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

Nat Commun. 2019 Mar 1;10(1):998. doi: 10.1038/s41467-019-09025-z.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Base Sequence*
  • Computational Biology / instrumentation
  • Computational Biology / methods*
  • DNA Mutational Analysis
  • Genome, Human*
  • Genome-Wide Association Study
  • Genomics
  • Genotype
  • Genotyping Techniques / instrumentation
  • Genotyping Techniques / methods
  • Humans
  • INDEL Mutation
  • Nanopores
  • Neural Networks, Computer*
  • Polymorphism, Single Nucleotide
  • Sequence Analysis, DNA
  • Software