Identification of transcription factor binding sites with variable-order Bayesian networks.
BACKGROUND
We propose a new class of variable-order Bayesian network (VOBN) models for the identification of transcription factor binding sites (TFBSs). The proposed models generalize the widely used position weight matrix (PWM) models, Markov models and Bayesian network models. In contrast to these models, where for each position a fixed subset of the remaining positions is used to model dependencies, in VOBN models, these subsets may vary based on the specific nucleotides observed, which are called the context. This flexibility turns out to be of advantage for the classification and analysis of TFBSs, as statistical dependencies between nucleotides in different TFBS positions (not necessarily adjacent) may be taken into account efficiently--in a position-specific and context-specific manner.


RESULTS
We apply the VOBN model to a set of 238 experimentally verified sigma-70 binding sites in Escherichia coli. We find that the VOBN model can distinguish these 238 sites from a set of 472 intergenic 'non-promoter' sequences with a higher accuracy than fixed-order Markov models or Bayesian trees. We use a replicated stratified-holdout experiment having a fixed true-negative rate of 99.9%. We find that for a foreground inhomogeneous VOBN model of order 1 and a background homogeneous variable-order Markov (VOM) model of order 5, the obtained mean true-positive (TP) rate is 47.56%. In comparison, the best TP rate for the conventional models is 44.39%, obtained from a foreground PWM model and a background 2nd-order Markov model. As the standard deviation of the estimated TP rate is approximately 0.01%, this improvement is highly significant.
Ben-Gal
I
I
Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel. bengal@eng.tau.ac.il
Shani
A
A
Gohr
A
A
Grau
J
J
Arviv
S
S
Shmilovici
A
A
Posch
S
S
Grosse
I
I
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001665:Binding Sites; D003198:Computer Simulation; D030562:Databases, Protein; D008390:Markov Chains; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D011485:Protein Binding; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship; D014157:Transcription Factors / Q000032:analysis / Q000737:chemistry* / Q000145:classification
D014157:Transcription Factors