MOTIVATION: Identifying transcription factor binding sites (TFBSs) encoding 
complex regulatory signals in metazoan genomes remains a challenging problem in 
computational genomics. Due to degeneracy of nucleotide content among binding 
site instances or motifs, and intricate 'grammatical organization' of motifs 
within cis-regulatory modules (CRMs), extant pattern matching-based in silico 
motif search methods often suffer from impractically high false positive rates, 
especially in the context of analyzing large genomic datasets, and noisy 
position weight matrices which characterize binding sites. Here, we try to 
address this problem by using a framework to maximally utilize the information 
content of the genomic DNA in the region of query, taking cues from values of 
various biologically meaningful genetic and epigenetic factors in the query 
region such as clade-specific evolutionary parameters, presence/absence of 
nearby coding regions, etc. We present a new method for TFBS prediction in 
metazoan genomes that utilizes both the CRM architecture of sequences and a 
variety of features of individual motifs. Our proposed approach is based on a 
discriminative probabilistic model known as conditional random fields that 
explicitly optimizes the predictive probability of motif presence in large 
sequences, based on the joint effect of all such features.
RESULTS: This model overcomes weaknesses in earlier methods based on less 
effective statistical formalisms that are sensitive to spurious signals in the 
data. We evaluate our method on both simulated CRMs and real Drosophila 
sequences in comparison with a wide spectrum of existing models, and outperform 
the state of the art by 22% in F1 score.
AVAILABILITY AND IMPLEMENTATION: The code is publicly available at 
http://www.sailing.cs.cmu.edu/discover.html.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.