at the given location and one or more base values for the target sequence at the given location.

4. The method of claim 1, wherein identifying the one or more base calls for the target sequence at the given location includes:

using the base values of the sequencing reads at the high-confidence locations to evaluate likelihoods of combinations of one or more base values for the target sequence at the given location and a base call for the target sequence at the given location.

5. The method of claim 1, wherein the target sequence corresponds to a N-ploid genotype and N base calls are identified at the given location for some positive integer N.

6. The method of claim 1, wherein the sequencing reads correspond to genomic data.

7. The method of claim 1, further comprising:

selecting the given location by determining that at least one sequencing read that includes the given location indicates a single nucleotide variation (SNV) relative to the reference sequence.

8. The method of claim 1, further comprising:

selecting the given location by identifying an insertion or a deletion at the given location, a copy-number variation at the given location, or a structural variation at the given location.

9. The method of claim 1, wherein a Bayesian prediction model is used to identify the one or more base calls for the target sequence at the given location, the Bayesian prediction model providing likelihood values that relate the base values of the sequencing reads at the high-confidence locations and the base values of the reference sequence at multiple locations with the one or more base calls for the target sequence at the given location.

10. The method of claim 1, wherein identifying the one or more base calls for the target sequence at the given location includes:

sampling M high-confidence base values from the sequencing reads for some positive integer M, the M high-confidence base values corresponding to at least one high-confidence location where a base value of the reference sequence is equivalent to a base value of the reference sequence at the given location; and

using the M high-confidence base values to estimate a likelihood of the one or more target-sequence base values at the given location.

11. The method of claim 1, wherein identifying the one or more base calls for the target sequence at the given location includes:

sampling M high-confidence measurements of the one or more target-sequence base values at the given location for some positive integer M, the M high-confidence measurements including base values from at least one high-confidence location where corresponding sequencing reads identify a combination of base values that is equivalent to the one or more target-sequence base values at the given location; and

using the M high-confidence measurements with the one or more target-sequence base values at the given location to estimate a likelihood of the base values from the sequencing reads at the given location.

12. A non-transitory computer-readable medium that stores a computer program for identifying at least one base call for a target sequence, the computer program including instructions that, when executed by at least one computer, cause the at least one computer to perform operations comprising:

accessing the reference sequence, the reference sequence including a plurality of base values that define the reference sequence;

accessing a plurality of sequencing reads, each sequencing read including a plurality of base values for a corresponding portion of the target sequence;

identifying a plurality of high-confidence locations in the sequencing reads, a high-confidence location being identified with a corresponding location in the reference sequence and satisfying a high-confidence condition for using base values of the sequencing reads at the high-confidence location to identify one or more base calls for the target sequence at the high-confidence location; and

identifying one or more base calls for the target sequence at a given location not satisfying the high-confidence condition by using base values of the sequencing reads at the high-confidence locations with base values of the sequencing reads at the given location and a base value of the reference sequence at the given location to identify the one or more base calls for the target sequence at the given location.

13. The non-transitory computer-readable medium of claim 12, wherein the high-confidence condition is satisfied at the high-confidence location

if at least a threshold number of the sequencing reads include the high-confidence location, and

If a distribution of sequencing-read base values at the high-confidence location satisfies a statistical confidence criterion.

14. The non-transitory computer-readable medium of claim 12, wherein identifying the one or more base calls for the target sequence at the given location includes:

using the base values of the sequencing reads at the high-confidence locations to evaluate likelihoods of combinations of the base value of the reference sequence at the given location and one or more base values for the target sequence at the given location.

15. The non-transitory computer-readable medium of claim 12, wherein identifying the one or more base calls for the target sequence at the given location includes:

using the base values of the sequencing reads at the high-confidence locations to evaluate likelihoods of combinations of one or more base values for the target sequence at the given location and a base call for the target sequence at the given location.

16. The non-transitory computer-readable medium of claim 12, wherein the target sequence corresponds to a N-ploid genotype and N base calls are identified at the given location for some positive integer N.

17. The non-transitory computer-readable medium of claim 12, wherein the computer program further includes instructions that, when executed by the at least one computer, cause the at least one computer to perform operations comprising:

selecting the given location by determining that at least one sequencing read that includes the given location indicates a single nucleotide variation (SNV) relative to the reference sequence.

18. The non-transitory computer-readable medium of claim 12, wherein the computer program further includes instructions that, when executed by the at least one computer, cause the at least one computer to perform operations comprising:

selecting the given location by identifying an insertion or a deletion at the given location, a copy-number variation at the given location, or a structural variation at the given location.

19. The non-transitory computer-readable medium of claim 12, wherein a Bayesian prediction model is used to identify the one or more base calls for the target sequence at the given location, the Bayesian prediction model providing likelihood values that relate the base values of the sequencing reads at the high-confidence locations and the base values of the reference sequence at multiple locations with the one or more base calls for the target sequence at the given location.

20. An apparatus to identify at least one base call for a target sequence, the apparatus comprising at least one computer configured to perform operations for computer-implemented modules including:

a first access module to access the reference sequence, the reference sequence including a plurality of base values that define the reference sequence;

a second access module to access a plurality of sequencing reads, each sequencing read including a plurality of base values for a corresponding portion of the target sequence;

a first identification module to identify a plurality of high-confidence locations in the sequencing reads, a high-confidence location being identified with a corresponding location in the reference sequence and satisfying a high-confidence condition for using base values of the sequencing reads at the high-confidence location to identify one or more base calls for the target sequence at the high-confidence location; and

a second identification module to identify one or more base calls for the target sequence at a given location not satisfying the high-confidence condition by using base values of the sequencing reads at the high-confidence locations with base values of the sequencing reads at the given location and a base value of the reference sequence at the given location to identify the one or more base calls for the target sequence at the given location.