基因转录表达数据的生物信息挖掘研究  

Bioinformatics mining for gene expression data

作　　者：郭安源[1] GUO AnYuan(Hubei Bioinformatics and Molecular Imaging Key Laboratory,Key Laboratory of Molecular Biophysics of the Ministry of Education,Center for Artificial Intelligence Biology,College of Life Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学生命科学与技术学院,人工智能生物学中心,湖北省生物信息与分子成像重点实验室,分子生物物理教育部重点实验室,武汉430074

出　　处：《中国科学：生命科学》2021年第1期70-82,共13页Scientia Sinica(Vitae)

基　　金：国家优秀青年科学基金(批准号:31822030)资助。

摘　　要：基因表达是生物体中最重要和最基础的生物学过程和分子活动,生物体正是通过调控不同基因表达而实现生长发育和抵御刺激等生命活动.转录组测序是目前在生物医学研究中应用最为广泛的高通量检测基因表达的技术,也促进了大量针对转录组数据的生物信息挖掘方法和工具的发展.本文就基因表达中的转录组数据分析和挖掘方法进行了综述,从已有大规模转录组数据资源、转录组数据的常规分析、癌症转录组分析、转录组新技术和分析等生物信息方法方面进行了总结;同时,阐述了基于转录组数据的疾病标志物发现和分类预测模型研究方法,对正在兴起和迅速发展的单细胞转录组和空间转录组及其分析方法也进行了介绍;最后,总结了转录组测序适用的研究问题和分析内容及工具.本文将有助于广大生物医学研究者快速了解转录组技术的分析内容和适用情况,为选择合适的转录组测序和分析方法提供参考.Gene expression is the most important and fundamental biological process and molecular activity in living organisms.It is through the regulation of gene expression that organisms achieve growth and development and respond to environmental stimuli.Transcriptome sequencing is currently the most widely used technology for high-throughput detection of gene expression in biomedical research.It also contributes to the development of a large number of bioinformatics data mining methods and tools for transcriptome data.This paper provides a review of bioinformatics methods for transcriptome data analysis and mining in gene expression,from existing large-scale transcriptome data resources,routine analysis of transcriptome data,cancer transcriptome analysis,and new transcriptome technologies.Methods for disease marker discovery and classification prediction models based on transcriptomic data are also described,as well as the emerging and rapidly developing single-cell transcriptomes and spatial transcriptomes and their analysis methods.Finally,the applicable research questions and analytical contents and tools for transcriptome sequencing are summarized.This article will help biomedical researchers to quickly understand the analytical content and applicability of transcriptome technology and provide a reference for the selection of appropriate transcriptome sequencing and analysis methods.

关 键 词：转录组 生物信息学 数据挖掘 调控网络 大数据 肿瘤免疫 单细胞转录组测序 分析工具 

分 类 号：Q811.4[生物学—生物工程]