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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?"

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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

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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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Traffic lights

Read about what's good
what should give you pause
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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Reviews summary

Hands-on plant bioinformatics tooling

According to learners, this course provides a strong, practical foundation in using key bioinformatics tools for plant research. Students highlight the course's structure, particularly the extensive hands-on labs which comprise a significant portion of each module. They appreciate the coverage of a wide array of databases and software for analyzing various biological data types, such as genomics, expression, and protein interactions. While the course is seen as a valuable resource for those in plant science, some learners note the challenging pace and suggest a need for prior biological or computational knowledge to fully benefit.
Assumes some prior biology/computing background.
"While the course provides theory mini-lectures, some prior understanding of molecular biology or basic computing is helpful."
"I found that my background in biology made it easier to grasp the context, but the bioinformatics side was still challenging."
"Having some familiarity with command-line interfaces or biological data types would be advantageous before starting."
Well-organized weekly learning units.
"The consistent structure of intro, theory, lab, discussion, and summary per module is very effective."
"I liked the modular approach; it broke down complex topics into manageable weekly chunks."
"The organization of the course content made it easy to follow and navigate."
Geared towards analyzing real 'omics data.
"This course directly applies bioinformatics concepts to the analysis of plant 'omics data, which is highly relevant."
"I can immediately apply the techniques learned to interpret my own experimental results."
"The focus on using tools to generate hypotheses from data was exactly what I needed."
Covers many essential databases and tools.
"I appreciated the breadth of tools covered, from genomic databases like Araport and Gramene to expression tools like eFP Browser."
"The course introduces a comprehensive set of tools essential for plant bioinformatics analysis."
"Learning about tools for GO enrichment and network analysis was particularly useful for my research."
Significant lab time for practical tool use.
"Each module has a 1.5 hour hands-on lab, allowing ample time to practice with the tools discussed."
"I really benefited from the extensive lab sessions; they were key to understanding the concepts."
"The practical labs using real bioinformatics tools are definitely the strongest aspect of this course for me."
May be fast for beginners or specific backgrounds.
"The amount of information and tools covered each week can feel a bit overwhelming at times."
"Keeping up with the pace required dedication, especially if you're new to some of the concepts or tools."
"Learners without a strong biology or computing background might find the material moves quickly."

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
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
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  • Review key concepts in plant anatomy, morphology, and developmental biology.
  • Recall basic principles of plant physiology and metabolism.
Review Statistics
Refresh your understanding of statistical concepts to prepare for the course's heavy use of statistical methods and data analysis.
Browse courses on Statistics
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  • Review fundamental concepts of probability and statistics.
  • Practice solving problems involving descriptive statistics.
Assist Fellow Students in Plant Bioinformatics
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
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  • 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
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
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
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
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.

Career center

Learners who complete Plant Bioinformatics will develop knowledge and skills that may be useful to these careers:
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.
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.
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.
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 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 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 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.
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.
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.
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.
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
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.
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.
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.
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.

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.
This textbook provides a comprehensive overview of the methods and tools used in plant bioinformatics for both background and additional reading.
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.

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