CoLoRd: compressing long reads

Marek Kokot; Adam Gudyś; Heng Li; Sebastian Deorowicz

doi:10.1038/s41592-022-01432-3

CoLoRd: compressing long reads

Department of Algorithmics and Software (RAU/RAU5)

Dana-Farber Cancer Institute
Harvard University

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

The cost of maintaining exabytes of data produced by sequencing experiments every year has become a major issue in today’s genomic research. In spite of the increasing popularity of third-generation sequencing, the existing algorithms for compressing long reads exhibit a minor advantage over the general-purpose gzip. We present CoLoRd, an algorithm able to reduce the size of third-generation sequencing data by an order of magnitude without affecting the accuracy of downstream analyses.

Original language	English
Pages (from-to)	441-444
Number of pages	4
Journal	Nature Methods
Volume	19
Issue number	4
DOIs	https://doi.org/10.1038/s41592-022-01432-3
Publication status	Published - Apr 2022

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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10.1038/s41592-022-01432-3

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CoLoRd: compressing long reads

Abstract

ASJC Scopus subject areas

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