IMPORTANT DATES for this Course   Deadline for applications: April 10th 2017   Course dates: April 17th - April 20th 2017

Candidates with adequate profile will be accepted in the next 72 hours after the application, until we reach 20 participants.

Course description

Overview
High-throughput technologies allow us to detect transcripts present in a cell or tissue.
This course covers practical aspects of the analysis of differential gene expression by RNAseq.
Participants will be presented with real world examples and work with them in the training room,
covering all the steps of RNAseq analysis, from planning the gathering of sequence data to the generation
of tables of differentially expressed gene lists and visualization of results. We we will also cover some of the
initial steps of secondary analysis, such as functional enrichment of the obtained gene lists.

Target Audiences
Life Scientists who want to be able to use NGS data to evaluate gene expression (RNAseq).
Computational researchers that wish to get acquainted with the concepts and methodologies used in RNAseq are also welcome.
Participants are encouraged to bring their own data and will have the opportunity to apply the concepts learned in the course.
Pre-requisites
Familiarity with elementary statistics and a few basics of scripting in R.
Please have a look at the following resources and gauge your ability to use R in statitics at the basic level:
Introduction to Data Science with R
Videos from Coursera's four week course in R
Statistics at Square One - BMJ
Basic Unix command line skills, such as being able to navigate in a directory tree and copy files.
See, for example, "Session 1" of the Software Carpentry training for a Unix introduction
(Shell-novice material from the Software Carpentry Foundation).