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Data Analysis for Genomics

Rafael Irizarry, Vincent Carey, and Michael Love

Advances in genomics have triggered fundamental changes in medicine and research. Genomic datasets are driving the next generation of discovery and treatment, and this series will enable you to analyze and interpret data generated by modern genomics technology.

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Advances in genomics have triggered fundamental changes in medicine and research. Genomic datasets are driving the next generation of discovery and treatment, and this series will enable you to analyze and interpret data generated by modern genomics technology.

Using open-source software, including R and Bioconductor, you will acquire skills to analyze and interpret genomic data. These courses are perfect for those who seek advanced training in high-throughput technology data. Problem sets will require coding in the R language to ensure mastery of key concepts. In the final course, you’ll investigate data analysis for several experimental protocols in genomics.

Enroll now to unlock the wealth of opportunities in modern genomics.

What you'll learn

  • How to bridge diverse genomic assay and annotation structures to data analysis and research presentations via innovative approaches to computing
  • Advanced techniques to analyze genomic data.
  • How to structure, annotate, normalize, and interpret genome-scale assays.
  • How to analyze data from several experimental protocols, using open-source software, including R and Bioconductor.
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What's inside

Three courses

Introduction to Bioconductor

(12 hours)
We begin with an introduction to the relevant biology, explaining what we measure and why. Then we focus on the two main measurement technologies: next generation sequencing and microarrays. We then move on to describing how raw data and experimental information are imported into R and how we use Bioconductor classes to organize these data, whether generated locally, or harvested from public repositories or institutional archives.
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Case Studies in Functional Genomics

(15 hours)
We will explain how to perform the standard processing and normalization steps, starting with raw data, to get to the point where one can investigate relevant biological questions.
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Advanced Bioconductor

(12 hours)
In this course, we begin with approaches to visualization of genome-scale data, and provide tools to build interactive graphical interfaces to speed discovery and interpretation. Using knitr and rmarkdown as basic authoring tools, the concept of reproducible research is developed, and the concept of an executable document is presented.
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Time
2340 hours
Via
edX
Institution
Harvard University
Instructors
Rafael Irizarry
Vincent Carey
Michael Love
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