Computation of mutual information from Hidden Markov Models

Reker, Daniel; Katzenbeisser, Stefan; Hamacher, Kay

doi:10.1016/j.compbiolchem.2010.08.005

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The overall magnitude or scale of the signal one can observe in the mutual information values of Equations and is largely determined by the single‐column entropies H i , H j , and H k , respectively. To normalize to a shared reference scale for all columns in an MSA regardless of the single column entropies H i , a null model of neutral evolution was suggested . Here, we shuffle the columns i , j (and k ) independently n iter times, recompute the

{\overset{MI}{false}}_{ij}

{\overset{MI}{false}}_{ijk}

after each shuffling, and derive an empirical distribution for the

{\overset{MI}{false}}_{ij}

{\overset{MI}{false}}_{ijk}

values of the shuffled MSAs.…”

Section: Central Conceptsmentioning

confidence: 99%

“…Large data sets allow the efficient analysis of (co)evolutionary signatures in biomolecules , in particular. Such signatures are frequently detected on the basis of the mutual information (MI) from multiple sequence alignments (MSA) of protein or nucleotide sequences . The MI directly reflects the information one position in an MSA contains about another position.…”

Section: Introductionmentioning

confidence: 99%

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Using graphics processing units to investigate molecular coevolution

Waechter

Jaeger

Thuerck

et al. 2013

Concurrency and Computation

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SUMMARYWe present a massively parallel implementation of the computation of (co)evolutionary signals from biomolecular sequence alignments based on mutual information (MI) and a normalization procedure to neutral evolution. The MI is computed for two-point and three-point correlations within any multiple sequence alignment. We meet the high computational demand in the normalization procedure efficiently with an implementation on Graphics Processing Units (GPUs) using NVIDIA's CUDA framework. In particular, the normalization of the MI for three-point 'cliques' of amino acids or nucleotides requires large sampling numbers in the normalization, which we achieve by using GPUs. GPU computation serves as an enabling technology here insofar as MI normalization is also possible using traditional computational methods [1] or cluster computation, but only GPU computation makes MI normalization for sequence analysis feasible in a statistically sufficient sample and in acceptable time given affordable commodity hardware. We illustrate (i) the computational efficiency and (ii) the biological usefulness of two-point and three-point MI by applications to the well-known protein calmodulin and the variable surface glycoprotein (VSG) of Trypanosoma brucei, which are subject to involved evolutionary pressure. Here, we find striking coevolutionary patterns and distinct information on the molecular evolution of these molecules that question previous work that relied on non-efficient MI computations.

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