转录组数据分析与功能基因挖掘  

Transcriptomics Analysis and Functional Genes Mining

作　　者：李欣 李小俊[1] 陈晓丽[1] 赵毅强 王栋[1] LI Xin;LI Xiaojun;CHEN Xiaoli;ZHAO Yiqiang;WANG Dong(Institute of Animal Science,Chinese Academy of Agricultural Sciences,Beijing 100193,China;College of Biological Sciences,China Agricultural University,Beijing 100193,China)

机构地区：[1]中国农业科学院北京畜牧兽医研究所,北京100193 [2]中国农业大学生物学院,北京100193

出　　处：《畜牧兽医学报》2019年第3期474-484,共11页ACTA VETERINARIA ET ZOOTECHNICA SINICA

基　　金：国家自然科学基金(31372296)

摘　　要：高通量测序技术的不断发展和应用,为挖掘重要功能基因提供了转录组分析方法,但如何利用海量测序数据准确、高效地挖掘功能基因,仍是转录组学分析方法研究的重要瓶颈。本文综述了RNA-seq数据质量控制与读段定位、基因组注释、转录本拼接、表达水平评估、差异表达分析等环节分析方法,比较了数据分析常用软件、算法和数据库等的性能和适用范围;同时,又综述了蛋白调控互作网络和加权基因共表达网络等差异表达基因的功能分析方法。转录组分析正在从只利用物种内信息挖掘差异基因,向引入其他物种参考系进行目标物种功能基因挖掘分析方向发展。结合同源基因预测候选基因法、选择信号法、极端数据法、GO注释和KEGG富集分析法及BSR-Seq(bulked segregant RNA-Seq)法等鉴定方法,使分析结果更加科学可靠。随着测序技术和数据分析方法不断进步、数据库资源不断完善,测序数据中隐含的基因表达调控和生命规律将会逐渐得到准确、深入揭示。With the continuous development and application of high-throughput sequencing technology, transcriptome analysis method is developed for mining genes with important function. However, a lot work needs to be done for efficient and accurate transcriptome analysis based on massive sequencing data. Here, we reviewed methods for reads quality control, reads mapping, genome annotation, transcripts assembling, expression quantification, differential expression analysis for RNA-seq data. We summarized the performance and scope of application of the common softwares, algorithms and databases used. We also reviewed analysis methods such as protein regulatory interaction networks as well as weighted gene co-expression networks. Transcriptome analysis has been evolved from identifying differentially expressed gene within-species to utilizing related species as reference to mine the functional genes in target species. By combining with various methods, such as the homologous gene prediction, select signal detection, extreme data analysis, GO annotation and KEGG enrichment and bulked segregant RNA-Seq (BSR-Seq) methods, the results from RNA-seq analysis are more scientific and reliable. With the development of sequencing technology and data analysis methods as well as continuous improvement of database resources, the underline gene regulation and the law of life implied in the sequencing data will be uncovered accurately and deeply in future.

关 键 词：转录组 数据分析 基因挖掘 

分 类 号：S813.1[农业科学—畜牧学]