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dc.contributor.authorBartholomäus, Alexander
dc.contributor.authorKolte, Baban
dc.contributor.authorMustafayeva, Ayten
dc.contributor.authorGoebel, Ingrid
dc.contributor.authorFuchs, Stephan
dc.contributor.authorBenndorf, Dirk
dc.contributor.authorEngelmann, Susanne
dc.contributor.authorIgnatova, Zoya
dc.date.accessioned2021-08-23T13:44:18Z
dc.date.available2021-08-23T13:44:18Z
dc.date.issued2021-06-14
dc.identifier.citationNucleic Acids Res. 2021 Jun 14:gkab477. doi: 10.1093/nar/gkab477. Epub ahead of print.en_US
dc.identifier.pmid34125903
dc.identifier.doi10.1093/nar/gkab477
dc.identifier.urihttp://hdl.handle.net/10033/622995
dc.description.abstractEmerging evidence places small proteins (≤50 amino acids) more centrally in physiological processes. Yet, their functional identification and the systematic genome annotation of their cognate small open-reading frames (smORFs) remains challenging both experimentally and computationally. Ribosome profiling or Ribo-Seq (that is a deep sequencing of ribosome-protected fragments) enables detecting of actively translated open-reading frames (ORFs) and empirical annotation of coding sequences (CDSs) using the in-register translation pattern that is characteristic for genuinely translating ribosomes. Multiple identifiers of ORFs that use the 3-nt periodicity in Ribo-Seq data sets have been successful in eukaryotic smORF annotation. They have difficulties evaluating prokaryotic genomes due to the unique architecture (e.g. polycistronic messages, overlapping ORFs, leaderless translation, non-canonical initiation etc.). Here, we present a new algorithm, smORFer, which performs with high accuracy in prokaryotic organisms in detecting putative smORFs. The unique feature of smORFer is that it uses an integrated approach and considers structural features of the genetic sequence along with in-frame translation and uses Fourier transform to convert these parameters into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way, and dependent on the data available for a particular organism, different modules can be selected for smORF search.en_US
dc.language.isoenen_US
dc.publisherOxford Academicen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titlesmORFer: a modular algorithm to detect small ORFs in prokaryotes.en_US
dc.typeArticleen_US
dc.identifier.eissn1362-4962
dc.contributor.departmentHZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany.en_US
dc.identifier.journalNucleic acids researchen_US
refterms.dateFOA2021-08-23T13:44:19Z
dc.source.journaltitleNucleic acids research
dc.source.countryEngland


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International