Simulator Methods

The full source code of the project can be found on GitHub at https://github.com/NabaviLab/VarSimLab

1. Simulating SNPs, Indels and CNVs

This step uses SInC There are several advantages to this tool over similar error generators– its speed, its ability to simulate a wider variety of errors (SNPs, INDELS, and CNVs), and the biologically realistic nature of the errors it generates.

Pattnaik, Swetansu, et al. “SInC: an accurate and fast error-model based simulator for SNPs, Indels and CNVs coupled with a read generator for short-read sequence data.” BMC bioinformatics 15.1 (2014): 40.

2. Simulating Tumors

Tumor Ploidy is implemented by re-running SInC to generate more than 2 alleles. Tumor subclone is done by iteratively going through all above simulation steps to generate different aberrant genomes for different subclones.

3. Generating Short Reads

This step utilizes ART articifial read generator This is a widely used read generator with the capacity to faithfully simulate the error profiles of current next generation sequencers

Huang, W., Li, L., Myers, J. R., & Marth, G. T. (2012). ART: a next-generation sequencing read simulator. Bioinformatics, 28(4), 593–594. http://doi.org/10.1093/bioinformatics/btr708