CMV: Visualization for RNA and protein family models and their comparisons

5Citations
Citations of this article
16Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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.

Cite

CITATION STYLE

APA

Eggenhofer, F., Hofacker, I. L., Backofen, R., & Siederdissen, C. H. (2018). CMV: Visualization for RNA and protein family models and their comparisons. Bioinformatics, 34(15), 2676–2678. https://doi.org/10.1093/bioinformatics/bty158

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free