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. 2017 Mar 14;18(Suppl 3):53. doi: 10.1186/s12859-017-1464-8

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© The Author(s) 2017

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 1 — The workflow of Pysim-sv. Component 1 simulates a personal genome by introducing genomic variations to a given reference genome. Component 2 generates tumor genomes by simulating aneuploidy and somatic variations. Subclones are iteratively generated. Component 3 generates HTS reads, mixes reads from different tumor/normal genomes and introduces GC-bias