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Nicholas James Provart
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?"
Read more
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?"
Read more
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 [Material updated in June 2023]:
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, ARDB, TravaDB, NCBI Genome Data Viewer for exploring RNA-seq data for many plant species, 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
Register for this course and see more details by visiting:
OpenCourser.com/course/o3ufyq/plant
What's inside
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!
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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!
Read more
Expression Analysis
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.
Coexpression Tools
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.
Sectional Quiz 1
Promoter Analysis
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.
Functional Classification and Pathway Vizualization
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!
Network Exploration (PPIs, PDIs, GRNs)
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.
Sectional Quiz 2 and Final Assignment
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in applying computational tools and techniques to plant molecular biology research
Core audience includes students and researchers with interests in plant biology, computational biology, and molecular biology
Emphasizes hands-on labs and interactive materials, providing practical experience
Provides a comprehensive overview of computational tools for plant molecular biology research
Covers a wide range of topics, from genomic databases to network exploration
May not be suitable for complete beginners in plant biology or computational biology
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Save Plant Bioinformatics to your list so you can find it easily later:
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Reviews summary
Highly recommended course
According to students, Plant Bioinformatics is a highly recommended course. Learners say the course is very informative, with excellent lectures that introduce relevant bioinformatics tools and research topics. Labs and questions help reinforce concepts and provide hands-on experience. The course is suitable for learners with and without prior bioinformatics experience.
Lectures are clear and informative.
"Lectures were short, but very informative"
"Overall the course is good. However, lecture should be more in detail."
Instructor is knowledgeable and helpful.
"I would also like to thank, Course Instructor, Nicholas Provart, for making such important course, this will help lot of aspiring plant biologists."
"Thanks to Plant Bionformatics I've learn many know-how in bioinformatics!"
Course is well-structured and easy to follow.
"VERY DETAILED INSTRUCTIONS THAT INDUCE INVOLVEMENT FROM THE READER."
"Great course!! I really liked most of the labs and the way the course is organized."
Course is well-received by students.
"Very good course."
"Excellent course "
"I really enjoyed this course."
Labs provide hands-on experience with bioinformatics tools.
"the lab exercise helped to explore multiple bioinformatics tools and database."
"I am able to learn the different tools in transcritpom analysis."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Plant Bioinformatics with these
activities:
Revisit Plant Biology
Show steps
Reinforce your foundational knowledge of plant biology, providing a strong base for understanding the advanced concepts covered in the course.
Browse courses on
Plant Biology
Show steps
-
Review key concepts in plant anatomy, morphology, and developmental biology.
-
Recall basic principles of plant physiology and metabolism.
Review Statistics
Show steps
Refresh your understanding of statistical concepts to prepare for the course's heavy use of statistical methods and data analysis.
Browse courses on
Statistics
Show steps
-
Review fundamental concepts of probability and statistics.
-
Practice solving problems involving descriptive statistics.
Assist Fellow Students in Plant Bioinformatics
Show steps
Enhance your understanding by helping others learn. Volunteer to mentor fellow students in plant bioinformatics or participate in online forums to provide support.
Browse courses on
Mentoring
Show steps
-
Identify opportunities to mentor other students, such as through university clubs or online communities.
-
Share your knowledge and expertise to assist others in understanding plant bioinformatics concepts and tools.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore Biopython
Show steps
Become familiar with Biopython, a powerful Python library for bioinformatics, to enhance your ability to analyze biological data.
Show steps
-
Follow tutorials to learn the basics of Biopython.
-
Practice working with sequence data using Biopython functions.
Participate in a Plant Bioinformatics Workshop
Show steps
Acquire hands-on experience and in-depth knowledge by participating in a plant bioinformatics workshop led by experts in the field.
Show steps
-
Research and identify plant bioinformatics workshops offered by universities, research institutions, or online platforms.
-
Apply and participate in a workshop that aligns with your interests and learning objectives.
Solve Plant Bioinformatics Coding Challenges
Show steps
Sharpen your computational skills by solving coding challenges specifically designed for plant bioinformatics.
Browse courses on
Coding Challenges
Show steps
-
Identify online platforms or resources offering plant bioinformatics coding challenges.
-
Practice solving challenges involving sequence analysis, phylogenetic reconstruction, or genome assembly.
Develop a Plant Genome Analysis Workflow
Show steps
Create a step-by-step workflow for analyzing plant genome data, incorporating the tools and techniques covered in the course.
Browse courses on
Genome Analysis
Show steps
-
Design a workflow for a specific plant genome analysis task.
-
Document the workflow using a tool like Snakemake or Nextflow.
-
Execute and evaluate the workflow using real plant genome data.
Revisit Plant Biology
Show steps
Reinforce your foundational knowledge of plant biology, providing a strong base for understanding the advanced concepts covered in the course.
Browse courses on
Plant Biology
Show steps
Show steps
Show steps
- Review key concepts in plant anatomy, morphology, and developmental biology.
- Recall basic principles of plant physiology and metabolism.
Review Statistics
Show steps
Refresh your understanding of statistical concepts to prepare for the course's heavy use of statistical methods and data analysis.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review fundamental concepts of probability and statistics.
- Practice solving problems involving descriptive statistics.
Assist Fellow Students in Plant Bioinformatics
Show steps
Enhance your understanding by helping others learn. Volunteer to mentor fellow students in plant bioinformatics or participate in online forums to provide support.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify opportunities to mentor other students, such as through university clubs or online communities.
- Share your knowledge and expertise to assist others in understanding plant bioinformatics concepts and tools.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore Biopython
Show steps
Become familiar with Biopython, a powerful Python library for bioinformatics, to enhance your ability to analyze biological data.
Show steps
Show steps
Show steps
- Follow tutorials to learn the basics of Biopython.
- Practice working with sequence data using Biopython functions.
Participate in a Plant Bioinformatics Workshop
Show steps
Acquire hands-on experience and in-depth knowledge by participating in a plant bioinformatics workshop led by experts in the field.
Show steps
Show steps
Show steps
- Research and identify plant bioinformatics workshops offered by universities, research institutions, or online platforms.
- Apply and participate in a workshop that aligns with your interests and learning objectives.
Solve Plant Bioinformatics Coding Challenges
Show steps
Sharpen your computational skills by solving coding challenges specifically designed for plant bioinformatics.
Browse courses on
Coding Challenges
Show steps
Show steps
Show steps
- Identify online platforms or resources offering plant bioinformatics coding challenges.
- Practice solving challenges involving sequence analysis, phylogenetic reconstruction, or genome assembly.
Develop a Plant Genome Analysis Workflow
Show steps
Create a step-by-step workflow for analyzing plant genome data, incorporating the tools and techniques covered in the course.
Browse courses on
Genome Analysis
Show steps
Show steps
Show steps
- Design a workflow for a specific plant genome analysis task.
- Document the workflow using a tool like Snakemake or Nextflow.
- Execute and evaluate the workflow using real plant genome data.
Career center
Learners who complete Plant Bioinformatics will develop knowledge and skills
that may be useful to these careers:
Bioinformatician
Bioinformatician
A Bioinformatician uses computational tools to solve biological problems. They may develop new algorithms or software, or apply existing ones to analyze data. This course provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills are essential for a Bioinformatician, and this course will help you develop the skills you need to succeed in this field.
Data Scientist
Data Scientist
A Data Scientist uses data to solve problems. They may collect, clean, and analyze data, or develop new algorithms or software to do so. This course may be useful for a Data Scientist because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to analyze plant data and develop new tools for plant research.
Research Scientist
Research Scientist
A Research Scientist conducts research in a variety of fields, including biology, chemistry, and physics. They may use a variety of techniques to conduct their research, including DNA sequencing, gene expression analysis, and genetic mapping. This course may be useful for a Research Scientist because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to analyze plant data and develop new tools for plant research.
Bioinformatics Scientist
Bioinformatics Scientist
A Bioinformatics Scientist uses computational tools to solve biological problems. They may develop new algorithms or software, or apply existing ones to analyze data. This course may be useful for a Bioinformatics Scientist because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to analyze plant data and develop new tools for plant research.
Geneticist
Geneticist
A Geneticist studies genes and their role in heredity. They may use a variety of techniques to study genes, including DNA sequencing, gene expression analysis, and genetic mapping. This course may be useful for a Geneticist because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to analyze plant data and develop new tools for plant research.
Plant Pathologist
Plant Pathologist
A Plant Pathologist studies plant diseases. They may use a variety of techniques to study plant diseases, including DNA sequencing, gene expression analysis, and genetic mapping. This course may be useful for a Plant Pathologist because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to analyze plant data and develop new tools for plant research.
Plant Breeder
Plant Breeder
A Plant Breeder uses genetic techniques to improve plants. They may develop new varieties of plants with desired traits, such as resistance to pests or diseases. This course may be useful for a Plant Breeder because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to analyze plant data and develop new tools for plant research.
Plant Physiologist
Plant Physiologist
A Plant Physiologist studies the physiology of plants. They may use a variety of techniques to study plant physiology, including DNA sequencing, gene expression analysis, and genetic mapping. This course may be useful for a Plant Physiologist because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to analyze plant data and develop new tools for plant research.
Computational Biologist
Computational Biologist
A Computational Biologist uses computational tools to solve biological problems. They may develop new algorithms or software, or apply existing ones to analyze data. This course may be useful for a Computational Biologist because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to analyze plant data and develop new tools for plant research.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course may be useful for a Software Engineer because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to develop new software tools for plant research.
Web Developer
Web Developer
A Web Developer designs, develops, and maintains websites. This course may be useful for a Web Developer because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to develop websites about plant research.
Technical Writer
Technical Writer
A Technical Writer writes technical documentation, such as user manuals, white papers, and marketing materials. This course may be useful for a Technical Writer because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to write technical documentation about plant research in a clear and concise way.
Statistician
Statistician
A Statistician uses statistical methods to collect, analyze, and interpret data. This course may be useful for a Statistician because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to analyze plant data and develop new statistical methods for plant research.
Teacher
Teacher
A Teacher teaches students at a variety of levels, from elementary school to college. This course may be useful for a Teacher because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to teach plant biology to students in a clear and concise way.
Science Writer
Science Writer
A Science Writer writes about science for a variety of audiences, including the general public, students, and researchers. This course may be useful for a Science Writer because it provides a foundation in plant bioinformatics, including topics such as genomic databases, expression analysis, and coexpression tools. These skills can be used to write about plant research in a clear and concise way.
For more career information including salaries, visit:
OpenCourser.com/course/o3ufyq/plant
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Plant Bioinformatics.
Save
This textbook provides a comprehensive overview of the methods and tools used in plant bioinformatics for both background and additional reading.
Provides a comprehensive overview of the field of bioinformatics and functional genomics, including chapters on plant bioinformatics.
Provides a good background in bioinformatics for students who need more information on the general methods and tools used in the field.
Provides a broad overview of the field of plant biotechnology, including chapters on plant bioinformatics.
Valuable background reference for students who need more information on the general methods and tools used in bioinformatics.
Provides a good background in bioinformatics for students who need more information on the general methods and tools used.
Useful reference tool to better understand the experimental methods used in plant bioinformatics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/o3ufyq/plant
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Effort
6 weeks of hands-on study, plus 2 evaluation weeks. The hands-on modules will take 2-4 hours of time
Level
Intermediate
Via
Coursera
Institution
University of Toronto
Instructor
Nicholas James Provart
Language
English
Transcripts
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This course belongs to a
collection
called
Plant Bioinformatic Methods. It's comprised of four courses:
- Bioinformatic Methods I
- Plant Bioinformatics Capstone
- Bioinformatic Methods II
- Plant Bioinformatics (viewing)
Good to know
Develops skills in applying computational tools and techniques to plant molecular biology research
Core audience includes students and researchers with interests in plant biology, computational biology, and molecular biology
Emphasizes hands-on labs and interactive materials, providing practical experience
Provides a comprehensive overview of computational tools for plant molecular biology research
Covers a wide range of topics, from genomic databases to network exploration
May not be suitable for complete beginners in plant biology or computational biology
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