ClairS: a deep-learning method for long-read somatic small variant calling

Abstract

Identifying somatic variants in tumor samples is a crucial task, which is often performed using statistical methods and heuristic filters applied to short-read data. However, with the increasing demand for long-read somatic variant calling, existing methods have fallen short. To address this gap, we present ClairS, the first deep-learning-based, long-read somatic small variant caller. ClairS was trained on massive synthetic somatic variants with diverse coverages and variant allele frequencies (VAF), enabling it to accurately detect a wide range of somatic variants from paired tumor and normal samples. We evaluated ClairS using the latest Nanopore Q20+ HCC1395-HCC1395BL dataset. With 50-fold/25-fold tumor/normal, ClairS achieved a 93.01%/86.86% precision/recall rate for Single Nucleotide Variation (SNVs), and 66.54%/66.89% for somatic insertions and deletions (Indels). Applying ClairS to short-read datasets from multiple sources showed comparable or better performance than Strelka2 and Mutect2. Our findings suggest that improved read phasing enabled by long-read sequencing is key to accurate long-read SNV calling, especially for variants with low VAF. Through experiments across various coverage, purity, and contamination settings, we demonstrated that ClairS is a reliable somatic variant caller. ClairS is open-source at https://github.com/HKU-BAL/ClairS.

Full text

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"
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.CC-BY-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 21, 2023. ; https://doi.org/10.1101/2023.08.17.553778doi: bioRxiv preprint

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Results
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The ClairS method
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.CC-BY-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 21, 2023. ; https://doi.org/10.1101/2023.08.17.553778doi: bioRxiv preprint

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.CC-BY-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 21, 2023. ; https://doi.org/10.1101/2023.08.17.553778doi: bioRxiv preprint

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"
"
$$#
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ClairS performance on Illumina data
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"
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"
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"
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"
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.CC-BY-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 21, 2023. ; https://doi.org/10.1101/2023.08.17.553778doi: bioRxiv preprint

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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 21, 2023. ; https://doi.org/10.1101/2023.08.17.553778doi: bioRxiv preprint

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.CC-BY-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 21, 2023. ; https://doi.org/10.1101/2023.08.17.553778doi: bioRxiv preprint

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The copyright holder for this preprintthis version posted August 21, 2023. ; https://doi.org/10.1101/2023.08.17.553778doi: bioRxiv preprint

FWR$R!#`"B0",'('A+B'"'F+O/+Y0("6'BA*DG-"*(DO/M*(,"_!51D0A'-"qA'D*G*0(-"+(M":'D+OO"+,+*(GB"B&'"
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Acknowledgements
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.CC-BY-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 21, 2023. ; https://doi.org/10.1101/2023.08.17.553778doi: bioRxiv preprint

Author contribu.ons
L!!
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Compe.ng interests
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:R2R"A'D'*F'G"A'G'+AD&"C/(M*(,"CA06"ia7R"7&'"0B&'A"+/B&0AG"M'DO+A'"(0"D06@'Y(,"*(B'A'GBGR"
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L!V
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.CC-BY-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 21, 2023. ; https://doi.org/10.1101/2023.08.17.553778doi: bioRxiv preprint

References
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The copyright holder for this preprintthis version posted August 21, 2023. ; https://doi.org/10.1101/2023.08.17.553778doi: bioRxiv preprint

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Figure 5. Categorizing the FPs and FNs in ClairS
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Extended Data Figures
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Extended Data Figure 1. Performance differences between phasable and not-phasable
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Extended Data Figure 2. Visualization of neural network inputs.
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Extended Data Figure 3. Two examples of haplotype inconsistency that signifies a false
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