生物信息学方法 II

课程概况

大规模生物项目，如人类基因组测序，利用RNA序列、微阵列和其他科学技术的基因表达研究，已经为生物学家创建了丰富的数据资料。但是对科学家来说，对数据进行分析、并从中提取与研究项目相关的有用信息却是现在面临的一大挑战。本课程侧重于生物数据分析的实际操作，以及利用现存的生物信息资源，主要通过基于网络的应用程序和数据库访问大量数据，解答生物学家的一般性问题。

课程分为两个独立部分，内容包括多序列对比、系统发生学、基因表达的数据分析和蛋白质相互作用网络。

第一部分课程，生物信息学方法一，主要研究数据库、Blast、多序列对比、系统发生学、选择分析和宏基因组学。

第二部分课程，生物信息学方法二，主讲基序检索、蛋白质相互作用、结构生物信息学、基因表达的数据分析和顺式元件预测。

整套课程对考虑攻读生物科学和分子医学研究生的学生均有所帮助。

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.

Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and protein interaction networks, in two separate parts.

The first part, Bioinformatic Methods I, dealt with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis and metagenomics.

This, the second part, Bioinformatic Methods II, will cover motif searching, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions.

This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine.

These courses are based on one taught at the University of Toronto to upper-level undergraduates who have some understanding of basic molecular biology. If you’re not familiar with this, something like https://learn.saylor.org/course/bio101 might be helpful. No programming is required for this course although some command line work (though within a web browser) occurs in the 5th module.