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Candace Savonen, MS

The course is intended for students in the biomedical sciences and researchers who have been given data and don’t know what to do with it or would like an overview of the different genomic data types that are out there.

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The course is intended for students in the biomedical sciences and researchers who have been given data and don’t know what to do with it or would like an overview of the different genomic data types that are out there.

This course is written for individuals who:

- Have genomic data and don’t know what to do with it.

- Want a basic overview of genomic data types.

- Want to find resources for processing and interpreting genomics data.

Goal of this course:

Equip learners with tutorials and resources so they can understand and interpret their genomic data in a way that helps them meet their goals and handle the data properly. This includes helping learners formulate questions they will need to ask others about their data

What is not the goal

Teach learners about choosing parameters or about the ins and outs of every genomic tool they might be interested in. This course is meant to connect people to other resources that will help them with the specifics of their genomic data and help learners have more efficient and fruitful discussions about their data with bioinformatic experts.

The course is intended for students in the biomedical sciences who have been given data and don’t know what to do with it or would like an overview of the different genomic data types that are out there.

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What's inside

Syllabus

Introduction
In this module, we cover the basics of what will be covered in this course and what you should expect. Next we get into an overview of what omic data types and their workflows often look like.
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Considerations for omic projects
In this module, we cover some considerations for how to choose between different omics tools. Then we talk about what is metadata and how do you make sure you get the most of your metadata?
Omic Basics
In this module, we build on the previous module's generalities about omic data and get into specifics about sequencing data and microarray data.
Annotation and general tools
In this module, we discuss what are general data analysis tools you may want to consider using. Lastly we cover some basics about annotation that are involved in every omic data analysis workflow.
Wrapping Up
Now that you've learned some fundamentals about omic data, in this section we have a final quiz that tests your absorption of what we've discussed. We also have a Tool Glossary of recommended tools you may want to consider using for your next steps with your omic data.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Suitable for beginners in biomedical sciences and researchers
Provides tutorials and resources for understanding genomic data
Covers basic concepts and tools for genomic data analysis
Helps learners ask the right questions about their data
Connects learners to resources for further exploration
Focuses on understanding data rather than specific tools

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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 Researcher's guide to omic fundamentals with these activities:
Review Basic Statistics and Probability
Strengthen your foundation by revisiting essential statistical and probability concepts used in omic data analysis.
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  • Review your notes or textbooks on basic statistics and probability.
  • Solve practice problems to test your understanding.
Compile a Glossary of Omic Data Terminology
Deepen your understanding of omic data by creating a comprehensive glossary of key terms and concepts.
Show steps
  • Review course materials and identify unfamiliar terms.
  • Search for definitions in textbooks, online resources, and scientific papers.
  • Organize the terms into a structured glossary, such as a spreadsheet or wiki.
Explore the Bioconductor Project
Reinforce your understanding of omic data workflows by exploring the widely-used Bioconductor Project.
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  • Visit the Bioconductor website and browse the available packages.
  • Identify a package relevant to your research interests.
  • Follow the tutorials provided for the package to gain hands-on experience.
Four other activities
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Show all seven activities
Form a Study Group for Omic Data Analysis
Enhance your learning by collaborating with classmates and discussing omic data analysis concepts and challenges.
Show steps
  • Identify classmates who share your interests and learning goals.
  • Establish a regular meeting schedule and meeting format.
  • Prepare discussion topics and assignments to facilitate knowledge sharing.
Practice Data Wrangling with R and Bioconductor
Solidify your data manipulation skills by practicing with real-world genomic datasets using R and Bioconductor.
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  • Find a public genomic dataset that aligns with your interests.
  • Load the data into R using appropriate Bioconductor packages.
  • Perform data cleaning and preprocessing steps.
  • Explore the data using visualizations and statistical tests.
Attend a Bioinformatics Workshop
Gain practical experience and connect with experts by attending a workshop focused on omic data analysis techniques.
Browse courses on Genomics
Show steps
  • Search for upcoming bioinformatics workshops in your area.
  • Select a workshop that aligns with your interests and skill level.
  • Actively participate in the workshop and engage with the instructors.
Develop a Data Analysis Plan for an Omic Data Project
Apply your knowledge by creating a detailed plan for analyzing a real-world omic dataset, ensuring a structured and efficient approach.
Show steps
  • Define the research question and objectives.
  • Select appropriate data analysis methods and tools.
  • Outline the steps of the analysis workflow.
  • Consider potential challenges and mitigation strategies.

Career center

Learners who complete Researcher's guide to omic fundamentals will develop knowledge and skills that may be useful to these careers:

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