Mauve assembly metrics

Darling, AE; Tritt, A; Eisen, JA; Facciotti, MT

Mauve assembly metrics

Darling, AE

Tritt, A Eisen, JA Facciotti, MT

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Publication Type:: Journal Article
Citation:: Bioinformatics, 2011, 27 (19), pp. 2756 - 2757
Issue Date:: 2011-10-01

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Field	Value	Language
dc.contributor.author	Darling, AE https://orcid.org/0000-0003-2397-7925	en_US
dc.contributor.author	Tritt, A	en_US
dc.contributor.author	Eisen, JA	en_US
dc.contributor.author	Facciotti, MT	en_US
dc.date.issued	2011-10-01	en_US
dc.identifier.citation	Bioinformatics, 2011, 27 (19), pp. 2756 - 2757	en_US
dc.identifier.issn	1367-4803	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28875
dc.description.abstract	Summary: High-throughput DNA sequencing technologies have spurred the development of numerous novel methods for genome assembly. With few exceptions, these algorithms are heuristic and require one or more parameters to be manually set by the user. One approach to parameter tuning involves assembling data from an organism with an available high-quality reference genome, and measuring assembly accuracy using some metrics.We developed a system to measure assembly quality under several scoring metrics, and to compare assembly quality across a variety of assemblers, sequence data types, and parameter choices. When used in conjunction with training data such as a high-quality reference genome and sequence reads from the same organism, our program can be used to manually identify an optimal sequencing and assembly strategy for de novo sequencing of related organisms. © The Author(s) 2011. Published by Oxford University Press.	en_US
dc.relation.ispartof	Bioinformatics	en_US
dc.relation.isbasedon	10.1093/bioinformatics/btr451	en_US
dc.subject.classification	Bioinformatics	en_US
dc.subject.mesh	Haloferax volcanii	en_US
dc.subject.mesh	Sequence Alignment	en_US
dc.subject.mesh	Sequence Analysis, DNA	en_US
dc.subject.mesh	Base Sequence	en_US
dc.subject.mesh	Genome	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	High-Throughput Nucleotide Sequencing	en_US
dc.title	Mauve assembly metrics	en_US
dc.type	Journal Article
utslib.citation.volume	19	en_US
utslib.citation.volume	27	en_US
utslib.for	0605 Microbiology	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - ithree - Institute of Infection, Immunity and Innovation
utslib.copyright.status	closed_access
pubs.issue	19	en_US
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

Summary: High-throughput DNA sequencing technologies have spurred the development of numerous novel methods for genome assembly. With few exceptions, these algorithms are heuristic and require one or more parameters to be manually set by the user. One approach to parameter tuning involves assembling data from an organism with an available high-quality reference genome, and measuring assembly accuracy using some metrics.We developed a system to measure assembly quality under several scoring metrics, and to compare assembly quality across a variety of assemblers, sequence data types, and parameter choices. When used in conjunction with training data such as a high-quality reference genome and sequence reads from the same organism, our program can be used to manually identify an optimal sequencing and assembly strategy for de novo sequencing of related organisms. © The Author(s) 2011. Published by Oxford University Press.

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

http://hdl.handle.net/10453/28875