Harvard University - Introduction to Bioconductor

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Introduction to Bioconductor
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edX
Overview

The structure, annotation, normalization, and interpretation of genome scale assays.

Duration	11 hours
Start from	Start Now
Total fee	Free
Mode of learning	Online
Schedule type	Self paced
Difficulty level	Intermediate
Official Website	Explore Free Course
Credential	Certificate

Introduction to Bioconductor
at
edX
Highlights

28% got a tangible career benefit from this course
Instructors -
Rafael Irizarry, Michael Love & Vincent Carey
Effort - 2?4 hours per week
FREE
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Introduction to Bioconductor
at
edX
Course details

Skills you will learn

R Statistics Software Testing

Who should do this course?

This course is designed for statisticians as well as biologists

More about this course

It starts with an introduction to the relevant biology, explaining what we measure and why. Then it focuses on the two main measurement technologies: next generation sequencing and microarrays. It then moves on to describing how raw data and experimental information are imported into R and how it uses Bioconductor classes to organize these data, whether generated locally, or harvested from public repositories or institutional archives. Genomic features are generally identified using intervals in genomic coordinates, and highly efficient algorithms for computing with genomic intervals will be examined in detail. Statistical methods for testing gene-centric or pathway-centric hypotheses with genome-scale data are found in packages such as limma, some of these techniques will be illustrated in lectures and labs. Given the diversity in educational background of the students it has divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third coursem it will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.