Products & Services
Applications

Applications

Nanopore sequencing offers advantages in all areas of research. Our offering includes DNA sequencing, as well as RNA and gene expression analysis and future technology for analysing proteins.

Learn about applications
View all Applications
Resources
News Explore
Contact
Back

Clairvoyante: a multi-task convolutional deep neural network for variant calling in Single Molecule Sequencing

Tool

Date: 1st March 2019 | Source:

Authors: Ruibang Luo, Fritz J Sedlazeck, Tak-Wah Lam, Michael Schatz

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 achieved 99.73%, 97.68% and 95.36% precision on known 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 two hours on a standard server. Furthermore, we identified 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.

Nature Communications - Full text

Recommended for you

Open a chat to talk to our sales team
FAQs

FAQs

Search