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
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

4506-4507

Informations de copyright

© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Guillermo Dufort Y Álvarez (G)

Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay.

Gadiel Seroussi (G)

Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay.
Xperi Corp, San Jose, CA 95134, USA.

Pablo Smircich (P)

Facultad de Ciencias, Universidad de la República, Montevideo 11400, Uruguay.
Departamento de Genómica, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo 11600, Uruguay.

José Sotelo (J)

Facultad de Ciencias, Universidad de la República, Montevideo 11400, Uruguay.
Departamento de Genómica, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo 11600, Uruguay.

Idoia Ochoa (I)

TECNUN School of Engineering, University of Navarra, Donostia-San Sebastián 20018, Spain.

Álvaro Martín (Á)

Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay.

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Classifications MeSH