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Author, Editor

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)

Note:


(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

Categories / Keywords:


HyperLinks / References / URLs:


Copyright Message:


Personal Comments:


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
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Editor(s)
[Library]
Created
12/19/2012 11:27:49 AM
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