We will explain how to start with raw data, and perform the standard processing and normalization steps to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment. We start with RNA-seq data analysis covering basic concepts of RNA-seq and a first look at FASTQ files. We will also go over quality control of FASTQ files; aligning RNA-seq reads; visualizing alignments and move on to analyzing RNA-seq at the gene-level: counting reads in genes; Exploratory Data Analysis and variance stabilization for counts; count-based differential expression; normalization and batch effects. Finally, we cover RNA-seq at the transcript-level: inferring expression of transcripts (i.e. alternative isoforms); differential exon usage. We will learn the basic steps in analyzing DNA methylation data, including reading the raw data, normalization, and finding regions of differential methylation across multiple samples. The course will end with a brief description of the basic steps for analyzing ChIP-seq datasets, from read alignment, to peak calling, and assessing differential binding patterns across multiple samples.
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 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 course 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.
The courses in this series will be released sequentially each month and are self-paced:
This class was supported in part by NIH grant R25GM114818.
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EdX est une plateforme d'apprentissage en ligne (dite FLOT ou MOOC). Elle héberge et met gratuitement à disposition des cours en ligne de niveau universitaire à travers le monde entier. Elle mène également des recherches sur l'apprentissage en ligne et la façon dont les utilisateurs utilisent celle-ci. Elle est à but non lucratif et la plateforme utilise un logiciel open source.
EdX a été fondée par le Massachusetts Institute of Technology et par l'université Harvard en mai 2012. En 2014, environ 50 écoles, associations et organisations internationales offrent ou projettent d'offrir des cours sur EdX. En juillet 2014, elle avait plus de 2,5 millions d'utilisateurs suivant plus de 200 cours en ligne.
Les deux universités américaines qui financent la plateforme ont investi 60 millions USD dans son développement. La plateforme France Université Numérique utilise la technologie openedX, supportée par Google.