A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.
BACKGROUND
Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.


RESULTS
In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.


CONCLUSIONS
We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.
Peña-Castillo
Lourdes
L
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S3E1, Canada.
Tasan
Murat
M
Myers
Chad L
CL
Lee
Hyunju
H
Joshi
Trupti
T
Zhang
Chao
C
Guan
Yuanfang
Y
Leone
Michele
M
Pagnani
Andrea
A
Kim
Wan Kyu
WK
Krumpelman
Chase
C
Tian
Weidong
W
Obozinski
Guillaume
G
Qi
Yanjun
Y
Mostafavi
Sara
S
Lin
Guan Ning
GN
Berriz
Gabriel F
GF
Gibbons
Francis D
FD
Lanckriet
Gert
G
Qiu
Jian
J
Grant
Charles
C
Barutcuoglu
Zafer
Z
Hill
David P
DP
Warde-Farley
David
D
Grouios
Chris
C
Ray
Debajyoti
D
Blake
Judith A
JA
Deng
Minghua
M
Jordan
Michael I
MI
Noble
William S
WS
Morris
Quaid
Q
Klein-Seetharaman
Judith
J
Bar-Joseph
Ziv
Z
Chen
Ting
T
Sun
Fengzhu
F
Troyanskaya
Olga G
OG
Marcotte
Edward M
EM
Xu
Dong
D
Hughes
Timothy R
TR
Roth
Frederick P
FP
D000465:Algorithms; D000818:Animals; D051379:Mice / Q000235:genetics* / Q000378:metabolism; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*
D011506:Proteins