ML-Consensus: A General Consensus Model for Variable-Length Transcription Factor Binding Sites

Abstract

Many DNA motif finding algorithms that use Consensus (or any of its variants) in its motif model implicitly impose some restrictive assumptions over transcription factor (TF) binding sites (TFBS). Examples include all binding sites being of equal length, or having exactly one core region with fixed format, etc. In this paper, we have constructed a generalized consensus model (called Mixed-Length-Consensus, or ML-Consensus) without such constraints through multiple sequence alignment of known TFBS. We have extended this model with Information Content (IC) and Pairwise nucleotide correlation Score (PS), and have experimented with using multiple ML-Consensus for a set of binding sites. We have performed leave-one-out cross validation for training and testing of this algorithm over real binding site data of human, mouse, fruit fly, and yeast. We have produced ROC curves (True Positive Rate against False Positive Rate) for these experiments, and have used Wilcoxon Matched-Pair Signed Ranks Test to determine their statistical significance. Our results show that using IC and PS together with ML-Consensus consistently leads to better performance. We have experimented with various scopes for PS, and have found that scope values of 3-5 yield significantly better performance for different configurations. We have also found that using multiple ML-Consensus for one TF significantly improves recognition performance, but single ML-Consensus does better in yeast than in human data. Finally, we have found that using different multiple sequence alignment strategies for ML-Consensus yields varied performance across different species; a naive sorting based multiple sequence alignment outperformed CLUSTAL W2 alignment in yeast data.