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Keywords {🔍}

pubmed, google, scholar, article, protein, function, cas, functional, gene, central, proteins, prediction, sequences, methods, annotation, sequence, data, acids, annotations, biol, terms, nucleic, database, res, ontology, biological, information, molecular, transfers, assignments, families, mol, issuedd, search, bioinformatics, chapter, term, approaches, structure, approach, computational, access, jones, biology, results, functions, domain, dessimoz, plos, analysis,

Topics {✒️}

de novo method resolves multi-domain architectures improved homology-independent prediction domain architecture [14] protein-protein interaction maps multi-domain architectures cookies skip key functional residues providing pre-calculated predictions bayesian phylogenomics efficient attention-based approach quantitative sequence-function relationships sequence similarity-based approaches high-throughput functional annotation feature-based prediction unconventionally high e-values paralogues—homologue pairs derived template-based prediction open access chapter protein function prediction short linear motifs—stretches open access charges protein interaction networks key differences [62] jensen lj computational function prediction privacy choices/manage cookies alignment-derived features eddy sr sonnhammer el enzyme function prediction protein domain family functional family based diverse protein families amino acid sequence gene ontology handbook support vector machines sequence-based approaches open access license protein function assessed feature-based methods protein sequences based phylogenetic-based propagation human protein function pairwise sequence identity caenorhabditis elegans reveals current gene ontology store domain assignments diverse data sources term annotation transfers

Questions {❓}

• Tian W, Skolnick J (2003) How well is enzyme function conserved as a function of pairwise sequence identity?

Schema {🗺️}

ScholarlyArticle:
      headline:Computational Methods for Annotation Transfers from Sequence
      pageEnd:67
      pageStart:55
      image:https://media.springernature.com/w153/springer-static/cover/book/978-1-4939-3743-1.jpg
      genre:
         Springer Protocols
      isPartOf:
         name:The Gene Ontology Handbook
         isbn:
            978-1-4939-3743-1
            978-1-4939-3741-7
         type:Book
      publisher:
         name:Springer New York
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Domenico Cozzetto
            affiliation:
                  name:University College London
                  address:
                     name:Bioinformatics Group, Department of Computer Science, University College London, London, UK
                     type:PostalAddress
                  type:Organization
            type:Person
            name:David T. Jones
            affiliation:
                  name:University College London
                  address:
                     name:Bioinformatics Group, Department of Computer Science, University College London, London, UK
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
      keywords:Protein function prediction, Homology-based annotation transfers, Phylogenomics, Multi-domain architecture, De novo function prediction
      description:Surveys of public sequence resources show that experimentally supported functional information is still completely missing for a considerable fraction of known proteins and is clearly incomplete for an even larger portion. Bioinformatics methods have long made use of very diverse data sources alone or in combination to predict protein function, with the understanding that different data types help elucidate complementary biological roles. This chapter focuses on methods accepting amino acid sequences as input and producing GO term assignments directly as outputs; the relevant biological and computational concepts are presented along with the advantages and limitations of individual approaches.
      datePublished:2017
      isAccessibleForFree:1
      context:https://schema.org
Book:
      name:The Gene Ontology Handbook
      isbn:
         978-1-4939-3743-1
         978-1-4939-3741-7
Organization:
      name:Springer New York
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:University College London
      address:
         name:Bioinformatics Group, Department of Computer Science, University College London, London, UK
         type:PostalAddress
      name:University College London
      address:
         name:Bioinformatics Group, Department of Computer Science, University College London, London, UK
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Domenico Cozzetto
      affiliation:
            name:University College London
            address:
               name:Bioinformatics Group, Department of Computer Science, University College London, London, UK
               type:PostalAddress
            type:Organization
      name:David T. Jones
      affiliation:
            name:University College London
            address:
               name:Bioinformatics Group, Department of Computer Science, University College London, London, UK
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:Bioinformatics Group, Department of Computer Science, University College London, London, UK
      name:Bioinformatics Group, Department of Computer Science, University College London, London, UK

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