Algorithms for the De Novo Sequencing of Peptides From Tandem Mass Spectra

dc.contributor.author Allmer, Jens
dc.coverage.doi 10.1586/epr.11.54
dc.date.accessioned 2017-03-06T08:30:52Z
dc.date.available 2017-03-06T08:30:52Z
dc.date.issued 2011
dc.description.abstract Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field. © 2011 Expert Reviews Ltd. en_US
dc.description.sponsorship The Turkish Academy of Science (TÜBA) en_US
dc.identifier.citation Allmer, J. (2011). Algorithms for the de novo sequencing of peptides from tandem mass spectra. Expert Review of Proteomics, 8(5), 645-657. doi:10.1586/epr.11.54 en_US
dc.identifier.doi 10.1586/epr.11.54 en_US
dc.identifier.issn 1478-9450
dc.identifier.issn 1744-8387
dc.identifier.scopus 2-s2.0-80054760698
dc.identifier.uri http://doi.org/10.1586/epr.11.54
dc.identifier.uri http://hdl.handle.net/11147/4976
dc.language.iso en en_US
dc.publisher Taylor & Francis en_US
dc.relation.ispartof Expert Review of Proteomics en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Tandem MS en_US
dc.subject Mass spectrometry en_US
dc.subject De novo en_US
dc.subject Proteomics en_US
dc.subject Sequencing en_US
dc.title Algorithms for the De Novo Sequencing of Peptides From Tandem Mass Spectra en_US
dc.title.alternative De novo sequencing of MS/MS spectra en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Allmer, Jens
gdc.bip.impulseclass C4
gdc.bip.influenceclass C4
gdc.bip.popularityclass C4
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department İzmir Institute of Technology. Molecular Biology and Genetics en_US
gdc.description.endpage 657 en_US
gdc.description.issue 5 en_US
gdc.description.publicationcategory Diğer en_US
gdc.description.scopusquality Q2
gdc.description.startpage 645 en_US
gdc.description.volume 8 en_US
gdc.description.wosquality Q2
gdc.identifier.openalex W2057282070
gdc.identifier.pmid 21999834
gdc.identifier.wos WOS:000296249500014
gdc.index.type WoS
gdc.index.type Scopus
gdc.index.type PubMed
gdc.oaire.accesstype BRONZE
gdc.oaire.diamondjournal false
gdc.oaire.impulse 21.0
gdc.oaire.influence 6.5429426E-9
gdc.oaire.isgreen false
gdc.oaire.keywords Proteomics
gdc.oaire.keywords Molecular Sequence Data
gdc.oaire.keywords Proteins
gdc.oaire.keywords Sequence Analysis, Protein
gdc.oaire.keywords Tandem Mass Spectrometry
gdc.oaire.keywords Humans
gdc.oaire.keywords Amino Acid Sequence
gdc.oaire.keywords Databases, Protein
gdc.oaire.keywords Peptides
gdc.oaire.keywords Algorithms
gdc.oaire.keywords Software
gdc.oaire.popularity 2.4819894E-8
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0301 basic medicine
gdc.oaire.sciencefields 0303 health sciences
gdc.oaire.sciencefields 03 medical and health sciences
gdc.openalex.collaboration National
gdc.openalex.fwci 4.49855538
gdc.openalex.normalizedpercentile 0.95
gdc.openalex.toppercent TOP 10%
gdc.opencitations.count 101
gdc.plumx.crossrefcites 93
gdc.plumx.mendeley 92
gdc.plumx.pubmedcites 34
gdc.plumx.scopuscites 106
gdc.scopus.citedcount 106
gdc.wos.citedcount 91
relation.isAuthorOfPublication.latestForDiscovery bf9f97a4-6d62-49cd-a7c8-1bc8463d14d2
relation.isOrgUnitOfPublication.latestForDiscovery 9af2b05f-28ac-4013-8abe-a4dfe192da5e

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