Szymon Grabowski1, Sebastian Deorowicz1, Łukasz Roguski2. 1. Institute of Applied Computer Science, Lodz University of Technology, Al. Politechniki 11, 90-924 Łódź, Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warszawa, Poland and Centro Nacional de Análisis Genómico (CNAG), 08-028 Barcelona, Spain. 2. Institute of Applied Computer Science, Lodz University of Technology, Al. Politechniki 11, 90-924 Łódź, Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warszawa, Poland and Centro Nacional de Análisis Genómico (CNAG), 08-028 Barcelona, Spain Institute of Applied Computer Science, Lodz University of Technology, Al. Politechniki 11, 90-924 Łódź, Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warszawa, Poland and Centro Nacional de Análisis Genómico (CNAG), 08-028 Barcelona, Spain.
Abstract
MOTIVATION: High-coverage sequencing data have significant, yet hard to exploit, redundancy. Most FASTQ compressors cannot efficiently compress the DNA stream of large datasets, since the redundancy between overlapping reads cannot be easily captured in the (relatively small) main memory. More interesting solutions for this problem are disk based, where the better of these two, from Cox et al. (2012), is based on the Burrows-Wheeler transform (BWT) and achieves 0.518 bits per base for a 134.0 Gbp human genome sequencing collection with almost 45-fold coverage. RESULTS: We propose overlapping reads compression with minimizers, a compression algorithm dedicated to sequencing reads (DNA only). Our method makes use of a conceptually simple and easily parallelizable idea of minimizers, to obtain 0.317 bits per base as the compression ratio, allowing to fit the 134.0 Gbp dataset into only 5.31 GB of space. AVAILABILITY AND IMPLEMENTATION: http://sun.aei.polsl.pl/orcom under a free license. CONTACT: sebastian.deorowicz@polsl.pl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: High-coverage sequencing data have significant, yet hard to exploit, redundancy. Most FASTQ compressors cannot efficiently compress the DNA stream of large datasets, since the redundancy between overlapping reads cannot be easily captured in the (relatively small) main memory. More interesting solutions for this problem are disk based, where the better of these two, from Cox et al. (2012), is based on the Burrows-Wheeler transform (BWT) and achieves 0.518 bits per base for a 134.0 Gbphuman genome sequencing collection with almost 45-fold coverage. RESULTS: We propose overlapping reads compression with minimizers, a compression algorithm dedicated to sequencing reads (DNA only). Our method makes use of a conceptually simple and easily parallelizable idea of minimizers, to obtain 0.317 bits per base as the compression ratio, allowing to fit the 134.0 Gbp dataset into only 5.31 GB of space. AVAILABILITY AND IMPLEMENTATION: http://sun.aei.polsl.pl/orcom under a free license. CONTACT: sebastian.deorowicz@polsl.pl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Ibrahim Numanagić; James K Bonfield; Faraz Hach; Jan Voges; Jörn Ostermann; Claudio Alberti; Marco Mattavelli; S Cenk Sahinalp Journal: Nat Methods Date: 2016-10-24 Impact factor: 28.547
Authors: Antonio A Ginart; Joseph Hui; Kaiyuan Zhu; Ibrahim Numanagić; Thomas A Courtade; S Cenk Sahinalp; David N Tse Journal: Nat Commun Date: 2018-02-08 Impact factor: 14.919