ENANO: Encoder for NANOpore FASTQ files.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
15 08 2020
15 08 2020
Historique:
received:
16
03
2020
revised:
07
05
2020
accepted:
26
05
2020
pubmed:
30
5
2020
medline:
20
2
2021
entrez:
30
5
2020
Statut:
ppublish
Résumé
The amount of genomic data generated globally is seeing explosive growth, leading to increasing needs for processing, storage and transmission resources, which motivates the development of efficient compression tools for these data. Work so far has focused mainly on the compression of data generated by short-read technologies. However, nanopore sequencing technologies are rapidly gaining popularity due to the advantages offered by the large increase in the average size of the produced reads, the reduction in their cost and the portability of the sequencing technology. We present ENANO (Encoder for NANOpore), a novel lossless compression algorithm especially designed for nanopore sequencing FASTQ files. The main focus of ENANO is on the compression of the quality scores, as they dominate the size of the compressed file. ENANO offers two modes, Maximum Compression and Fast (default), which trade-off compression efficiency and speed. We tested ENANO, the current state-of-the-art compressor SPRING and the general compressor pigz on several publicly available nanopore datasets. The results show that the proposed algorithm consistently achieves the best compression performance (in both modes) on every considered nanopore dataset, with an average improvement over pigz and SPRING of >24.7% and 6.3%, respectively. In addition, in terms of encoding and decoding speeds, ENANO is 2.9× and 1.7× times faster than SPRING, respectively, with memory consumption up to 0.2 GB. ENANO is freely available for download at: https://github.com/guilledufort/EnanoFASTQ. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 32470109
pii: 5848644
doi: 10.1093/bioinformatics/btaa551
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4506-4507Informations de copyright
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.