1Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany2Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria3Bioinformatics and Computational Biology Research Group, University of Vienna, Vienna, Austria4Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany5Bioinformatics Group, Department of Computer Science, University of Leipzig, D-04107 Leipzig, Germany6Interdisciplinary Center for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany
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Summary:A standard method for the identification of novel RNAs or proteins is homology search via probabilistic models. One approach relies on the definition of families, which can be encoded as covariance models (CMs) or Hidden Markov Models (HMMs). While being powerful tools, their complexity makes it tedious to investigate them in their (default) tabulated form. This specifically applies to the interpretation of comparisons between multiple models as in family clans. The Covariance model visualization tools (CMV) visualize CMs or HMMs to: I) Obtain an easily interpretable representation of HMMs and CMs; II) Put them in context with the structural sequence alignments they have been created from; III) Investigate results of model comparisons and highlight regions of interest.Availability and implementation:Source code (http://www.github.com/eggzilla/cmv), web-service (http://rna.informatik.uni-freiburg.de/CMVS).Supplementary information:Supplementary data are available at Bioinformatics online.