Computing and visually analyzing mutual information in molecular co-evolution

<p>Abstract</p> <p>Background</p> <p>Selective pressure in molecular evolution leads to uneven distributions of amino acids and nucleotides. In fact one observes correlations among such constituents due to a large number of biophysical mechanisms (folding properties, el...

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Main Authors: Held Stephanie, Boba Patrick, Schreck Tobias, Bremm Sebastian, Hamacher Kay
Format: Article
Language:English
Published: BMC 2010-06-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/11/330
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spelling doaj-a71b2dfe60a44f169b1bca3b713cf2062020-11-24T22:06:24ZengBMCBMC Bioinformatics1471-21052010-06-0111133010.1186/1471-2105-11-330Computing and visually analyzing mutual information in molecular co-evolutionHeld StephanieBoba PatrickSchreck TobiasBremm SebastianHamacher Kay<p>Abstract</p> <p>Background</p> <p>Selective pressure in molecular evolution leads to uneven distributions of amino acids and nucleotides. In fact one observes correlations among such constituents due to a large number of biophysical mechanisms (folding properties, electrostatics, ...). To quantify these correlations the mutual information -after proper normalization - has proven most effective. The challenge is to navigate the large amount of data, which in a study for a typical protein cannot simply be plotted.</p> <p>Results</p> <p>To visually analyze mutual information we developed a matrix visualization tool that allows different views on the mutual information matrix: filtering, sorting, and weighting are among them. The user can interactively navigate a huge matrix in real-time and search e.g., for patterns and unusual high or low values. A computation of the mutual information matrix for a sequence alignment in FASTA-format is possible. The respective stand-alone program computes in addition proper normalizations for a null model of neutral evolution and maps the mutual information to <it>Z</it>-scores with respect to the null model.</p> <p>Conclusions</p> <p>The new tool allows to compute and visually analyze sequence data for possible co-evolutionary signals. The tool has already been successfully employed in evolutionary studies on HIV1 protease and acetylcholinesterase. The functionality of the tool was defined by users using the tool in real-world research. The software can also be used for visual analysis of other matrix-like data, such as information obtained by DNA microarray experiments. The package is platform-independently implemented in <monospace>Java</monospace> and free for academic use under a GPL license.</p> http://www.biomedcentral.com/1471-2105/11/330
collection DOAJ
language English
format Article
sources DOAJ
author Held Stephanie
Boba Patrick
Schreck Tobias
Bremm Sebastian
Hamacher Kay
spellingShingle Held Stephanie
Boba Patrick
Schreck Tobias
Bremm Sebastian
Hamacher Kay
Computing and visually analyzing mutual information in molecular co-evolution
BMC Bioinformatics
author_facet Held Stephanie
Boba Patrick
Schreck Tobias
Bremm Sebastian
Hamacher Kay
author_sort Held Stephanie
title Computing and visually analyzing mutual information in molecular co-evolution
title_short Computing and visually analyzing mutual information in molecular co-evolution
title_full Computing and visually analyzing mutual information in molecular co-evolution
title_fullStr Computing and visually analyzing mutual information in molecular co-evolution
title_full_unstemmed Computing and visually analyzing mutual information in molecular co-evolution
title_sort computing and visually analyzing mutual information in molecular co-evolution
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2010-06-01
description <p>Abstract</p> <p>Background</p> <p>Selective pressure in molecular evolution leads to uneven distributions of amino acids and nucleotides. In fact one observes correlations among such constituents due to a large number of biophysical mechanisms (folding properties, electrostatics, ...). To quantify these correlations the mutual information -after proper normalization - has proven most effective. The challenge is to navigate the large amount of data, which in a study for a typical protein cannot simply be plotted.</p> <p>Results</p> <p>To visually analyze mutual information we developed a matrix visualization tool that allows different views on the mutual information matrix: filtering, sorting, and weighting are among them. The user can interactively navigate a huge matrix in real-time and search e.g., for patterns and unusual high or low values. A computation of the mutual information matrix for a sequence alignment in FASTA-format is possible. The respective stand-alone program computes in addition proper normalizations for a null model of neutral evolution and maps the mutual information to <it>Z</it>-scores with respect to the null model.</p> <p>Conclusions</p> <p>The new tool allows to compute and visually analyze sequence data for possible co-evolutionary signals. The tool has already been successfully employed in evolutionary studies on HIV1 protease and acetylcholinesterase. The functionality of the tool was defined by users using the tool in real-world research. The software can also be used for visual analysis of other matrix-like data, such as information obtained by DNA microarray experiments. The package is platform-independently implemented in <monospace>Java</monospace> and free for academic use under a GPL license.</p>
url http://www.biomedcentral.com/1471-2105/11/330
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