|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/bioinformatics-methods-2
课程评论：没有评论
课程名称： 生物信息学方法 II 课程概述： 随着人类基因组测序和RNA-seq、微阵列等技术的广泛应用，大规模生物学项目产生了大量数据。然而，科学家面临的挑战是如何分析和访问这些数据，以提取与研究系统相关的有用信息。本课程重点介绍如何利用现有的生物信息资源（主要是基于网络的程序和数据库）来访问丰富的数据，并解答普通生物学家的相关问题，课程实践性强。 课程内容分为两个部分。第一部分生物信息学方法 I 涉及数据库、Blast、多序列比对、系统发育、选择分析和宏基因组学；第二部分生物信息学方法 II 则涵盖了模式搜索、蛋白质间相互作用、结构生物信息学、基因表达数据分析和顺式元件预测。 这对课程适合考虑攻读生物科学研究生的学生，以及考虑分子医学的学生。课程内容基于多伦多大学的教学案例，面向对基础分子生物学有一定了解的高年级本科生。如果您不熟悉基础知识，可以参考类似于 https://learn.saylor.org/course/bio101 的资源。该课程不要求编程，但在第5模块中包含一些命令行操作（通过网页浏览器进行）。 生物信息学方法 II 随时更新，最近一次更新是在2022年2月。 课程大纲： 1. 蛋白质模式：研究蛋白质家族中保守区域，了解其生物学功能，并通过各种描述方法探讨保守区域。 2. 蛋白质间相互作用：探索蛋白质间相互作用（PPIs）的重要性，使用不同工具和数据库分析BRCA2的相互作用伙伴，并进行基因本体（GO）术语富集分析。 3. 蛋白质结构：讨论确定蛋白质三维结构的方法，介绍蛋白质结构数据库PDB，并使用PyMOL探索多个蛋白质结构。 4. 基因表达分析 I：处理RNA-seq生成的基因表达数据，使用BioConductor进行数据处理和可视化。 5. 基因表达分析 II：对显著差异表达基因进行层次聚类，利用在线工具识别与选定基因关联的基因，并进行基因本体富集分析。 6. 顺式调控系统：研究基因表达的调控机制，分析特定基因启动子的顺式元件，并尝试预测新的顺式元件。 结课内容包括对基因表达分析和顺式调控系统的复习，以及最终作业的安排。
Name:Protein Motifs
Description:In this module we'll be exploring conserved regions within protein families. Such regions can help us understand the biology of a sequence, in that they are likely important for biological function, and also be used to help ascribe function to sequences where we can't identify any homologs in the databases. There are various ways of describing the conserved regions from simple regular expressions to profiles to profile hidden Markov models (HMMs).
Name:Protein-Protein Interactions
Description:In this module we'll be exploring protein-protein interactions (PPIs). Protein-protein interactions are important as proteins don't act in isolation, and often an examination of the interaction partners (determined in an unbiased, perhaps high throughput way) of a given protein can tell us a lot about its biology. We'll talk about some different methods used to determine PPIs and go over their strengths and weaknesses. In the lab we'll use 3 different tools and two different databases to examine interaction partners of BRCA2, a protein that we examined in last module's lab. Finally, we'll touch on a "foundational" concept, Gene Ontology (GO) term enrichment analysis, to help us understand in an overview way the proteins interacting with our example.
Name:Protein Structure
Description:The determination of a protein's tertiary structure in three dimensions can tell us a lot about the biology of that protein. In this module's mini-lecture, we'll talk about some different methods used to determine a protein's tertiary structure and cover the main database for protein structure data, the PDB. In the lab we'll explore the PDB and an online tool for searching for structural (as opposed to sequence) similarity, VAST. We'll then use a nice piece of stand-alone software, PyMOL, to explore several protein structures in more detail.
Name:Review: Protein Motifs, Protein-Protein Interactions, and Protein Structure
Description:
Name:Gene Expression Analysis I
Description:When and where genes are expressed (active) in tissues or cells is one of the main determinants of what makes that tissue or cell the way it is, both in terms of morphology and in terms of response to external stimuli. Several different methods exist for generating gene expression levels for all of the genes in the genome in tissues or even at cell-type-specific resolution. In this class we'll be processing and then examining some gene expression data generated using RNA-seq. We'll explore one of the main databases for RNA-seq expression data, the Sequence Read Archive (SRA), and then use an open-source suite of programs in R called BioConductor to process the raw reads from 4 RNA-seq data sets, to summarize their expression levels, to select significantly differentially expressed genes, and finally to visualize these as a heat map.
Name:Gene Expression Analysis II
Description:When and where genes are expressed (active) in tissues or cells is one of the main determinants of what makes that tissue or cell the way it is, both in terms of morphology and in terms of response to external stimuli. Several different methods exist for generating gene expression levels for all of the genes in the genome in tissues or even at cell-type-specific resolution. In this class we'll be hierarchically clustering our significantly differentially expressed genes from last time using BioConductor and the built-in function of an online tool, called Expression Browser. Then we'll be using another online tool that uses a similarity metric, the Pearson correlation coefficient, to identify genes responding in a similar manner to our gene of interest, in this case AP3. We'll use a second tool, ATTED-II to corroborate our gene list. We'll also be exploring some online databases of gene expression and an online tool for doing a Gene Ontology enrichment analysis.
Name:Cis Regulatory Systems
Description:When and where genes are expressed in tissues or cells is one of the main determinants of what makes that tissue or cell the way it is, both in terms of morphology and in terms of response to external stimuli. Gene expression is controlled in part by the presence of short sequences in the promoters (and other parts) of genes, called cis-elements, which permit transcription factors and other regulatory proteins to bind to direct the patterns of expression in certain tissues or cells or in response to environmental stimuli: We'll explore a couple of sets of promoters of genes that are coexpressed with AP3 from Arabidopsis, and with INSULIN from human, for the presence of known cis-elements, and we'll also try to predict some new ones using a couple of different methods.
Name:Review: Gene Expression Analysis and Cis Regulatory Systems + Final Assignment
Description:
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. Bioinformatic Methods II is regularly updated, and was last updated for February 2022.
