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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/plant-bioinformatics
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课程名称：植物生物信息学 课程概述：过去15年植物生物学发展迅速，数百个植物基因组被测序，RNA测序（RNA-seq）使得全面的转录组表达谱分析成为可能，众多基于“-seq”的方法大大降低了蛋白质-蛋白质和蛋白质-DNA相互作用的检测成本，同时提高了高通量检测能力。这些数据集使我们能够通过简单的点击生成假设。例如，了解基因的表达位置和时间可以帮助我们在“正常”生长条件下找出基因突变体不表现表型的原因。共表达分析和关联网络能够提供与特定生物过程相关的高质量候选基因。使用基因本体富集分析和通路可视化工具，可以帮助我们理解自己的“组学”实验，并回答“在我们感兴趣的突变体中，哪些过程/通路被扰动了？” 课程结构：该课程为期6周的实践模块，每个模块包括约2分钟的介绍、约20分钟的理论小讲座、1.5小时的动手实验、可选的约20分钟实验讨论（以解决实验中的困难），以及约2分钟的总结。 涵盖工具： 模块1：基因组数据库 / 预计算基因树 / 蛋白质工具（包括Araport, TAIR, Gramene, EnsemblPlants Compara, PLAZA; SUBA4和细胞eFP浏览器, 1001基因组浏览器） 模块2：表达工具（eFP浏览器/eFP-Seq浏览器, Araport, Genevestigator, TravaDB, NCBI基因组数据浏览器） 模块3：共表达工具（ATTED II, Expression Angler, AraNet, AtCAST2） 模块4：启动子分析（Cistome, MEME, ePlant） 模块5：GO富集分析和通路可视化（AgriGO, AmiGO, Classification SuperViewer, TAIR, g:profiler, AraCyc, MapMan） 模块6：网络探索（阿拉伯芥相互作用浏览器2, ePlant, TF2Network, 虚拟植物, GeneMANIA） 课程大纲： 1. 植物基因组数据库和蛋白质信息网站的探索。 2. 基因表达分析，包括探讨各种工具来分析Arabidopsis和其他植物的RNA-seq数据。 3. 共表达工具的利用，包括通过WGCNA算法组织数据。 4. 启动子分析，探讨基因如何通过转录因子的结合进行调控。 5. 功能分类和通路可视化，使用工具进行基因本体富集分析和路径映射。 6. 网络探索，研究细胞内的分子交互和网络行为。 本课程的目标是使学生能够熟练运用多种生物信息学工具，以便有效分析植物基因组、转录组和相关生物信息，以推动植物研究的深入发展。
Name:Plant Genomic Databases, and useful sites for info about proteins
Description:In this module we'll be exploring several plant databases including Ensembl Plants, Gramene, PLAZA, SUBA, TAIR and Araport. The information in these databases allows us to easily identify functional regions within gene products, view subcellular localization, find homologs in other species, and even explore pre-computed gene trees to see if our gene of interest has undergone a gene duplication event in another species, all at the click of a mouse!
Name:Expression Analysis
Description:Vast databases of gene expression and nifty visualization tools allow us to explore where and when a gene is expressed. Often this information can be used to help guide a search for a phenotype if we don't see a phenotype in a gene mutant under "normal" growth conditions. We explore several tools for Arabidopsis data (eFP Browser, ARDB, TraVA DB, Araport) along with NCBI's Genome Data Viewer for RNA-seq data for other plant species. We also examine the MPSS database of small RNAs and degradation products to see if our example gene has any potential microRNA targets.
Name:Coexpression Tools
Description:Being able to group genes by similar patterns of expression across expression data sets using algorithms like WGCNA is a very useful way of organizing the data. Clusters of genes with similar patterns of expression can then be subject to Gene Ontology term enrichment analysis (see Module 5) or examined to see if they are part of the same pathway. What's even more powerful is being able to identify genes with similar patterns of expression without doing a single expression profiling experiment, by mining gene expression databases! There are several tools that allow you to do this in many plant species simply by entering a query gene identifier. The genes that are returned are often in the same biological process as the query gene, and thus this "guilt-by-association" paradigm is a excellent tool for hypothesis generation.
Name:Sectional Quiz 1
Description:
Name:Promoter Analysis
Description:The regulation of gene expression is one of the main ways by which a plant can control the abundance of a gene product (post-translational modifications and protein degradation are some others). When and where a gene is expressed is controlled to a large extent by the presence of short sequence motifs, called cis-elements, present in the promoter of the gene. These in turn are regulated by transcription factors that perhaps get induced in response to environmental stresses or during specific developmental programs. Thus understanding which transcription factors can bind to which promoters can help us understand the role the downstream genes might be playing in a biological system.
Name:Functional Classification and Pathway Vizualization
Description:Often the results of 'omics experiments are large lists of genes, such as those that are differentially expressed. We can use a "cherry picking" approach to explore individual genes in those lists but it's nice to be able to have an automated way of analyzing them. Here tools for performing Gene Ontology enrichment analysis are invaluable and can tell you if any particular biological processes or molecular functions are over-represented in your gene list. We'll explore AgriGO, AmiGO, tools at TAIR and the BAR, and g:Profiler, which all allow you to do such analyses. Another useful analysis is to be able to map your gene lists (along with associated e.g. expression values) onto pathway representations, and we'll use AraCyc and MapMan to do this. In this way it is easy to see if certain biosynthetic reactions are upregulated, which can help you interpret your 'omics data!
Name:Network Exploration (PPIs, PDIs, GRNs)
Description:Molecules inside the cell rarely operate in isolation. Proteins act together to form complexes, or are part of signal transduction cascades. Transcription factors bind to cis-elements in promoters or elsewhere and can act as activators or repressors of transcription. MicroRNAs can affect transcription in other ways. One of the main themes to have emerged in the past two decades in biology is that of networks. In terms of protein-protein interaction networks, often proteins that are highly connected with others are crucial for biological function – when these “hubs” are perturbed, we see large phenotypic effects. The way that transcription factors interact with downstream promoters, some driving the expression of other transcription factors that in turn regulate genes combinatorially with upstream transcription factors can have an important biological effect in terms of modulating the kind of output achieved. The tools described in this lab can help us to explore molecular interactions in a network context, perhaps with the eventual goal of modeling the behaviour of a given system.
Name:Sectional Quiz 2 and Final Assignment
Description:
The past 15 years have been exciting ones in plant biology. Hundreds of plant genomes have been sequenced, RNA-seq has enabled transcriptome-wide expression profiling, and a proliferation of "-seq"-based methods has permitted protein-protein and protein-DNA interactions to be determined cheaply and in a high-throughput manner. These data sets in turn allow us to generate hypotheses at the click of a mouse. For instance, knowing where and when a gene is expressed can help us narrow down the phenotypic search space when we don't see a phenotype in a gene mutant under "normal" growth conditions. Coexpression analyses and association networks can provide high-quality candidate genes involved in a biological process of interest. Using Gene Ontology enrichment analysis and pathway visualization tools can help us make sense of our own 'omics experiments and answer the question "what processes/pathways are being perturbed in our mutant of interest?" Structure: each of the 6 week hands-on modules consists of a ~2 minute intro, a ~20 minute theory mini-lecture, a 1.5 hour hands-on lab, an optional ~20 minute lab discussion if experiencing difficulties with lab, and a ~2 minute summary. Tools covered: Module 1: GENOMIC DBs / PRECOMPUTED GENE TREES / PROTEIN TOOLS. Araport, TAIR, Gramene, EnsemblPlants Compara, PLAZA; SUBA4 and Cell eFP Browser, 1001 Genomes Browser Module 2: EXPRESSION TOOLS. eFP Browser / eFP-Seq Browser, Araport, Genevestigator, TravaDB, NCBI Genome Data Viewer for exploring RNA-seq data for many plant species other than Arabidopsis, MPSS database for small RNAs Module 3: COEXPRESSION TOOLS. ATTED II, Expression Angler, AraNet, AtCAST2 Module 4: PROMOTER ANALYSIS. Cistome, MEME, ePlant Module 5: GO ENRICHMENT ANALYSIS AND PATHWAY VIZUALIZATION. AgriGO, AmiGO, Classification SuperViewer, TAIR, g:profiler, AraCyc, MapMan (optional: Plant Reactome) Module 6: NETWORK EXPLORATION. Arabidopsis Interactions Viewer 2, ePlant, TF2Network, Virtual Plant, GeneMANIA [Material updated in June 2019]
