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Customized de novo mutation detection for any variant calling pipeline: SynthDNM

Aojie Lian, James Guevara, Kun Xia, Jonathan Sebat
doi: https://doi.org/10.1101/2021.02.10.427198
Aojie Lian
1Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
2Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
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James Guevara
2Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
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Kun Xia
1Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
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Jonathan Sebat
2Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
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  • For correspondence: jsebat@ucsd.edu
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Abstract

Motivation As sequencing technologies and analysis pipelines evolve, DNM calling tools must be adapted. Therefore, a flexible approach is needed that can accurately identify de novo mutations from genome or exome sequences from a variety of datasets and variant calling pipelines.

Results Here, we describe SynthDNM, a random-forest based classifier that can be readily adapted to new sequencing or variant-calling pipelines by applying a flexible approach to constructing simulated training examples from real data. The optimized SynthDNM classifiers predict de novo SNPs and indels with robust accuracy across multiple methods of variant calling.

Availability SynthDNM is freely available on Github (https://github.com/james-guevara/synthdnm)

Contact jsebat{at}ucsd.edu

Supplementary information Supplementary data are available at Bioinformatics online.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/james-guevara/synthdnm/wiki

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 10, 2021.
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Customized de novo mutation detection for any variant calling pipeline: SynthDNM
Aojie Lian, James Guevara, Kun Xia, Jonathan Sebat
bioRxiv 2021.02.10.427198; doi: https://doi.org/10.1101/2021.02.10.427198
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Customized de novo mutation detection for any variant calling pipeline: SynthDNM
Aojie Lian, James Guevara, Kun Xia, Jonathan Sebat
bioRxiv 2021.02.10.427198; doi: https://doi.org/10.1101/2021.02.10.427198

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