FASTQSim: platform-independent data characterization and in silico read generation for NGS datasets
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1756-0500-7-533.pdf
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3.68 MB
Format
Adobe PDF
Checksum (MD5)
bc2801378ac4abe0b1999133e744f44f
Author(s)
Shcherbina, Anna
Date Issued
August 2014
Journal
BMC Research Notes
Publisher
BioMed Central Ltd
Citation
Shcherbina, Anna. "FASTQSim: platform-independent data characterization and in silico read generation for NGS datasets." BMC Research Notes 2014, 7:533.
Version
Final published version
Abstract
Background
High-throughput next generation sequencing technologies have enabled rapid characterization of clinical and environmental samples. Consequently, the largest bottleneck to actionable data has become sample processing and bioinformatics analysis, creating a need for accurate and rapid algorithms to process genetic data. Perfectly characterized in silico datasets are a useful tool for evaluating the performance of such algorithms. Background contaminating organisms are observed in sequenced mixtures of organisms. In silico samples provide exact truth. To create the best value for evaluating algorithms, in silico data should mimic actual sequencer data as closely as possible.
Results
FASTQSim is a tool that provides the dual functionality of NGS dataset characterization and metagenomic data generation. FASTQSim is sequencing platform-independent, and computes distributions of read length, quality scores, indel rates, single point mutation rates, indel size, and similar statistics for any sequencing platform. To create training or testing datasets, FASTQSim has the ability to convert target sequences into in silico reads with specific error profiles obtained in the characterization step.
Conclusions
FASTQSim enables users to assess the quality of NGS datasets. The tool provides information about read length, read quality, repetitive and non-repetitive indel profiles, and single base pair substitutions. FASTQSim allows the user to simulate individual read datasets that can be used as standardized test scenarios for planning sequencing projects or for benchmarking metagenomic software. In this regard, in silico datasets generated with the FASTQsim tool hold several advantages over natural datasets: they are sequencing platform independent, extremely well characterized, and less expensive to generate. Such datasets are valuable in a number of applications, including the training of assemblers for multiple platforms, benchmarking bioinformatics algorithm performance, and creating challenge datasets for detecting genetic engineering toolmarks, etc.
MIT Department
Lincoln Laboratory
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Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/1756-0500-7-533
