Literature DB >> 29077806

Quark enables semi-reference-based compression of RNA-seq data.

Hirak Sarkar1, Rob Patro1.   

Abstract

MOTIVATION: The past decade has seen an exponential increase in biological sequencing capacity, and there has been a simultaneous effort to help organize and archive some of the vast quantities of sequencing data that are being generated. Although these developments are tremendous from the perspective of maximizing the scientific utility of available data, they come with heavy costs. The storage and transmission of such vast amounts of sequencing data is expensive.
RESULTS: We present Quark, a semi-reference-based compression tool designed for RNA-seq data. Quark makes use of a reference sequence when encoding reads, but produces a representation that can be decoded independently, without the need for a reference. This allows Quark to achieve markedly better compression rates than existing reference-free schemes, while still relieving the burden of assuming a specific, shared reference sequence between the encoder and decoder. We demonstrate that Quark achieves state-of-the-art compression rates, and that, typically, only a small fraction of the reference sequence must be encoded along with the reads to allow reference-free decompression.
AVAILABILITY AND IMPLEMENTATION: Quark is implemented in C ++11, and is available under a GPLv3 license at www.github.com/COMBINE-lab/quark. CONTACT: rob.patro@cs.stonybrook.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

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Year:  2017        PMID: 29077806     DOI: 10.1093/bioinformatics/btx428

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  1 in total

1.  BdBG: a bucket-based method for compressing genome sequencing data with dynamic de Bruijn graphs.

Authors:  Rongjie Wang; Junyi Li; Yang Bai; Tianyi Zang; Yadong Wang
Journal:  PeerJ       Date:  2018-10-19       Impact factor: 2.984

  1 in total

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