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Protein Sequence Analysis Using the MPI Bioinformatics Toolkit

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dc.contributor.authorGabler, Felix-
dc.contributor.authorNam, Seung-Zin-
dc.contributor.authorTill, Sebastian-
dc.contributor.authorMirdita, Milot-
dc.contributor.authorSteinegger, Martin-
dc.contributor.authorSöding, Johannes-
dc.contributor.authorLupas, Andrei N.-
dc.contributor.authorAlva, Vikram-
dc.date.accessioned2024-05-16T01:28:25Z-
dc.date.available2024-05-16T01:28:25Z-
dc.date.created2021-01-27-
dc.date.created2021-01-27-
dc.date.issued2020-12-
dc.identifier.citationCurrent Protocols in Bioinformatics, Vol.72 No.1, p. e108-
dc.identifier.issn1934-3396-
dc.identifier.urihttps://hdl.handle.net/10371/202565-
dc.description.abstract© 2020 The Authors.The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) provides interactive access to a wide range of the best-performing bioinformatics tools and databases, including the state-of-the-art protein sequence comparison methods HHblits and HHpred. The Toolkit currently includes 35 external and in-house tools, covering functionalities such as sequence similarity searching, prediction of sequence features, and sequence classification. Due to this breadth of functionality, the tight interconnection of its constituent tools, and its ease of use, the Toolkit has become an important resource for biomedical research and for teaching protein sequence analysis to students in the life sciences. In this article, we provide detailed information on utilizing the three most widely accessed tools within the Toolkit: HHpred for the detection of homologs, HHpred in conjunction with MODELLER for structure prediction and homology modeling, and CLANS for the visualization of relationships in large sequence datasets. © 2020 The Authors. Basic Protocol 1: Sequence similarity searching using HHpred. Alternate Protocol: Pairwise sequence comparison using HHpred. Support Protocol: Building a custom multiple sequence alignment using PSI-BLAST and forwarding it as input to HHpred. Basic Protocol 2: Calculation of homology models using HHpred and MODELLER. Basic Protocol 3: Cluster analysis using CLANS.-
dc.language영어-
dc.publisherJohn Wiley & Sons Inc.-
dc.titleProtein Sequence Analysis Using the MPI Bioinformatics Toolkit-
dc.typeArticle-
dc.identifier.doi10.1002/cpbi.108-
dc.citation.journaltitleCurrent Protocols in Bioinformatics-
dc.identifier.scopusid2-s2.0-85097878981-
dc.citation.number1-
dc.citation.startpagee108-
dc.citation.volume72-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorSteinegger, Martin-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordAuthorCLANS-
dc.subject.keywordAuthorcluster analysis-
dc.subject.keywordAuthorHHpred-
dc.subject.keywordAuthorHMM-
dc.subject.keywordAuthorhomology-
dc.subject.keywordAuthorprofile hidden Markov models-
dc.subject.keywordAuthorsequence comparison-
dc.subject.keywordAuthorsequence similarity searches-
dc.subject.keywordAuthorstructure prediction-
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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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