Troyanskaya Olga G
Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, NJ 08544, USA.
Brief Bioinform. 2005 Mar;6(1):34-43. doi: 10.1093/bib/6.1.34.
In recent years, multiple types of high-throughput functional genomic data that facilitate rapid functional annotation of sequenced genomes have become available. Gene expression microarrays are the most commonly available source of such data. However, genomic data often sacrifice specificity for scale, yielding very large quantities of relatively lower-quality data than traditional experimental methods. Thus sophisticated analysis methods are necessary to make accurate functional interpretation of these large-scale data sets. This review presents an overview of recently developed methods that integrate the analysis of microarray data with sequence, interaction, localisation and literature data, and further outlines current challenges in the field. The focus of this review is on the use of such methods for gene function prediction, understanding of protein regulation and modelling of biological networks.
近年来，多种有助于对测序基因组进行快速功能注释的高通量功能基因组数据已可获取。基因表达微阵列是此类数据最常见的来源。然而，基因组数据往往为了规模而牺牲特异性，产生的相对低质量数据数量比传统实验方法多得多。因此，需要复杂的分析方法来对这些大规模数据集进行准确的功能解读。本综述概述了最近开发的将微阵列数据分析与序列、相互作用、定位和文献数据相结合的方法，并进一步概述了该领域当前面临的挑战。本综述的重点是此类方法在基因功能预测、蛋白质调控理解和生物网络建模中的应用。
