Broom: application for non-redundant storage of high throughput sequencing data

Albayrak, Levent; Khanipov, Kamil; Golovko, George; Fofanov, Yuriy; Valencia, ed., Alfonso

doi:10.1093/bioinformatics/bty580

Title: Broom: application for non-redundant storage of high throughput sequencing data

Abstract

Abstract Motivation The data generation capabilities of high throughput sequencing (HTS) instruments have exponentially increased over the last few years, while the cost of sequencing has dramatically decreased allowing this technology to become widely used in biomedical studies. For small labs and individual researchers, however, storage and transfer of large amounts of HTS data present a significant challenge. The recent trends in increased sequencing quality and genome coverage can be used to reconsider HTS data storage strategies. Results We present Broom, a stand-alone application designed to select and store only high-quality sequencing reads at extremely high compression rates. Written in C++, the application accepts single and paired-end reads in FASTQ and FASTA formats and decompresses data in FASTA format. Availability and implementation C++ code available at https://scsb.utmb.edu/labgroups/fofanov/broom.asp. Supplementary information Supplementary data are available at Bioinformatics online.

Authors:: Albayrak, Levent; Khanipov, Kamil; Golovko, George; Fofanov, Yuriy; Valencia, ed., Alfonso

Publication Date:: 2018-07-13

Sponsoring Org.:: USDOE

OSTI Identifier:: 1487297

Resource Type:: Published Article

Journal Name:: Bioinformatics

Additional Journal Information:: Journal Name: Bioinformatics Journal Volume: 35 Journal Issue: 1; Journal ID: ISSN 1367-4803

Publisher:: Oxford University Press

Country of Publication:: United Kingdom

Language:: English

Citation Formats


                    Albayrak, Levent, Khanipov, Kamil, Golovko, George, Fofanov, Yuriy, and Valencia, ed., Alfonso. Broom: application for non-redundant storage of high throughput sequencing data.  United Kingdom: N. p., 2018. 
Web.  doi:10.1093/bioinformatics/bty580.

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                    Albayrak, Levent, Khanipov, Kamil, Golovko, George, Fofanov, Yuriy, & Valencia, ed., Alfonso. Broom: application for non-redundant storage of high throughput sequencing data.  United Kingdom.  https://doi.org/10.1093/bioinformatics/bty580

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                    Albayrak, Levent, Khanipov, Kamil, Golovko, George, Fofanov, Yuriy, and Valencia, ed., Alfonso. Fri .  
"Broom: application for non-redundant storage of high throughput sequencing data".  United Kingdom.  https://doi.org/10.1093/bioinformatics/bty580.

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@article{osti_1487297,

  title        = {Broom: application for non-redundant storage of high throughput sequencing data},

  author       = {Albayrak, Levent and Khanipov, Kamil and Golovko, George and Fofanov, Yuriy and Valencia, ed., Alfonso},

  abstractNote = {Abstract Motivation The data generation capabilities of high throughput sequencing (HTS) instruments have exponentially increased over the last few years, while the cost of sequencing has dramatically decreased allowing this technology to become widely used in biomedical studies. For small labs and individual researchers, however, storage and transfer of large amounts of HTS data present a significant challenge. The recent trends in increased sequencing quality and genome coverage can be used to reconsider HTS data storage strategies. Results We present Broom, a stand-alone application designed to select and store only high-quality sequencing reads at extremely high compression rates. Written in C++, the application accepts single and paired-end reads in FASTQ and FASTA formats and decompresses data in FASTA format. Availability and implementation C++ code available at https://scsb.utmb.edu/labgroups/fofanov/broom.asp. Supplementary information Supplementary data are available at Bioinformatics online.},

  doi          = {10.1093/bioinformatics/bty580},

  journal      = {Bioinformatics},

  number       = 1,

  volume       = 35,

  place        = {United Kingdom},

  year         = {2018},

  month        = {7}

}

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Works referenced in this record:

The MPEG video compression algorithm
journal, April 1992

Le Gall, Didier J.
Signal Processing: Image Communication, Vol. 4, Issue 2
DOI: 10.1016/0923-5965(92)90019-C

The Sequence Read Archive
journal, November 2010

Leinonen, R.; Sugawara, H.; Shumway, M.
Nucleic Acids Research, Vol. 39, Issue Database
DOI: 10.1093/nar/gkq1019

Compression of next-generation sequencing reads aided by highly efficient de novo assembly
journal, August 2012

Jones, Daniel C.; Ruzzo, Walter L.; Peng, Xinxia
Nucleic Acids Research, Vol. 40, Issue 22
DOI: 10.1093/nar/gks754

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Title: Broom: application for non-redundant storage of high throughput sequencing data

Abstract

Citation Formats

The MPEG video compression algorithm journal, April 1992

The Sequence Read Archive journal, November 2010

Compression of next-generation sequencing reads aided by highly efficient de novo assembly journal, August 2012

The MPEG video compression algorithm
journal, April 1992

The Sequence Read Archive
journal, November 2010

Compression of next-generation sequencing reads aided by highly efficient de novo assembly
journal, August 2012