Authors:
Yoshihiro Shibuya
1
and
Matteo Comin
2
Affiliations:
1
Department of Information Engineering, University of Padua, via Gradenigo 6B, Padua, Italy, Laboratoire d’Informatique Gaspard-Monge (LIGM), University Paris-Est Marne-la-Vallée, Bâtiment Copernic - 5, bd Descartes, Champs sur Marne, France
;
2
Department of Information Engineering, University of Padua, via Gradenigo 6B, Padua, Italy
Keyword(s):
k-mers, Indexing, Quality Score, Read Compression.
Abstract:
Many bioinformatics tools heavily rely on k-mer dictionaries to describe the composition of sequences and allow for faster reference-free algorithms or look-ups. Unfortunately, naive k-mer dictionaries are very memory inefficient, requiring very large amount of storage space to save each k-mer. This problem is generally worsened by the necessity of an index for fast queries. In this work we discuss how to build an indexed linear reference containing a set of input k-mers, and its application to the compression of quality score in FASTQ files. Most of the entropy of sequencing data lies in the quality scores, and thus they are difficult to compress. Here, we present an application to improve the compressibility of quality values while preserving the information for SNPs calling. We show how a dictionary of significant k-mers, obtained from SNPs databases and multiple genomes, can be indexed in linear space and used to improve the compression of quality value. Availability: the software
is freely available at https://github.com/yhhshb/yalff.
(More)