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从基因到生物系统研究中的功能类别。

From genes to functional classes in the study of biological systems.

作者信息

Al-Shahrour Fátima, Arbiza Leonardo, Dopazo Hernán, Huerta-Cepas Jaime, Mínguez Pablo, Montaner David, Dopazo Joaquín

机构信息

Bioinformatics Department, Valencia, Spain.

出版信息

BMC Bioinformatics. 2007 Apr 3;8:114. doi: 10.1186/1471-2105-8-114.

DOI:10.1186/1471-2105-8-114
PMID:17407596
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1853114/
Abstract

BACKGROUND

With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.

RESULTS

Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.

CONCLUSION

Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.

摘要

背景

随着高通量技术的普及，对有助于生物学结果解释的程序的需求大幅增加。最近，受系统生物学标准启发的新程序已开始研发。

结果

在此我们展示FatiScan，这是一个基于网络的程序，它为大规模实验的功能解释实施了一种与阈值无关的测试，该测试不依赖于基于对每个基因多次应用独立测试来预先选择基因。所实施的测试旨在直接测试功能相关基因块的行为，而非专注于单个基因。此外，该测试不依赖于用于获得显著性值的数据类型，因此不同类型的生物学信息术语（基因本体、通路、功能基序、转录因子结合位点或来自CisRed的调控位点）可应用于不同类别的基因组规模研究。我们举例说明了它在微阵列基因表达、进化和相互作用组学中的应用。

结论

此外独立于原始数据和实验设计的基因集富集方法，构成了基因组规模实验功能分析的一个有前景的替代方案。可在以下网址找到执行所述测试及其他类似测试的网络服务器：http://www.babelomics.org 。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/c7be830a5683/1471-2105-8-114-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/c7f4b1993e12/1471-2105-8-114-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/7da0a811ad41/1471-2105-8-114-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/b9d8e9f76d5b/1471-2105-8-114-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/b8093926ffcf/1471-2105-8-114-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/98d29296e3e1/1471-2105-8-114-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/c7be830a5683/1471-2105-8-114-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/c7f4b1993e12/1471-2105-8-114-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/7da0a811ad41/1471-2105-8-114-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/b9d8e9f76d5b/1471-2105-8-114-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/b8093926ffcf/1471-2105-8-114-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/98d29296e3e1/1471-2105-8-114-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e3f/1853114/c7be830a5683/1471-2105-8-114-6.jpg

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