Improved Multiple Sequence Alignments using Coupled Pattern Mining

We present ARMiCoRe, a novel approach to a classical bioinformatics problem, viz. multiple sequence alignment (MSA) of gene and protein sequences. Aligning multiple biological sequences is a key step in elucidating evolutionary relationships, annotating newly sequenced segments, and understanding the relationship between biological sequences and functions. Classical MSA algorithms are designed to primarily capture conservations in sequences whereas couplings, or correlated mutations, are well known as an additional important aspect of sequence evolution. (Two sequence positions are coupled when mutations in one are accompanied by compensatory mutations in another). As a result, it is not uncommon for practitioners to hand-tweak a conservation-based alignment to better expose couplings. ARMiCoRe introduces a distinctly pattern mining approach to improving MSAs: using frequent episode mining as a foundational basis, we define the notion of a coupled pattern and demonstrate how the discovery and tiling of coupled patterns using a max-flow approach can yield MSAs that are significantly better than conservation-based alignments. Although we were motivated to improve MSAs for the sake of better exposing couplings, we demonstrate that our MSAs are also improvements in terms of traditional metrics of assessment. We demonstrate the effectiveness of ARMiCoRe on a large collection of datasets.

In  Proceedings of the Third ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB 2012)

Publisher  ACM
[Invited to ACM/IEEE TCBB, Transactions in Computational Biology & Bioinformatics, under Best Papers of ACM-BCB 2012]

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