Complex functionality of gene groups identified from high-throughput data

被引:17
作者
Antonov, Alexey V.
Mewes, Hans W.
机构
[1] GSF, Natl Res Ctr Environm & hlth, Inst Bioinformat, D-85764 Neuherberg, Germany
[2] Tech Univ Munich, Dept Genome Oriented Bioinformat, Wissenschaftzentrum Weihenstephan, D-85350 Freising Weihenstephan, Germany
关键词
gene function; functional annotation; automatic functional profiling; high-throughput data; rule mining;
D O I
10.1016/j.jmb.2006.07.062
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 [生物化学与分子生物学]; 081704 [应用化学];
摘要
Relating experimental data to biological knowledge is necessary to cope with the avalanches of new data emerging from recent developments in high-throughput technologies. Automatic functional profiling becomes the defacto standard approach for the secondary analysis of high-throughput data. A number of tools employing available gene functional annotations have been developed for this purpose. However, current annotations are derived mostly from traditional analysis of the individual gene function. The complex biological phenomena carried out by the concerted activity of many genes often requires the definition of new complex functionality (related to a group of genes), which is, in many cases, not available in current annotation vocabularies. Functional profiling with annotation terms related to the description of individual biological functions of a gene may fail to provide reasonable interpretation of biological relationships in a set of genes involved in complex biological phenomena. We introduce a novel procedure to profile a complex functionality of a gene set. Complex functionality is constructed as a combination of available an-notation terms. By profiling ChIP-chip data from Saccharomyces cerevisiae we demonstrate that this technique produces deeper insights into the results of high-throughput experiments that are beyond the known facts described in the functional classifications. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:289 / 296
页数:8
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