Authors:
Fábio Madeira
and
Ludwig Krippahl
Affiliation:
Universidade Nova de Lisboa, Portugal
Keyword(s):
Protein coevolution, Multiple sequence alignments, Mutual information.
Related
Ontology
Subjects/Areas/Topics:
Algorithms and Software Tools
;
Bioinformatics
;
Biomedical Engineering
;
Data Mining and Machine Learning
;
Sequence Analysis
Abstract:
Protein coevolution has emerged as an important research topic. Several methods and scoring systems were developed to quantify coevolution, though the quality of the results usually depends on the completeness of the biological data. To simplify the computation of coevolution indicators from the data, we have implemented a fully integrated and automated workflow which enables efficient analysis of protein coevolution, using the Python scripting language. Pycoevol automates access to remote or local databases and third-party applications, including also data processing functions. For a given protein complex under study, Pycoevol retrieves and processes all the information needed to undergo the analysis, namely homologous sequence search, multiple sequence alignment computation and coevolution analysis, using a Mutual Information indicator. In addition, friendly output results are created, namely histograms and heatmaps of inter-protein mutual information scores, as well as lists of si
gnificant coevolving residue pairs. An illustrative example is presented. Pycoevol is platform independent, and is available under the general public license from http://code.google.com/p/pycoevol.
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