MORPH: Probabilistic Alignment Combined with Hidden Markov Models of cis-Regulatory Modules

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Abstract
The discovery and analysis of cis-regulatory modules (CRMs) in metazoan genomes is crucial for understanding the transcriptional control of development and many other biological processes. Cross-species sequence comparison holds much promise for improving computational prediction of CRMs, for elucidating their binding site composition, and for understanding how they evolve. Current methods for analyzing orthologous CRMs from multiple species rely upon sequence alignments produced by off-the-shelf alignment algorithms, which do not exploit the presence of binding sites in the sequences. We present here a unified probabilistic framework, called MORPH, that integrates the alignment task with binding site predictions, allowing more robust CRM analysis in two species. The framework sums over all possible alignments of two sequences, thus accounting for alignment ambiguities in a natural way. We perform extensive tests on orthologous CRMs from two moderately diverged species Drosophila melanogaster and D. mojavensis, to demonstrate the advantages of the new approach. We show that it can overcome certain computational artifacts of traditional alignment tools and provide a different, likely more accurate, picture of cis-regulatory evolution than that obtained from existing methods. The burgeoning field of cis-regulatory evolution, which is amply supported by the availability of many related genomes, is currently thwarted by the lack of accurate alignments of regulatory regions. Our work will fill in this void and enable more reliable analysis of CRM evolution. Interspecies comparison of regulatory sequences is a major focus in the bioinformatics community today. There is extensive ongoing effort toward measuring the extent and patterns of binding site turnover in cis-regulatory modules. A major roadblock in such an analysis has been the fact that traditional alignment methods are not very accurate for regulatory sequences. This is partly because the alignment is performed independently from the binding site predictions and turnover analysis. This article describes a new computational method to compare and align two orthologous regulatory sequences. It uses a unified probabilistic framework to perform alignment and binding site prediction simultaneously, rather than one after the other. Predictions of binding sites and their evolutionary relationships are obtained after summing over all possible alignments, making them robust to alignment ambiguities. The method can also be used to predict new cis-regulatory modules. The article presents extensive applications of the method on synthetic as well as real data. These include the analysis of over 200 cis-regulatory modules in D. melanogaster and their orthologs in D. mojavensis. This analysis reveals a significantly greater degree of conservation of binding sites between these two species than will be inferred from existing alignment tools.