SInC: An accurate and fast error-model based simulator for SNPs, Indels and CNVs coupled with a read generator for short-read sequence data

TL;DR

SInC is a fast, accurate, open-source simulator for biological variants and short-read sequence data, incorporating platform-specific error models and optimized for multi-core desktop systems.

Contribution

It introduces SInC, a novel simulator that efficiently models SNPs, Indels, and CNVs with a read generator, outperforming existing tools in speed and accuracy.

Findings

High efficiency in simulating large genomes

Multi-threaded read generation reduces runtime

Accurate modeling of platform-specific sequencing errors

Abstract

We report SInC (SNV, Indel and CNV) simulator and read generator, an open-source tool capable of simulating biological variants taking into account a platform-specific error model. SInC is capable of simulating and generating single- and paired-end reads with user-defined insert size with high efficiency compared to the other existing tools. SInC, due to its multi-threaded capability during read generation, has a low time footprint. SInC is currently optimised to work in limited infrastructure setup and can efficiently exploit the commonly used quad-core desktop architecture to simulate short sequence reads with deep coverage for large genomes. Sinc can be downloaded from https://sourceforge.net/projects/sincsimulator/.