Introduction to Bioconductor
offered by Harvard University

Private University
3 Campuses
Estd. 1636

Introduction to Bioconductor
at
Harvard University
Overview

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

Duration	4 weeks
Total fee	₹18,186
Mode of learning	Online
Difficulty level	Intermediate
Official Website	Go to Website
Course Level	UG Certificate

Introduction to Bioconductor
at
Harvard University
Highlights

Earn a certificate after completion of the course

Introduction to Bioconductor
at
Harvard University
Course details

Skills you will learn

R Software Testing Data Science Statistics Data analysis Engineering Design

What are the course deliverables?

What we measure with high-throughput technologies and why
Introduction to high-throughput technologies: Next generation sequencing and Microarrays
Preprocessing and normalization
The Bioconductor Genomic Ranges utilities
Genomic annotation

More about this course

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
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 our students we have 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 a biologist 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 course we'll 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.

Introduction to Bioconductor
at
Harvard University
Curriculum

Data Science, Biostatistics, Data Analysis, R, Statistics, Biology

Introduction to Bioconductor
at
Harvard University
Faculty details

Rafael Irizarry

Designation : Professor of Biostatistics, T.H. Chan School of Public Health

Michael Love

Designation : Assistant Professor, Departments of Biostatistics and Genetics, UNC Gillings School of Global Public Health

Vincent Carey

Designation : Professor of Medicine (Biostatistics) in the Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School