Tackling the Challenges of FASTQ Referential Compression

Bioinformatics and Biology Insights
Aníbal GuerraSebastián Isaza

Abstract

The exponential growth of genomic data has recently motivated the development of compression algorithms to tackle the storage capacity limitations in bioinformatics centers. Referential compressors could theoretically achieve a much higher compression than their non-referential counterparts; however, the latest tools have not been able to harness such potential yet. To reach such goal, an efficient encoding model to represent the differences between the input and the reference is needed. In this article, we introduce a novel approach for referential compression of FASTQ files. The core of our compression scheme consists of a referential compressor based on the combination of local alignments with binary encoding optimized for long reads. Here we present the algorithms and performance tests developed for our reads compression algorithm, named UdeACompress. Our compressor achieved the best results when compressing long reads and competitive compression ratios for shorter reads when compared to the best programs in the state of the art. As an added value, it also showed reasonable execution times and memory consumption, in comparison with similar tools.

References

May 13, 2010·Briefings in Bioinformatics·Heng Li, Nils Homer
Jan 18, 2014·Nature Reviews. Genetics·David SimsChris P Ponting
Oct 31, 2014·Nature Methods·Faraz HachS Cenk Sahinalp
Mar 21, 2015·Journal of Bioinformatics and Computational Biology·Anirban DuttaSharmila S Mande
Jun 26, 2015·Scientific Reports·Sebastian DeorowiczMarcin Niemiec
Dec 1, 2017·Bioinformatics·Jan VogesMikel Hernaez
Feb 10, 2018·Nature Communications·Antonio A GinartDavid N Tse
Feb 15, 2018·Bioinformatics·Shubham ChandakTsachy Weissman
May 17, 2018·Human Molecular Genetics·Martin O PollardManjinder S Sandhu

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Software Mentioned

UdeACompress
Fqzcomp
FASTQ
Quip
FASTQZ
Bzip2
Kpath
libbzip2
Centos
Leon

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