A multi-task convolutional deep neural network for variant calling in single molecule sequencing.

TitleA multi-task convolutional deep neural network for variant calling in single molecule sequencing.
Publication TypeJournal Article
Year of Publication2019
AuthorsLuo, R, Sedlazeck, FJ, Lam, T-W, Schatz, MC
JournalNat Commun
Date Published2019 03 01
KeywordsBase Sequence, Computational Biology, DNA Mutational Analysis, Genome, Human, Genome-Wide Association Study, Genomics, Genotype, Genotyping Techniques, Humans, INDEL Mutation, Nanopores, Neural Networks (Computer), Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Software

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15%. Meeting this demand, we developed Clairvoyante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53% F1-score on 1KP common variants, and 98.65, 92.57, 87.26% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2 h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is available open-source ( https://github.com/aquaskyline/Clairvoyante ), with modules to train, utilize and visualize the model.

Alternate JournalNat Commun
PubMed ID30824707
PubMed Central IDPMC6397153
Grant ListR01 HG006677 / HG / NHGRI NIH HHS / United States
UM1 HG008898 / HG / NHGRI NIH HHS / United States