@article{Eggenhofer_Hofacker_Backofen-CMV_Visua_for-2018,
author = {Eggenhofer, Florian and Hofacker, Ivo L. and Backofen, Rolf 
          and Honer Zu Siederdissen, Christian},
title = {{CMV} - {Visualization} for {RNA} and {Protein} family 
         models and their comparisons},
journal = {Bioinformatics},
year = {2018},
doi = {10.1093/bioinformatics/bty158},
volume = {},
user = {backofen},
pmid = {29554223},
pages = {},
number = {},
issn = {1367-4811},
abstract = {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: Source code 
            (http://www.github.com/eggzilla/cmv), web-service 
            (http://rna.informatik.uni-freiburg.de/CMVS). Contact: 
            egg@informatik.uni-freiburg.de, 
            choener@bioinf.uni-leipzig.de. Supplementary information: 
            Supplementary data available online.}
}