Literature DB >> 10487860

Evaluation of human-readable annotation in biomolecular sequence databases with biological rule libraries.

F Eisenhaber1, P Bork.   

Abstract

MOTIVATION: Computer-based selection of entries from sequence databases with respect to a related functional description, e.g. with respect to a common cellular localization or contributing to the same phenotypic function, is a difficult task. Automatic semantic analysis of annotations is not only hampered by incomplete functional assignments. A major problem is that annotations are written in a rich, non-formalized language and are meant for reading by a human expert. This person can extract from the text considerably more information than is immediately apparent due to his extended biological background knowledge and logical reasoning. APPROACH: A technique of automated annotation evaluation based on a combination of lexical analysis and the usage of biological rule libraries has been developed. The proposed algorithm generates new functional descriptors from the annotation of a given entry using the semantic units of the annotation as prepositions for implications executed in accordance with the rule library.
RESULTS: The prototype of a software system, the Meta_A(nnotator) program, is described and the results of its application to sequence attribute assignment and sequence selection problems, such as cellular localization and sequence domain annotation of SWISS-PROT entries, are presented. The current software version assigns useful subcellular localization qualifiers to approximately 88% of all SWISS-PROT entries. As shown by demonstrative examples, the combination of sequence and annotation analysis is a powerful approach for the detection of mutual annotation/sequence inconsistencies. AVAILABILITY: Results for the cellular localization assignment can be viewed at the URL http://www.bork. embl-heidelberg.de/CELL_LOC/CELL_LOC.html.

Entities:  

Mesh:

Substances:

Year:  1999        PMID: 10487860     DOI: 10.1093/bioinformatics/15.7.528

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  9 in total

1.  Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature.

Authors:  Soumya Raychaudhuri; Jeffrey T Chang; Patrick D Sutphin; Russ B Altman
Journal:  Genome Res       Date:  2002-01       Impact factor: 9.043

2.  Using text analysis to identify functionally coherent gene groups.

Authors:  Soumya Raychaudhuri; Hinrich Schütze; Russ B Altman
Journal:  Genome Res       Date:  2002-10       Impact factor: 9.043

3.  The computational analysis of scientific literature to define and recognize gene expression clusters.

Authors:  Soumya Raychaudhuri; Jeffrey T Chang; Farhad Imam; Russ B Altman
Journal:  Nucleic Acids Res       Date:  2003-08-01       Impact factor: 16.971

4.  Predicting protein cellular localization using a domain projection method.

Authors:  Richard Mott; Jörg Schultz; Peer Bork; Chris P Ponting
Journal:  Genome Res       Date:  2002-08       Impact factor: 9.043

5.  Predicting transmembrane beta-barrels in proteomes.

Authors:  Henry R Bigelow; Donald S Petrey; Jinfeng Liu; Dariusz Przybylski; Burkhard Rost
Journal:  Nucleic Acids Res       Date:  2004-05-11       Impact factor: 16.971

6.  Predicting subcellular localization via protein motif co-occurrence.

Authors:  Michelle S Scott; David Y Thomas; Michael T Hallett
Journal:  Genome Res       Date:  2004-10       Impact factor: 9.043

7.  Darkness in the Human Gene and Protein Function Space: Widely Modest or Absent Illumination by the Life Science Literature and the Trend for Fewer Protein Function Discoveries Since 2000.

Authors:  Swati Sinha; Birgit Eisenhaber; Lars Juhl Jensen; Bharata Kalbuaji; Frank Eisenhaber
Journal:  Proteomics       Date:  2018-10-30       Impact factor: 3.984

8.  Amplification of the Gene Ontology annotation of Affymetrix probe sets.

Authors:  Enrique M Muro; Carolina Perez-Iratxeta; Miguel A Andrade-Navarro
Journal:  BMC Bioinformatics       Date:  2006-03-20       Impact factor: 3.169

9.  Can inferred provenance and its visualisation be used to detect erroneous annotation? A case study using UniProtKB.

Authors:  Michael J Bell; Matthew Collison; Phillip Lord
Journal:  PLoS One       Date:  2013-10-15       Impact factor: 3.240

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.