Plant Bioinformatics

Go to Course: https://www.coursera.org/learn/plant-bioinformatics

Introduction

### Course Review: Plant Bioinformatics on Coursera In the rapidly advancing field of plant biology, the integration of bioinformatics has become a game-changer for researchers and enthusiasts alike. Coursera’s course, **Plant Bioinformatics**, stands out as a valuable resource for anyone interested in diving deep into the intricacies of plant genomes and gene expression data. With a comprehensive syllabus and a hands-on approach to learning, this course is designed to equip participants with the skills needed to navigate the complex world of plant bioinformatics effectively. #### Course Overview The last decade and a half has witnessed an explosion of data in plant biology—where scientific exploration now has the capability to generate hypotheses with remarkable ease. This course offers an in-depth exploration of the tools and techniques used in modern plant bioinformatics, allowing students to analyze vast datasets generated by sequencing technologies, RNA-seq, and other "-seq"-based methods. The overarching aim is to help learners understand gene function, expression, and the networks in which these genes operate, enhancing biological insights through data interpretation. #### Syllabus Breakdown 1. **Plant Genomic Databases**: The course kicks off with an exploration of essential plant databases such as Ensembl Plants, Gramene, and TAIR. Learners will become proficient at utilizing these resources to identify functional regions within genes and explore evolutionary relationships through gene trees. 2. **Expression Analysis**: This module delves into gene expression across different tissues and developmental stages, using tools like the eFP Browser and NCBI's Genome Data Viewer. By analyzing expression data, students learn to make informed predictions about phenotypic outcomes based on gene activity. 3. **Coexpression Tools**: Powerful algorithms like WGCNA allow students to classify genes with similar expression patterns, opening avenues for hypothesis generation. This module emphasizes the importance of association in biological research, equipping participants with techniques to link genes based on expression data. 4. **Promoter Analysis**: Understanding gene regulation via promoter regions is crucial. This section studies how transcription factors interact with cis-elements to modulate gene expression in response to environmental stimuli. This foundational knowledge is vital for grasping the complexities of plant behaviors. 5. **Functional Classification and Pathway Visualization**: Here, students learn about Gene Ontology (GO) enrichment analysis, enabling them to extract meaningful biological interpretations from large gene lists. Tools like AgriGO and MapMan facilitate pathway mapping, enhancing the understanding of biological processes and metabolic pathways. 6. **Network Exploration (PPIs, PDIs, GRNs)**: The course culminates in exploring molecular interactions through protein-protein interactions (PPIs) and genetic regulatory networks (GRNs). This segment underscores the interconnectedness of biological molecules, thus coaching students on techniques to model biological systems holistically. 7. **Quizzes and Assignments**: The course is structured with sectional quizzes to assess understanding, culminating with a comprehensive final assignment encouraging students to apply the knowledge gained throughout the modules. #### Recommendations **Who Should Take This Course?** The Plant Bioinformatics course is suitable for a diverse audience. Whether you are a beginner curious about plant bioinformatics or an experienced researcher looking to refine your analytical skills, this course offers valuable insights and practical knowledge. It is particularly beneficial for graduate students, researchers in plant biology, and professionals in biotechnology fields. **Why Enroll?** - **Hands-On Learning**: The course emphasizes practical applications and utilizes real datasets, which enhances learning by doing. - **Expert Instruction**: Learn from industry professionals who bring their expertise and insights into the classroom. - **Flexible Schedule**: Coursera's format allows you to progress at your own pace, making it easy to fit into a busy schedule. - **Networking Opportunities**: Engage with a community of learners who share your interests in plant biology and bioinformatics. In conclusion, the **Plant Bioinformatics** course on Coursera provides a robust foundation in the essential concepts and techniques of plant genomic analysis. By marrying theoretical insights with practical applications, learners are positioned to contribute meaningfully to this exciting and evolving field. Enroll today and take the next step in your bioinformatics journey!

Syllabus

Plant Genomic Databases, and useful sites for info about proteins

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!

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.

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.

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.

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!

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.