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

Ruibang Luo; Fritz J. Sedlazeck; Tak-Wah Lam; Michael C. Schatz

doi:10.1038/s41467-019-09025-z

Nature Communications (Mar 2019)

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

Ruibang Luo,
Fritz J. Sedlazeck,
Tak-Wah Lam,
Michael C. Schatz

Affiliations

Ruibang Luo: Department of Computer Science, The University of Hong Kong
Fritz J. Sedlazeck: Human Genome Sequencing Center, Baylor College of Medicine
Tak-Wah Lam: Department of Computer Science, The University of Hong Kong
Michael C. Schatz: Department of Computer Science, Johns Hopkins University

DOI: https://doi.org/10.1038/s41467-019-09025-z
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 11

Abstract

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Single Molecule Sequencing (SMS) technologies generate long but noisy reads data. Here, the authors develop Clairvoyante, a deep neural network-based method for variant calling with SMS reads such as PacBio and ONT data.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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