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MISTIC:互信息服务器推断共进化。

MISTIC: Mutual information server to infer coevolution.

机构信息

Bioinformatics Unit, Fundación Instituto Leloir, Av. Patricias Argentinas 435, C1405BWE, Buenos Aires, Argentina.

出版信息

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W8-14. doi: 10.1093/nar/gkt427. Epub 2013 May 28.

Abstract

MISTIC (mutual information server to infer coevolution) is a web server for graphical representation of the information contained within a MSA (multiple sequence alignment) and a complete analysis tool for Mutual Information networks in protein families. The server outputs a graphical visualization of several information-related quantities using a circos representation. This provides an integrated view of the MSA in terms of (i) the mutual information (MI) between residue pairs, (ii) sequence conservation and (iii) the residue cumulative and proximity MI scores. Further, an interactive interface to explore and characterize the MI network is provided. Several tools are offered for selecting subsets of nodes from the network for visualization. Node coloring can be set to match different attributes, such as conservation, cumulative MI, proximity MI and secondary structure. Finally, a zip file containing all results can be downloaded. The server is available at http://mistic.leloir.org.ar. In summary, MISTIC allows for a comprehensive, compact, visually rich view of the information contained within an MSA in a manner unique to any other publicly available web server. In particular, the use of circos representation of MI networks and the visualization of the cumulative MI and proximity MI concepts is novel.

摘要

MISTIC(互信息服务器推断共进化)是一个用于图形化展示多序列比对(MSA)中所包含信息的网络服务器,也是蛋白质家族中互信息网络的完整分析工具。该服务器使用 circos 表示法输出几个与信息相关的数量的图形可视化。这提供了 MSA 的综合视图,包括(i)残基对之间的互信息(MI),(ii)序列保守性和(iii)残基累积和邻近 MI 得分。此外,还提供了一个交互式界面来探索和描述 MI 网络。提供了几种工具来从网络中选择节点子集进行可视化。节点颜色可以设置为匹配不同的属性,如保守性、累积 MI、邻近 MI 和二级结构。最后,可以下载包含所有结果的 zip 文件。该服务器可在 http://mistic.leloir.org.ar 访问。总之,MISTIC 以任何其他公开可用的网络服务器所没有的方式,提供了对 MSA 中所包含信息的全面、紧凑、视觉丰富的视图。特别是,使用 MI 网络的 circos 表示法和累积 MI 和邻近 MI 概念的可视化是新颖的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec1c/3692073/4fbda42c59b5/gkt427f1p.jpg

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