从植物微阵列数据推断功能信息的生物信息学工具II:单基因以外的分析。

Issa Coulibaly, Grier P Page
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引用次数: 16

摘要

虽然可以一次解释单个基因的微阵列实验,但大多数研究产生的差异表达基因的长列表,其解释需要整合先前的生物学知识。这些先验知识存储在各种公共和私人数据库中,涵盖了基因功能和生物信息的几个方面。在这篇综述中,我们将描述找到先前准确的生物信息的工具和地点,以及如何处理和合并它们来解释微阵列数据分析。在这里,我们重点介绍了基因类水平本体论分析(第2节)、基因共表达分析(第3节)、基因网络分析(第4节)、生物途径分析(第5节)、转录调控分析(第6节)和组学数据整合(第7节)所选择的工具和资源。本综述的总体目标是为研究人员提供工具和信息,以促进微阵列数据的解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bioinformatic tools for inferring functional information from plant microarray data II: Analysis beyond single gene.

While it is possible to interpret microarray experiments a single gene at a time, most studies generate long lists of differentially expressed genes whose interpretation requires the integration of prior biological knowledge. This prior knowledge is stored in various public and private databases and covers several aspects of gene function and biological information. In this review, we will describe the tools and places where to find prior accurate biological information and how to process and incorporate them to interpret microarray data analyses. Here, we highlight selected tools and resources for gene class level ontology analysis (Section 2), gene coexpression analysis (Section 3), gene network analysis (Section 4), biological pathway analysis (Section 5), analysis of transcriptional regulation (Section 6), and omics data integration (Section 7). The overall goal of this review is to provide researchers with tools and information to facilitate the interpretation of microarray data.

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