Full details and the application form at: https://elixir-iib-training.github.io/website/2017/09/27/RNA-seq-Salerno.html
IMPORTANT DATES for this Training course:
- Deadline for applications: 11 Sep 2017
- Course date: 27-29 Sep 2017
VENUE: Campus di Fisciano, University of Salerno, Via Giovanni Paolo II n. 132, 84084 Fisciano (SA), Italy.
FEE: The fee will cover attendance to lectures and practicals, coffee breaks, lunches and dinners. The participants are expected to pay their own travel and accommodation costs:
- candidates from a bioinformatics lab from one of the ELIXIR-IIB member institutions (see the list at the bottom): 120 euros
- other candidates: 160 euros
A maximum of 25 candidates will be accepted. Selection will start on July 20th and those with an adequate profile will be accepted immediately.
- Fabrizio Ferré - Dept. of Pharmacy and Biotechnology, University of Bologna, Italy.
- Loredana Le Pera - Center for Life Nano Science@Sapienza, IIT, Roma, Italy.
- Giorgio Giurato - Lab. of Molecular Medicine and Genomics and Genomix4Life srl, University of Salerno.
- Allegra Via - ELIXIR-IIB Training Coordinator, IBPM-CNR, Italy.
- Anna Marabotti (ELIXIR-IIB Training Team and University of Salerno, Italy)
- Roberto Tagliaferri (University of Salerno, Italy)
- Alessandro Weisz (Lab. of Molecular Medicine and Genomics, Department of Medicine, University of Salerno, Italy)
- Loredana Le Pera (ELIXIR-IIB Training Team and CLNS@Sapienza, IIT, Italy)
- Allegra Via (ELIXIR-IIB Training Coordinator, IBPM-CNR, Italy)
Next-generation sequencing (NGS) of RNA libraries (RNA-Seq) has become increasingly common and it largely replaced microarray technology for gene expression profiling. The aim of this course is to get a deeper understanding of RNA-Seq experiments, providing a theoretical introduction to the data processing steps, together with practical sessions illustrating the use of the most popular data analysis tools. The classroom size is limited to 25 participants to optimize the learning and the interaction with the instructors. Starting from the raw sequenced data coming from different phenotypical samples (e.g disease vs healthy control samples), genes which are differentially expressed between the two conditions are determine. Some strategies are illustrated for detecting alternative splicing products, predicting novel isoforms and gene fusion events. Some methods for downstream analysis are described to give insight into how biological knowledge can be generated from RNA-Seq experiments. A lecture on single-cell RNA-Seq will provide an idea of how transcriptome data from individual cells is now emerging as a powerful tool, allowing the study of cell-to-cell gene expression heterogeneity in the same tissue.
Students will gain an understanding of:
- experimental design
- quality control
- theoretical principles of RNA-Seq data analysis process
- how to identify differentially regulated genes
- how to identify alternative splicing events
- how to identify gene fusion events
- multiple testing correction
- biological interpretation of RNA-seq data
This course is aimed at PhD students and young researchers in the life science and computational biology field who are planning to use RNA-seq data and are looking for the best practices to analyze these types of data. Basic knowledge of UNIX/Linux operative system and the BASH command line is desirable; however, on the first day of the course, a practical tutorial will provide an introduction about shell usage and common commands. To apply for the course please fill in the application form, which includes a questionnaire; participants will be selected according to their profile and to their answers to the questionnaire.
After this course participants should be able to:
- understand the importance of RNA-Seq experimental design
- assess the quality of data
- preprocess data
- align data to a reference genome/transcriptome
- perform a complete analysis of RNA-Seq data (Tuxedo Suite)
- estimate known gene and transcript expression
- discover alternative splicing events and novel isoforms
- discover fusion gene events
- perform differential expression analysis
- perform functional enrichment analysis
- summarize and interpret the RNA-seq analysis results
A basic understanding of molecular biology and transcriptomics is assumed. A basic knowledge of Linux (shell usage, common commands) and familiarity with elementary statistics are beneficial, but not essential.