Dr Luo Ruibang (left) and Professor Lam Tak-wah of Department of Computer Science and their team published a paper entitled “A multi-task convolutional deep neural network for variant calling in single molecule sequencing” in the prestigious journal Nature Communications.
A multi-task convolutional deep neural network for variant calling in single molecule sequencing
By Luo Ruibang, Fritz J. Sedlazeck, Lam Tak-Wah & Michael C. Schatz
Abstract of the paper:
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.
Full article could be found in https://www.nature.com/articles/s41467-019-09025-z.