A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data-Reference-Cited by-同舟云学术

A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data

Published:2018-11-05 Issue:12 Volume:50 Page:1735-1743
ISSN:1061-4036
Container-title:Nature Genetics
language:en
Short-container-title:Nat Genet

Author:

Ainscough Benjamin J.^ORCID,Barnell Erica K.^ORCID,Ronning Peter,Campbell Katie M.^ORCID,Wagner Alex H.^ORCID,Fehniger Todd A.^ORCID,Dunn Gavin P.,Uppaluri Ravindra,Govindan Ramaswamy,Rohan Thomas E.,Griffith Malachi^ORCID,Mardis Elaine R.,Swamidass S. Joshua,Griffith Obi L.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Genetics

Link

http://www.nature.com/articles/s41588-018-0257-y.pdf

Reference49 articles.

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3. Robinson, J. T., Thorvaldsdóttir, H., Wenger, A. M., Zehir, A. & Mesirov, J. P. Variant review with the integrative genomics viewer. Cancer Res. 77, e31–e34 (2017).

4. Li, M. M. et al. Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J. Mol. Diagn. 19, 4–23 (2017).

5. Roy, S. et al. Standards and guidelines for validating next-generation sequencing bioinformatics pipelines: a joint recommendation of the Association for Molecular Pathology and the College of American Pathologists. J. Mol. Diagn. 20, 4–27 (2017).

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