@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},
  volume =	 {},
  number =	 {},
  pages =	 {},
  user =	 {backofen},
  pmid =	 {29554223},
  doi = 	 {10.1093/bioinformatics/bty158},
  issn = 	 {1367-4811},
  issn = 	 {1367-4803},
  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.}
}