IP Library Granted Patent US 12,499,974
Granted Patent B2
US 12,499,974 · App. 17/952,194 · Granted Dec 16, 2025

Quality detection of variant calling using a machine learning classifier

Inventors: Hong Gao (Palo Alto, CA); Tobias Hamp (Cambridge, GB); Joshua Goodwin Jon Mcmaster-Schraiber (Berkeley, CA); Laksshman Sundaram (Fremont, CA); Kai-How Farh (Hillsborough, CA)
Assignee: Illumina, Inc.
G16B30/00G06F18/2111G06F18/2148G06F18/2155G06N3/045G06N3/08G06N3/126G06N20/00G06N20/20G16B10/00G16B20/00G16B20/20G16B20/40G16B30/10G16B40/00G16B40/20G16B40/30G16B50/10
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Quick Facts
Patent No.
US 12,499,974
App. No.
17/952,194
Granted
Dec 16, 2025
Kind
B2
Abstract

The technology disclosed relates to variant calling of sequenced reads of a sample of a target species against a reference genome of a pseudo-target species. Low-quality variants are identified as false positive variants that are present in the second set of variants but absent from the first set of variants.

Claims (32)

1 . A system, comprising:

a variant quality classifier configured to process a plurality of features of a target variant, and generate a quality indication for the target variant,

wherein the variant quality classifier is trained on a set of high-quality variants and a set of low-quality variants,

wherein high-quality variants in the set of high-quality variants are identified as true positive variants that are common between a first set of variants and a second set of variants,

wherein low-quality variants in the set of low-quality variants are identified as false positive variants that are present in the second set of variants but absent from the first set of variants,

wherein the first set of variants is detected by variant calling sequenced reads of a sample of a target species against a reference genome of a non-target species, and

wherein the second set of variants is detected by variant calling the sequenced reads of the sample of the target species against a reference genome of a pseudo-target species.

2 . The system of claim 1 , wherein the variant quality classifier is a random forest model.

3 . The system of claim 1 , wherein the variant quality classifier is a logistic regression model.

4 . The system of claim 1 , wherein the variant quality classifier is a neural network model.

5 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a guanine-cytosine (GC) content within the sequenced reads of the target variant.

6 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a guanine-cytosine (GC) skew within the sequenced reads of the target variant,

wherein the GC skew represents a normalized excess of cytosine over guanine in a given sequenced read of the target variant.

7 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a local composition complexity within one hundred base pairs upstream or downstream of the target variant.

8 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is an allelic count of the sequenced reads of the target variant.

9 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a mapping quality of the sequenced reads of the target variant.

10 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a p-value of Fisher's exact test to detect strand bias in the sequenced reads of the target variant.

11 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a symmetric odds ratio to detect strand bias in the sequenced reads of the target variant.

12 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a variant quality by depth of the sequenced reads of the target variant.

13 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a genotype quality of the sequenced reads of the target variant.

14 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a read depth of the target variant normalized by a mean coverage of the sequenced reads of the target variant.

15 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a fraction alternative allele read depth out of a target variant coverage of the sequenced reads of the target variant.

16 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is an existence of insertion and/or deletion (indel) mutations within five base pairs upstream or downstream of the sequenced reads of the target variant.

17 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is an existence of insertion and/or deletion (indel) mutations within ten base pairs upstream or downstream of the sequenced reads of the target variant.

18 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a mean coverage of flanking regions one hundred base pairs upstream or downstream of the sequenced reads of the target variant normalized by the mean coverage of the sequenced reads of the target variant.

19 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a mean coverage of flanking regions five hundred base pairs upstream or downstream of the sequenced reads of the target variant normalized by the mean coverage of the sequenced reads of the target variant.

20 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a number of heterozygote single nucleotide polymorphisms within one hundred base pairs upstream or downstream of the sequenced reads of the target variant normalized by a median count of variants within the same length regions of the sequenced reads of the target variant.

21 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a number of heterozygote single nucleotide polymorphisms within five hundred base pairs upstream or downstream of the sequenced reads of the target variant normalized by a median count of variants within the same length regions of the sequenced reads of the target variant.

22 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a number of homozygote single nucleotide polymorphisms within one hundred base pairs upstream or downstream of the sequenced reads of the target variant normalized by a median count of variants within the same length regions of the sequenced reads of the target variant.

23 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a number of homozygote single nucleotide polymorphisms within five hundred base pairs upstream or downstream of the sequenced reads of the target variant normalized by a median count of variants within the same length regions of the sequenced reads of the target variant.

24 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a number of alternate homozygote single nucleotide polymorphisms within one hundred base pairs upstream or downstream of the sequenced reads of the target variant normalized by a median count of variants within the same length regions of the sequenced reads of the target variant.

25 . The system of claim 1 , wherein a feature in the plurality of features of the target variant is a number of alternate homozygote single nucleotide polymorphisms within five hundred base pairs upstream or downstream of the sequenced reads of the target variant normalized by a median count of variants within the same length regions of the sequenced reads of the target variant.

Assignments (11)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 13, 2023
From: ILLUMINA CAMBRIDGE LIMITED
To: ILLUMINA, INC.
Reel/Frame 065988/0723 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: FARH, KAI-HOW
To: ILLUMINA, INC.
Reel/Frame 062583/0451 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: HAMP, TOBIAS
To: ILLUMINA CAMBRIDGE LIMITED
Reel/Frame 062583/0606 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: GAO, HONG; MCMASTER-SCHRAIBER, JOSHUA GOODWIN JON; SUNDARAM, LAKSSHMAN; FARH, KAI-HOW
To: ILLUMINA, INC.
Reel/Frame 062583/0725 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: GAO, HONG; MCMASTER-SCHRAIBER, JOSHUA GOODWIN JON; FARH, KAI-HOW
To: ILLUMINA, INC.
Reel/Frame 062583/0871 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: HAMP, TOBIAS
To: ILLUMINA CAMBRIDGE LIMITED
Reel/Frame 062583/0311 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: FARH, KAI-HOW
To: ILLUMINA, INC.
Reel/Frame 062583/0969 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: HAMP, TOBIAS
To: ILLUMINA CAMBRIDGE LIMITED
Reel/Frame 062584/0315 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: FARH, KAI-HOW
To: ILLUMINA, INC.
Reel/Frame 062584/0521 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: HAMP, TOBIAS
To: ILLUMINA CAMBRIDGE LIMITED
Reel/Frame 062584/0557 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2023
From: FARH, KAI-HOW
To: ILLUMINA, INC.
Reel/Frame 062583/0917 →
Continuity (7)
Provisional Application 63294828 · Dec 29, 2021
Provisional Application 63294813 · Dec 29, 2021
Provisional Application 63294830 · Dec 29, 2021
Provisional Application 63294827 · Dec 29, 2021
Provisional Application 63294816 · Dec 29, 2021
Provisional Application 63294820 · Dec 29, 2021
Related Publication 20230207058A1 · Jun 29, 2023
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