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FASTQSim:高通量测序数据模拟应用,支持 illumina/ion/pacbio/roche平台

标题:

FASTQSim: platform-independent data characterization and in silico read generation for NGS datasets.

摘要:

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.

文章:

http://bmcresnotes.biomedcentral.com/articles/10.1186/1756-0500-7-533

源码:

https://sourceforge.net/projects/fastqsim

安装:

    axel  https://sourceforge.net/projects/fastqsim/files/FASTQsim_v2.0.tgz/download
    tar xzvf  FASTQsim_v2.0.tgz
    mv   FASTQsim_v2.0  FASTQsim-2.0

导读:

FASTQSim:高通量测序数据模拟应用,支持 illumina/ion/pacbio/roche平台,被广泛用于metagenome数据模拟,比如文章:
Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim

版本:

2016-11-13.v1

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