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About A Universal SNP and Small-Indel Variant Caller Using Deep Neural Networks
This document summarizes a new method for calling genetic variants from sequencing data using deep learning. It describes DeepVariant, a tool that uses a deep convolutional neural network (CNN) to identify single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) directly from read data, without relying on complex statistical models. The CNN is trained on labeled examples to learn patterns in "pileup images" of reads aligned to candidate variant sites. When evaluated on benchmark data, DeepVariant produced more accurate variant calls than existing state-of-the-art methods like GATK. It also generalized well to new sequencing technologies and experimental designs.
Detailed Information
| Author: | ['VasilisLemonidis'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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