Creating a universal SNP and small indel variant caller with deep neural networks

Abstract

Next-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an individual's genome 1 by calling genetic variants present in an individual using billions of short, errorful sequence reads 2 . Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome 3,4 .Here we show that a deep convolutional neural network 5 can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls. This approach, called DeepVariant, outperforms existing tools, even winning the "highest performance" award for SNPs in a FDA-administered variant calling challenge. The learned model generalizes across genome builds and even to other mammalian species, allowing non-human sequencing projects to benefit from the wealth of human ground truth data. We further show that, unlike existing tools which perform well on only a specific technology, DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, from deep whole genomes from 10X Genomics to Ion Ampliseq exomes. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data. Main TextCalling genetic variants from NGS data has proven challenging because NGS reads are not only errorful (with rates from ~0.1-10%) but result from a complex error process that depends on properties of the instrument, preceding data processing tools, and the genome sequence itself 1,3,4,6 . State-of-the-art variant callers use a variety of statistical techniques to model these error processes and thereby accurately identify differences between the reads and the reference genome caused by real genetic variants and those arising from errors in the reads 3,4,6,7 . For example, the widely-used GATK uses logistic regression to model base errors, hidden Markov models to compute read likelihoods, and naive Bayes classification to identify peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/092890 doi: bioRxiv preprint first posted online Dec. 14, 2016; Poplin et al. Creating a universal SNP and small indel variant caller with deep neural networks.variants, which are then filtered to remove likely false positives using a Gaussian mixture model with hand-crafted features capturing common error modes 6 . These techniques allow the GATK to achieve high but still imperfect accuracy on the Illumina sequencing platform 3,4 . Generalizing these models to other sequencing technologies has proven difficult due to the need for manual retuning or exte...

DAVI:Deep Learning Based Tool for Alignment and Single Nucleotide Variant identification

“…Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome [13]. Deep Neural Network based learning algorithms provide attractive solutions for many bio-informatics [14] problems because of their ability to scale for large dataset, and their effectiveness in identification of intrusive complex features from underlying data.…”

“…CNN based models are used exhaustively in identification of Motif in DNA sequence [18], but not much has been done for the prediction of SNVs in raw DNA reads. Only recently, Google has developed DeepVariant [19] tool which uses deep learning to predict variants in aligned and cleaned DNA reads. DeepVariant method uses CNN as universal approximator for identification of variants in NGS reads.…”

Computational biology: deep learning

“…A four-layer dense network considering only information at the candidate site can achieve reasonable performance [29,30]. Poplin and colleagues further converted the read pileup at a potential variable site into a 221 × 100-pixel RGB image, and then used Inception-v2 [31], a network architecture normally applied in image analysis tasks, to call mutation status [32]. Base identity, base quality, and strand information were encoded in the color channels, and no additional data were used.…”

Strelka2: Fast and accurate variant calling for clinical sequencing applications

“…Within the somatic variant probability model, the original Strelka method has been redesigned with a further novel innovation to account for contamination of tumor cells in the matched normal sample such that somatic recall is improved, especially for liquid tumor analysis. Consistent with the emphasis on automated sample adaption in Strelka2, the liquid tumor model is an expansion of the model's state space applied to all cases, and thus does not require prior knowledge of the normal sample contamination level.For both germline and somatic calling workflows, the variant probability model is supplemented by a final empirical variant scoring (EVS) step, motivated in part by machine learning-based variant classification approaches 9,10 . This step uses a random forest model trained on numerous features indicative of call quality to improve precision by accounting for error phenomena that are not adequately represented in the generative variant probability model.…”

“…For both germline and somatic calling workflows, the variant probability model is supplemented by a final empirical variant scoring (EVS) step, motivated in part by machine learning-based variant classification approaches 9,10 . This step uses a random forest model trained on numerous features indicative of call quality to improve precision by accounting for error phenomena that are not adequately represented in the generative variant probability model.…”