MPI-INF D3 Publications: Journal Article: Mining {GO} annotations for improving annotation consistency

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Author(s):

Faria, Daniel
Schlicker, Andreas
Pesquita, Catia
Bastos, Hugo
Ferreira, António E N
Albrecht, Mario
Falcão, André O

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Not MPG Author(s):

Faria, Daniel
Pesquita, Catia
Bastos, Hugo
Ferreira, António E N
Falcão, André O

BibTeX cite key*:

Albrecht2012d

Title

Title*:

Mining GO annotations for improving annotation consistency

Journal

Journal Title*:	PLoS ONE	Journal's URL:	http://www.plosone.org/
Download URL for the article:	http://dx.doi.org/10.1371/journal.pone.0040519	Language:	English

Publisher

Publisher's Name:	Public Library of Science	Publisher's URL:
Publisher's Address:	San Francisco, CA	ISSN:	1932-6203

Vol, No, Year, pp.

Volume:	7	Number:	7
Month:		Year*:	2012
Pages:	e40519,1-e40519,7	Number of VG Pages:
Sequence Number:	e40519	DOI:	10.1371/journal.pone.0040519

Abstract, Links, (C)

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(LaTeX) Abstract:	Despite the structure and objectivity provided by the Gene Ontology (GO), the annotation of proteins is a complex task that is subject to errors and inconsistencies. Electronically inferred annotations in particular are widely considered unreliable. However, given that manual curation of all GO annotations is unfeasible, it is imperative to improve the quality of electronically inferred annotations. In this work, we analyze the full GO molecular function annotation of UniProtKB proteins, and discuss some of the issues that affect their quality, focusing particularly on the lack of annotation consistency. Based on our analysis, we estimate that 64% of the UniProtKB proteins are incompletely annotated, and that inconsistent annotations affect 83% of the protein functions and at least 23% of the proteins. Additionally, we present and evaluate a data mining algorithm, based on the association rule learning methodology, for identifying implicit relationships between molecular function terms. The goal of this algorithm is to assist GO curators in updating GO and correcting and preventing inconsistent annotations. Our algorithm predicted 501 relationships with an estimated precision of 94%, whereas the basic association rule learning methodology predicted 12,352 relationships with a precision below 9%.
URL for the Abstract:	http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0040519#abstract0
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Download Access Level:	Internal

Correlation

MPG Unit:	Max-Planck-Institut für Informatik

MPG Subunit:	Computational Biology and Applied Algorithmics
Appearance:	MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort

BibTeX Entry:

@MISC{Albrecht2012d,
AUTHOR = {Faria, Daniel and Schlicker, Andreas and Pesquita, Catia and Bastos, Hugo and Ferreira, Ant{\"o}nio E N and Albrecht, Mario and Falcão, Andr{\'e} O},
TITLE = {Mining {GO} annotations for improving annotation consistency},
JOURNAL = {PLoS ONE},
PUBLISHER = {Public Library of Science},
YEAR = {2012},
NUMBER = {7},
VOLUME = {7},
PAGES = {e40519,1--e40519,7},
ADDRESS = {San Francisco, CA},
ISBN = {1932-6203},
DOI = {10.1371/journal.pone.0040519},
}

Entry last modified by Anja Becker, 02/12/2013

Edit History (please click the blue arrow to see the details)

	Editor(s) [Library]	Created 12/19/2012 11:27:49
Revisions 3. 2. 1. 0.	Editor(s) Anja Becker Anja Becker Mario Albrecht Nadezhda Tsankova Doncheva	Edit Dates 12.02.2013 15:40:32 12.02.2013 15:39:56 01/05/2013 01:15:21 AM 12/19/2012 11:27:49 AM