Chapter 8: Biological knowledge assembly and interpretation.
Chapter 8: Biological knowledge assembly and interpretation.
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DOI：
10.1371/journal.pcbi.1002858
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发表时间：
2012
影响因子：
4.3
通讯作者：
Kim JH
中科院分区：
文献类型：
--
作者：
Kim JH
大多数大规模基因表达微阵列和RNA-Seq数据分析方法旨在确定显示不同模式和/或显着差异的基因或基因产物列表。然而，最具挑战性和速率限制的步骤是确定所得到的基因和/或转录物列表的生物学意义。生物医学本体论和基于通路的功能富集分析被广泛用于解释紧密相关或差异表达基因的功能作用。使用基因本体术语或生物途径将基因组分配给相关的生物注释，然后测试它们是否显着丰富了相应的注释。与以前的方法不同，基因集富集分析采用完全相反的方法，使用预定义的基因集。差异共表达分析确定不同条件下配对基因集的共表达差异程度。 DNA 微阵列和 RNA-Seq 数据的结果可以转化为代表生物语义的图形结构。许多生物医学注释和外部存储库（包括临床资源）可以通过生物语义在概念格分析的框架内系统地集成。过去十年中开发了一系列生物知识组装和解释方法，明显提高了我们对高通量技术大规模基因组数据的生物学理解。
Most methods for large-scale gene expression microarray and RNA-Seq data analysis are designed to determine the lists of genes or gene products that show distinct patterns and/or significant differences. The most challenging and rate-liming step, however, is to determine what the resulting lists of genes and/or transcripts biologically mean. Biomedical ontology and pathway-based functional enrichment analysis is widely used to interpret the functional role of tightly correlated or differentially expressed genes. The groups of genes are assigned to the associated biological annotations using Gene Ontology terms or biological pathways and then tested if they are significantly enriched with the corresponding annotations. Unlike previous approaches, Gene Set Enrichment Analysis takes quite the reverse approach by using pre-defined gene sets. Differential co-expression analysis determines the degree of co-expression difference of paired gene sets across different conditions. Outcomes in DNA microarray and RNA-Seq data can be transformed into the graphical structure that represents biological semantics. A number of biomedical annotation and external repositories including clinical resources can be systematically integrated by biological semantics within the framework of concept lattice analysis. This array of methods for biological knowledge assembly and interpretation has been developed during the past decade and clearly improved our biological understanding of large-scale genomic data from the high-throughput technologies.
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影响因子：
14.9
作者：
Chung HJ;Park CH;Han MR;Lee S;Ohn JH;Kim J;Kim J;Kim JH
通讯作者：
Kim JH
影响因子：
14.9
作者：
Caspi R;Altman T;Dale JM;Dreher K;Fulcher CA;Gilham F;Kaipa P;Karthikeyan AS;Kothari A;Krummenacker M;Latendresse M;Mueller LA;Paley S;Popescu L;Pujar A;Shearer AG;Zhang P;Karp PD
通讯作者：
Karp PD
影响因子：
5.8
作者：
Al-Shahrour, F;Díaz-Uriarte, R;Dopazo, J
通讯作者：
Dopazo, J
影响因子：
19.6
作者：
Kim, JH;Ha, IS;Park, WY
通讯作者：
Park, WY
影响因子：
14.9
作者：
Chung, HJ;Kim, M;Kim, JH
通讯作者：
Kim, JH