Conrad: gene prediction using conditional random fields.
We present Conrad, the first comparative gene predictor based on semi-Markov conditional random fields (SMCRFs). Unlike the best standalone gene predictors, which are based on generalized hidden Markov models (GHMMs) and trained by maximum likelihood, Conrad is discriminatively trained to maximize annotation accuracy. In addition, unlike the best annotation pipelines, which rely on heuristic and ad hoc decision rules to combine standalone gene predictors with additional information such as ESTs and protein homology, Conrad encodes all sources of information as features and treats all features equally in the training and inference algorithms. Conrad outperforms the best standalone gene predictors in cross-validation and whole chromosome testing on two fungi with vastly different gene structures. The performance improvement arises from the SMCRF's discriminative training methods and their ability to easily incorporate diverse types of information by encoding them as feature functions. On Cryptococcus neoformans, configuring Conrad to reproduce the predictions of a two-species phylo-GHMM closely matches the performance of Twinscan. Enabling discriminative training increases performance, and adding new feature functions further increases performance, achieving a level of accuracy that is unprecedented for this organism. Similar results are obtained on Aspergillus nidulans comparing Conrad versus Fgenesh. SMCRFs are a promising framework for gene prediction because of their highly modular nature, simplifying the process of designing and testing potential indicators of gene structure. Conrad's implementation of SMCRFs advances the state of the art in gene prediction in fungi and provides a robust platform for both current application and future research.
DeCaprio
David
D
The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA. daved@broad.mit.edu
Vinson
Jade P
JP
Pearson
Matthew D
MD
Montgomery
Philip
P
Doherty
Matthew
M
Galagan
James E
JE
D000465:Algorithms; D001185:Artificial Intelligence; D001233:Aspergillus nidulans / Q000235:genetics*; D015825:Chromosomes, Fungal; D003455:Cryptococcus neoformans / Q000235:genetics*; D016002:Discriminant Analysis; D005800:Genes, Fungal; D016013:Likelihood Functions; D008390:Markov Chains; D012015:Reference Standards; D012984:Software
Genome Biol. 2007;8(1):R13
17241472
Genome Biol. 2006;7 Suppl 1:S4.1-9
16925838
J Comput Biol. 2004;11(2-3):413-28
15285899
PLoS Comput Biol. 2007 Mar 16;3(3):e54
17367206
Proc Int Conf Intell Syst Mol Biol. 1994;2:369-75
7584414
Genomics. 1996 Jun 15;34(3):353-67
8786136
J Mol Biol. 1997 Apr 25;268(1):78-94
9149143
Genome Res. 2004 Nov;14(11):2330-5
15479946
Science. 2005 Feb 25;307(5713):1321-4
15653466
Genome Res. 2005 May;15(5):742-7
15867435
Bioinformatics. 2005 Jun;21 Suppl 1:i57-65
15961499
Bioinformatics. 2005 Sep 15;21(18):3596-603
16076884
J Comput Biol. 2006 Mar;13(2):379-93
16597247
BMC Bioinformatics. 2006;7:327
16817966
Genome Res. 2000 Apr;10(4):511-5
10779490
Genome Res. 2000 Apr;10(4):516-22
10779491
Genome Res. 2001 May;11(5):803-16
11337476
Bioinformatics. 2001;17 Suppl 1:S140-8
11473003
Genome Res. 2002 Sep;12(9):1418-27
12213779
Bioinformatics. 2003 Jan 22;19(2):219-27
12538242
Nature. 2003 May 15;423(6937):241-54
12748633
Genome Res. 2004 Jan;14(1):142-8
14707176
Genome Res. 2004 Apr;14(4):708-15
15060014
BMC Bioinformatics. 2004 May 14;5:59
15144565
Genome Biol. 2006;7 Suppl 1:S11.1-8
16925833
Genome Biol. 2006;7 Suppl 1:S2.1-31
16925836
Bioinformatics. 2004 Aug 12;20(12):1850-60
14988105