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Cloud Prediction of Protein Structure and Function with PredictProtein for Debian

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dc.contributor.authorKajan, Laszlo-
dc.contributor.authorYachdav, Guy-
dc.contributor.authorVicedo, Esmeralda-
dc.contributor.authorSteinegger, Martin-
dc.contributor.authorMirdita, Milot-
dc.contributor.authorAngermueller, Christof-
dc.contributor.authorBoehm, Ariane-
dc.contributor.authorDomke, Simon-
dc.contributor.authorErtl, Julia-
dc.contributor.authorMertes, Christian-
dc.contributor.authorReisinger, Eva-
dc.contributor.authorStaniewski, Cedric-
dc.contributor.authorRost, Burkhard-
dc.date.accessioned2024-05-20T00:42:45Z-
dc.date.available2024-05-20T00:42:45Z-
dc.date.created2024-05-16-
dc.date.created2024-05-16-
dc.date.issued2013-
dc.identifier.citationBIOMED RESEARCH INTERNATIONAL, Vol.2013-
dc.identifier.issn2314-6133-
dc.identifier.urihttps://hdl.handle.net/10371/203412-
dc.description.abstractWe report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.-
dc.language영어-
dc.publisherHINDAWI LTD-
dc.titleCloud Prediction of Protein Structure and Function with PredictProtein for Debian-
dc.typeArticle-
dc.identifier.doi10.1155/2013/398968-
dc.citation.journaltitleBIOMED RESEARCH INTERNATIONAL-
dc.identifier.wosid000322480000001-
dc.identifier.scopusid2-s2.0-84881507563-
dc.citation.volume2013-
dc.description.isOpenAccessY-
dc.contributor.affiliatedAuthorSteinegger, Martin-
dc.type.docTypeArticle-
dc.description.journalClass1-
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Related Researcher

  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data

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