Exploring the limit of using a deep neural network on pileup data for germline variant calling-Reference-Cited by-同舟云学术

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Exploring the limit of using a deep neural network on pileup data for germline variant calling

Published:2020-04 Issue:4 Volume:2 Page:220-227
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Luo Ruibang^ORCID,Wong Chak-Lim,Wong Yat-Sing,Tang Chi-Ian,Liu Chi-Man,Leung Chi-Ming,Lam Tak-Wah

Funder

HKU | University Research Committee, University of Hong Kong

Research Grants Council, University Grants Committee

Innovation and Technology Fund

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

http://www.nature.com/articles/s42256-020-0167-4.pdf

Reference24 articles.

1. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

2. Ashley, E. A. Towards precision medicine. Nat. Rev. Genet. 17, 507–522 (2016).

3. Li, H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics 30, 2843–2851 (2014).

4. Luo, R., Schatz, M. C. & Salzberg, S. L. 16GT: a fast and sensitive variant caller using a 16-genotype probabilistic model. GigaScience 6, gix045 (2017).

5. Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinform. 43, 11–33 (2013).

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