Aojie Lian1,2, James Guevara2, Kun Xia1, Jonathan Sebat2. 1. Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China. 2. Department of Psychiatry, University of California San Diego, La Jolla, California United States of America.
Abstract
MOTIVATION: As sequencing technologies and analysis pipelines evolve, de novo mutations(DNMs) 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). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: As sequencing technologies and analysis pipelines evolve, de novo mutations(DNMs) 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). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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