Article

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

2018-04-28

Abstract excerpt

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...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 9908ac14-e52b-5855-b240-98fa418fb609
DOI: 10.1101/310458

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Clairvoyante: a multi-task convolutional deep neural network for variant calling in Single Molecule SequencingDOI 10.1101/310458Read

Creating a universal SNP and small indel variant caller with deep neural networksSemantic proximity Read

VCboost: reducing false positives in long-read variant calling for single-nucleotide polymorphism and indel detection in challenging genomic regions.Semantic proximity Read

Clair: Exploring the limit of using a deep neural network on pileup data for germline variant callingSemantic proximity Read

DeNovoCNN: A deep learning approach to <i>de novo</i> variant calling in next generation sequencing dataSemantic proximity Read

Symphonizing pileup and full-alignment for deep learning-based long-read variant calling.Semantic proximity Read

ClairS: a deep-learning method for long-read somatic small variant callingSemantic proximity Read

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.Semantic proximity Read

NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.Semantic proximity Read

NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networksSemantic proximity Read

Joint processing of long- and short-read sequencing data with deep learning improves variant callingSemantic proximity Read

Benchmarking of sequencing technologies defines optimal strategies for genetic variants detection in a human genome.Semantic proximity Read

Pangenome-aware DeepVariantSemantic proximity Read