An approach to describing and analysing bulk biological annotation quality: A case study using UniProtKB

Bioinformatics

Volumn 28, Issue 18, 2012, Pages

  Bell, Michael J ^a   Gillespie, Colin S ^a   Swan, Daniel ^a,b   Lord, Phillip ^a  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

^b OXFORD GENE TECHNOLOGY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN;

ARTICLE; CHEMISTRY; KNOWLEDGE BASE; MOLECULAR GENETICS; PHYSIOLOGY; PROTEIN DATABASE; STANDARD;

DATABASES, PROTEIN; KNOWLEDGE BASES; MOLECULAR SEQUENCE ANNOTATION; PROTEINS;

EID: 84866463515     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts372     Document Type: Article

References (38)

1. Adamic,L.A. and Huberman,B.A. (2002) Zipf's law and the internet. Glottometrics, 3, 143-150.
2. Andorf,C. et al. (2007) Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach. BMC Bioinformatics, 8, 284+.
3. Bairoch,A. and Apweiler,R. (1998) The SWISS-PROT protein sequence data bank and its supplement TrEMBL in 1998. Nucleic Acids Res., 26, 38-42.
4. Bairoch,A. and Apweiler,R. (2000) The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res., 28, 45-48.
5. Balasubrahmanyan,V.K. and Naranan, S. (1996) Quantitative linguistics and complex system studies. J. Quant. Linguisti., 3, 177-228.
6. Baumgartner,W.A. et al. (2007) Manual curation is not sufficient for annotation of genomic databases. Bioinformatics, 23, i41-148.
7. Boeckmann,B. et al. (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res., 31, 365-370.
8. Brillouin,L. et al. (2004) Science and Information Theory. 2nd Edition (Dover Phoenix Editions). Dover Publications.
9. Buza,T.J. et al. (2008) Gene ontology annotation quality analysis in model eukaryotes. Nucleic Acids Research, 36, e12.
10. Camon,E.B. et al. (2005) An evaluation of GO annotation retrieval for BioCreAtIvE and GOA. BMC Bioinformatics, 6 (Suppl. 1), S17.
11. Cancho,R.F. and Solé,R.V. (2001) Two regimes in the frequency of words and the origins of complex lexicons: Zipf's law revisited. J. Quant. Linguist., 8, 165-173.
12. Clauset,A. et al. (2009) Power-law distributions in empirical data. SIAM Rev., 51, 661+.
13. Curwen,V. et al. (2004) The Ensembl automatic gene annotation system. Genome Res., 14, 942-950.
14. Dolan,M.E. et al. (2005) A procedure for assessing GO annotation consistency. Bioinformatics, 21 (Suppl. 1), i136-i143.
15. Ferrer,R. (2005) The variation of Zipf's law in human language. Eur. Phys. J. B, 44, 249-257.
16. Ferrericancho,R. (2005) Decoding least effort and scaling in signal frequency distributions. Physica A, Stat. Mech. Appl., 345, 275-284.
17. Flesch,R. (1948) A new readability yardstick. J. Appl. Psychol., 32, 221-233.
18. Gilks,W.R. et al. (2002) Modeling the percolation of annotation errors in a database of protein sequences. Bioinformatics, 18, 1641-1649.
19. GO Consortium (2011) Guide to GO evidence codes.
20. Gross,A. et al. (2009) Estimating the quality of ontology-based annotations by considering evolutionary changes. In DILS '09: Proceedings of the 6th International Workshop on Data Integration in the Life Sciences. Springer-Verlag, Berlin, Heidelberg, pp. 71-87.
21. Ha,L.Q. et al. (2006) Zipf and type-token rules for the English, Spanish, Irish and Latin languages. Web J. Formal Comput. Congni. Linguist., 8, 1-12.
22. James,K. et al. (2011) Multiple gold standards address bias in functional network integration. Technical Report 1302. University of Newcastle Upon Tyne.
23. Jones,C. et al. (2007) Estimating the annotation error rate of curated GO database sequence annotations. BMC Bioinformatics, 8, 170+.
24. Kalankesh,L. et al. (2012) The language of gene ontology: a zipf's law analysis. BMC Bioinformatics, 13, 127+.
25. Laughlin,H.M. (1969) SMOG grading-a new readability formula. J. Reading, 12, 639-46.
26. Lord,P.W. et al. (2003) Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics, 19, 1275-1283.
27. Magrane,M. and Uniprot Consortium (2011) UniProt knowledgebase: a hub of integrated protein data. Database, 2011, bar009.
28. Pal,D. and Eisenberg,D. (2005) Inference of protein function from protein structure. Structure, 13, 121-130.
29. Piotrowska,W. and Piotrowska,X. (2004) Statistical parameters in pathological text. J. Quant. Linguist., 11, 133-140.
30. Roberts,R.J. et al. (2011) COMBREX: a project to accelerate the functional annotation of prokaryotic genomes. Nucleic Acids Res., 39 (Suppl. 1), D11-D14.
31. Rogers,M.F. and Ben-Hur,A. (2009) The use of Gene Ontology evidence codes in preventing classifier assessment bias. Bioinformatics, 25, 1173-1177.
32. Schnoes,A.M. et al. (2009) Annotation error in public databases: misannotation of molecular function in enzyme superfamilies. PLoS Comput. Biol., 5, e1000605+.
33. Serrano,M.A. et al. (2009) Modeling statistical properties of written text. PLoS ONE, 4, e5372+.
34. Stevens,R. and Lord,P. (2009) Application of ontologies in bioinformatics. In Staab,S. and Studer,R. (eds) Handbook on Ontologies, International Handbooks Information System, Springer, Berlin, Heidelberg, pp. 735-756.
35. UniProt Consortium (2010) The universal protein resource (uniprot) in 2010. Nucleic Acids Res., 38 (Suppl. 1), D142-D148.
36. UniProt Consortium (2011) UniProt knowledgebase user manual. http://www.geneontology.org/GO.evidence.shtml
37. Ussery,D.W. and Hallin,P.F. (2004) Genome update: annotation quality in sequenced microbial genomes. Microbiology, 150 (Pt 7), 2015-2017.
38. Zipf,G.K. (1949) Human Behaviour and the Principle of Least Effort. Hafner Publishing Co Ltd, new issue of 1949 edn.