DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.36
K. S. M. Tozammel Hossain , Virginia Tech, Blacksburg
Debprakash Patnaik , Amazon Inc., Seattle
Srivatsan Laxman , Microsoft Research, Bangalore
Prateek Jain , Microsoft Research, Bangalore
Chris Bailey-Kellogg , Dartmouth College, Hanover
Naren Ramakrishnan , Virginia Tech, Blacksburg
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.
Max-flow problems, Multiple sequence alignment, Coupled residues, Pattern set mining, Coupled pattern
K. S. Hossain, D. Patnaik, S. Laxman, P. Jain, C. Bailey-Kellogg and N. Ramakrishnan, "Improved Multiple Sequence Alignments Using Coupled Pattern Mining," in IEEE/ACM Transactions on Computational Biology and Bioinformatics.