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使用CLANS分析微阵列数据。

Analyzing microarray data using CLANS.

作者信息

Frickey Tancred, Weiller Georg

机构信息

ARC Centre of Excellence for Interactive Legume Research and Bioinformatics Laboratory, Genomic Interactions Group, Research School of Biological Sciences, Australian National University, Canberra, ACT, Australia

出版信息

Bioinformatics. 2007 May 1;23(9):1170-1. doi: 10.1093/bioinformatics/btm079. Epub 2007 Mar 7.

Abstract

UNLABELLED

Analysis of microarray experiments is complicated by the huge amount of data involved. Searching for groups of co-expressed genes is akin to searching for protein families in a database as, in both cases, small subsets of genes with similar features are to be found within vast quantities of data. CLANS was originally developed to find protein families in large sets of amino acid sequences where the amount of data involved made phylogenetic approaches overly cumbersome. We present a number of improvements that greatly extend the previous version of CLANS and show its application to microarray data as well as its ability of incorporating additional information to facilitate interactive analysis.

AVAILABILITY

The program is available for download from: http://bioinfoserver.rsbs.anu.edu.au/downloads/clans/

摘要

未标注

微阵列实验的分析因涉及大量数据而变得复杂。搜索共表达基因群组类似于在数据库中搜索蛋白质家族,因为在这两种情况下,都要在大量数据中找到具有相似特征的基因小子集。CLANS最初是为在大量氨基酸序列中查找蛋白质家族而开发的,因为所涉及的数据量使得系统发育方法过于繁琐。我们提出了一些改进措施,极大地扩展了CLANS的先前版本,并展示了其在微阵列数据中的应用以及整合额外信息以促进交互式分析的能力。

可用性

该程序可从以下网址下载:http://bioinfoserver.rsbs.anu.edu.au/downloads/clans/

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