Postgraduate course: Multiplatform-omics data analysis in nutrigenomics studies, 7-9 September 2020, on-line

Introduction
Multiple-omics technologies to obtain a comprehensive readout of a particular biological system. Multi-omics studies require a rigorous computational processing and analysis, which are the cornerstones that enable the subsequent data integration and biological interpretation. In this course we will cover metagenomics, RNA-seq and metabolomics data analysis with the aim of retrieving molecular identities from raw data.

Aim
The aim of this course is to cover the main basics of multi-omics analysis, from raw data to pathway analysis.

Registration
Selection for the course will be made based on the following criteria:
* This course is exclusively organised for attendees from NuGO members.
* PhD or Postdoc (<3 years after defence of thesis) of a NuGO member organisations.
* For some parts of the course R language knowledge is adviced.
* One - two candidate per member organisation, depending on the number of applications.

Candidate’s research topic should be related to the course topic. Therefore the registration email should describe:
* Title of your PhD research topic
* Background in ~omics data
* Which software programme(s) you are using to work with
* Name of NuGO research group and name of supervisor.

For registration please send an email including the above mentioned details to: Dr. Manuel Suárez Recio: manuel.suarez[at]urv.cat
Deadline for registration is 20 August 2020.

Programme:

First Day: 7 September 2020

Session 1: From raw data to ID: data processing and analysis of multi-omics data
Session coordinator: Dr. Xavier Domingo-Almena
Teachers: Dr. Xavier Domingo-Almenara; Dr. Adrià Ceretó-Massagué; Dr. Sara Martínez and Dr. Núria Canela

Multi-omics studies involve the integration of different -omics technologies to obtain a comprehensive readout of a particular biological system. Multi-omics studies require a rigorous computational processing and analysis, which are the cornerstones that enable the subsequent data integration and biological interpretation. In this course we will cover metagenomics, RNA-seq and metabolomics data analysis with the aim of retrieving molecular identities from raw data. The course will also include hands-on sessions on the use of software like Qiime or XCMS, and processing workflows in R. Particularly, and with an ever-growing research community, metabolomics is in high demand by biologists and clinicians thanks to its unique ability to pinpoint molecular mechanism. This is why the morning session of the course will cover all the metabolomics workflow steps, ranging from peak peaking to metabolite annotation and identification, using state-of-the-art computational software and spectral databases.

09:00-09:30 Introduction to metabolomics
09:30-10:15 Data processing and statistical analysis
10:15-11:00 Hands-on data processing and statistical analysis
11:00-11:10 Coffee break
11:10-12:00 Metabolite annotation and identification
12:00-12:45 Hands-on metabolite annotation and identification

12:45-13:30 Lunch break

13:30- 15:20 Introduction to metagenomics data analysis
15:20-15:30 Coffee break
15:30- 17:15 Introduction to RNA-seq data analysis
17:15- 17:30 Q&A

Second Day: 9 September 2020

Session 2: Pathway analysis
Session coordinator: Dr. Susan Coort

09:00-09:45 Introduction on biological pathways databases and pathway analysis
09:45-11:00 Hands-on pathway analysis (including break out sessions to discuss the results)
11:00-11:10 Coffee break
11:10-11:40 Introduction in network analysis for multi-omics data
11:40-12:30 Hands-on network analysis in Cytoscape using several apps (the selected apps depend on the dataset used)
12:30-12:45 Quiz to highlight the important aspects of multi-omics pathway and network analysis

12:45-13:30 Lunch break

Session 3: Data integration tools
Session coordinator: Dr. Kathryn Burton

13:30- 14:30 Short introduction data integration for multiomics data and existing multiomics data integration tools (presentation format with opportunity for questions during last 10-15min)

14:30- 16:00 Group work activity (5-6/group)- each group will receive a dataset together with code for one of the discussed data integration tools and a series of questions to address. The group will work together to make a first attempt to evaluate the test dataset and prepare a short presentation that will include the results as well as a critique on the tools.
(group discussion via break-out rooms, support to be offered via Moodle chat)

16:00- 17:15 Group presentations- each group will present the use of their assigned data integration tool for their data and respond to their questions (10 min per presentation, 5min questions)

17:15- 17:30 Wrap up- opportunity for participants to ask questions or join a break-out room to debrief with their group.