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

Pattnaik S, Gupta S, Rao AA, Panda B - BMC Bioinformatics (2014)

Bottom Line: SInC is capable of generating single- and paired-end reads with user-defined insert size and with high efficiency compared to the other existing tools.SInC, due to its multi-threaded capability during read generation, has a low time footprint.We have come up with a user-friendly multi-variant simulator and read-generator tools called SInC.

View Article: PubMed Central - HTML - PubMed

Affiliation: Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Biotech Park, Electronic City Phase I, Bangalore 560100, India. binay@ganitlabs.in.

ABSTRACT

Background: The rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration.

Results: We report SInC (Snp, Indel and Cnv) an open-source variant simulator and read generator capable of simulating all the three common types of biological variants taking into account a distribution of base quality score from a most commonly used next-generation sequencing instrument from Illumina. SInC is capable of generating single- and paired-end reads with user-defined insert size and 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.

Conclusions: We have come up with a user-friendly multi-variant simulator and read-generator tools called SInC. SInC can be downloaded from http://sourceforge.net/projects/sincsimulator.

Show MeSH
Flowcharts indicate the algorithm implemented in simulation of A) SNP, B) Indel, C) CNV and subsequent D) read generation process.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3926339&req=5

Figure 1: Flowcharts indicate the algorithm implemented in simulation of A) SNP, B) Indel, C) CNV and subsequent D) read generation process.

Mentions: This ensures that the simulated SNVs are well distributed over the genome. A positional filter is applied to remove the outlier SNVs, which are less than 15 bases apart. SInC simulator neglects SNVs simulated in the N-regions of the genome (where there is no A, T, G or C). Then the algorithm applies a user-defined transition to transversion (Ti/Tv) metric to maintain the biological significance of the SNVs across the genome. A Ti/Tv ratio of 2.1 was maintained across the population of simulated SNVs with 20% inherent heterozygosity to simulate human genome data as previously reported[32]. The flow chart illustrated in FigureĀ 1A depicts the algorithm for simulating SNVs.


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

Pattnaik S, Gupta S, Rao AA, Panda B - BMC Bioinformatics (2014)

Flowcharts indicate the algorithm implemented in simulation of A) SNP, B) Indel, C) CNV and subsequent D) read generation process.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3926339&req=5

Figure 1: Flowcharts indicate the algorithm implemented in simulation of A) SNP, B) Indel, C) CNV and subsequent D) read generation process.
Mentions: This ensures that the simulated SNVs are well distributed over the genome. A positional filter is applied to remove the outlier SNVs, which are less than 15 bases apart. SInC simulator neglects SNVs simulated in the N-regions of the genome (where there is no A, T, G or C). Then the algorithm applies a user-defined transition to transversion (Ti/Tv) metric to maintain the biological significance of the SNVs across the genome. A Ti/Tv ratio of 2.1 was maintained across the population of simulated SNVs with 20% inherent heterozygosity to simulate human genome data as previously reported[32]. The flow chart illustrated in FigureĀ 1A depicts the algorithm for simulating SNVs.

Bottom Line: SInC is capable of generating single- and paired-end reads with user-defined insert size and with high efficiency compared to the other existing tools.SInC, due to its multi-threaded capability during read generation, has a low time footprint.We have come up with a user-friendly multi-variant simulator and read-generator tools called SInC.

View Article: PubMed Central - HTML - PubMed

Affiliation: Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Biotech Park, Electronic City Phase I, Bangalore 560100, India. binay@ganitlabs.in.

ABSTRACT

Background: The rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration.

Results: We report SInC (Snp, Indel and Cnv) an open-source variant simulator and read generator capable of simulating all the three common types of biological variants taking into account a distribution of base quality score from a most commonly used next-generation sequencing instrument from Illumina. SInC is capable of generating single- and paired-end reads with user-defined insert size and 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.

Conclusions: We have come up with a user-friendly multi-variant simulator and read-generator tools called SInC. SInC can be downloaded from http://sourceforge.net/projects/sincsimulator.

Show MeSH