Minirmd: accurate and fast duplicate removal tool for short reads via multiple minimizers.

Liu, Y; Zhang, X; Zou, Q; Zeng, X

Minirmd: accurate and fast duplicate removal tool for short reads via multiple minimizers.

Liu, Y

Zhang, X

Zou, Q Zeng, X

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Publisher:: Oxford University Press
Publication Type:: Journal Article
Citation:: Bioinformatics, 2021, 37, (11), pp. 1604-1606
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Liu, Y https://orcid.org/0000-0002-7680-3155
dc.contributor.author	Zhang, X https://orcid.org/0000-0002-3783-6560
dc.contributor.author	Zou, Q
dc.contributor.author	Zeng, X
dc.date.accessioned	2021-10-02T23:19:00Z
dc.date.available	2020-10-14
dc.date.available	2021-10-02T23:19:00Z
dc.date.issued	2021
dc.identifier.citation	Bioinformatics, 2021, 37, (11), pp. 1604-1606
dc.identifier.issn	1367-4803
dc.identifier.issn	1367-4811
dc.identifier.uri	http://hdl.handle.net/10453/150807
dc.description.abstract	Removing duplicate and near-duplicate reads, generated by high-throughput sequencing technologies, is able to reduce computational resources in downstream applications. Here we develop minirmd, a de novo tool to remove duplicate reads via multiple rounds of clustering using different length of minimizer. Experiments demonstrate that minirmd removes more near-duplicate reads than existing clustering approaches and is faster than existing multi-core tools. To the best of our knowledge, minirmd is the first tool to remove near-duplicates on reverse-complementary strand
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Oxford University Press
dc.relation.ispartof	Bioinformatics
dc.relation.isbasedon	10.1093/bioinformatics/btaa915
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 06 Biological Sciences, 08 Information and Computing Sciences
dc.subject.classification	Bioinformatics
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Sequence Analysis, DNA
dc.subject.mesh	Algorithms
dc.subject.mesh	Software
dc.subject.mesh	High-Throughput Nucleotide Sequencing
dc.subject.mesh	Algorithms
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	High-Throughput Nucleotide Sequencing
dc.subject.mesh	Sequence Analysis, DNA
dc.subject.mesh	Software
dc.title	Minirmd: accurate and fast duplicate removal tool for short reads via multiple minimizers.
dc.type	Journal Article
utslib.citation.volume	37
utslib.location.activity	England
utslib.for	01 Mathematical Sciences
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-10-02T23:18:59Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	11

Abstract:

Removing duplicate and near-duplicate reads, generated by high-throughput sequencing technologies, is able to reduce computational resources in downstream applications. Here we develop minirmd, a de novo tool to remove duplicate reads via multiple rounds of clustering using different length of minimizer. Experiments demonstrate that minirmd removes more near-duplicate reads than existing clustering approaches and is faster than existing multi-core tools. To the best of our knowledge, minirmd is the first tool to remove near-duplicates on reverse-complementary strand

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/150807