Course: Bioinformatics and Computational Biology Solutions Using R and Bioconductor

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Syllabus

Course Description

Covers the basics of R software and the key capabilities of the Bioconductor project (a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology and rooted in the open source statistical computing environment R), including importation and preprocessing of high-throughput data from microarrays and other platforms. Also introduces statistical concepts and tools necessary to interpret and critically evaluate the bioinformatics and computational biology literature. Includes an overview of of preprocessing and normalization, statistical inference, multiple comparison corrections, Bayesian Inference in the context of multiple comparisons, clustering, and classification/machine learning.

Course Objectives

Upon successful completion of this course, students will be able to: 1) Understand the basics of how microarray technology works; 2) Understand and critique existing methodology for the analysis of microarray data; 3) Write R code to import and analyze microarray data.

Prerequisites

140.621-624 or equivalent

Readings

Bioinformatics and Computational Biology Solutions Using R and Bioconductor edited by Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit

Cartoon Guide to Genetics by Larry Gonick

Course Requirements

Student evaluation will be based on data analysis homework assignments and a final project. Students who want to learn the concepts without programming may take the class pass/fail and perform a literature review for a final project.