Rapid evaluation and quality control of next generation sequencing data with FaQCs

Lo, Chien -Chi; Chain, Patrick S. G.

doi:10.1186/s12859-014-0366-2

Title: Rapid evaluation and quality control of next generation sequencing data with FaQCs

Journal Article · Mon Dec 01 00:00:00 EST 2014 · BMC Bioinformatics

DOI:https://doi.org/10.1186/s12859-014-0366-2· OSTI ID:1200616

Lo, Chien -Chi ^[1]; Chain, Patrick S. G. ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Background: Next generation sequencing (NGS) technologies that parallelize the sequencing process and produce thousands to millions, or even hundreds of millions of sequences in a single sequencing run, have revolutionized genomic and genetic research. Because of the vagaries of any platform's sequencing chemistry, the experimental processing, machine failure, and so on, the quality of sequencing reads is never perfect, and often declines as the read is extended. These errors invariably affect downstream analysis/application and should therefore be identified early on to mitigate any unforeseen effects. Results: Here we present a novel FastQ Quality Control Software (FaQCs) that can rapidly process large volumes of data, and which improves upon previous solutions to monitor the quality and remove poor quality data from sequencing runs. Both the speed of processing and the memory footprint of storing all required information have been optimized via algorithmic and parallel processing solutions. The trimmed output compared side-by-side with the original data is part of the automated PDF output. We show how this tool can help data analysis by providing a few examples, including an increased percentage of reads recruited to references, improved single nucleotide polymorphism identification as well as de novo sequence assembly metrics. Conclusion: FaQCs combines several features of currently available applications into a single, user-friendly process, and includes additional unique capabilities such as filtering the PhiX control sequences, conversion of FASTQ formats, and multi-threading. The original data and trimmed summaries are reported within a variety of graphics and reports, providing a simple way to do data quality control and assurance.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1200616

Report Number(s):: LA-UR-13-20812

Journal Information:: BMC Bioinformatics, Vol. 15, Issue 1; ISSN 1471-2105

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 128 works

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