Omics
Introduction to the analysis of NGS data
Trainer: Janick Mathys
Goal
This training is an introduction to 'Hands on introduction to ChIP-Seq analysis', Hands-on introduction to NGS variant analysis', and 'RNA-Seq analysis for differential expression'. If you want to follow one of these trainings but you have no experience with NGS data you should follow this introduction.
Objectives
This training is an introduction to a series of trainings on the analysis of Next Generation Sequencing data. This training is intended for newbies to the field and will teach them all background knowledge required to successfully complete the advanced NGS analysis trainings. To this end the training will give an overview of:
- The Illumina platform
- NGS data formats and file handling
- Quality control of NGS data
- Assembly and mapping of NGS data
- Supporting IT: Galaxy and GenePattern platform
Bulk RNASeq: from counts to differential expression
Trainer: Janick Mathys
Objectives
The course will show:
- Tools to generate count files like featureCounts, and htseq count are demonstrated
- Count files from HTSeq-Count, FeatureCounts, Salmon or Kallisto are used to identify differentially expressed genes
Required skills
Experience in basic R programming. If you never worked in R you should attend the Basic statistics in R training first.
Analysis of single cell RNASeq data from 10x Genomics
Teacher: Niels Van Damme, Irina Matetovici, Janick Mathys
Material https://elearning.bits.vib.be/courses/single-cell-rnaseq-analysis/
Objectives
We will discuss some of the advantages and pitfalls of scRNASeq and go through the whole scRNASeq analysis pipeline. We will teach you how to do proper quality control and filtering on gene level and cell level, how to do create tSNE plots, how to get potential markers for a subset of cells...
Required skills
If you don’t have any experience with R, you should follow our R introduction training.
Bring your own data: Yes
Using MOFA for integration of omics data
Teacher: Ricard Argelaguet
Goals
- What kind of preprocessing of the data is required for MOFA?
- How to train MOFA on a multi-omic data set?
- How to interpret the MOFA factors by their loadings, using gene set enrichment or sample ordination?
- How to use MOFA for downstream analyses including regression, classification or clustering?
- How to impute missing values with MOFA?
- How to select the number of factors and compare different MOFA fits?
Required skills
If you don’t have any experience with R, you should follow our R introduction training.
Bring your own data: yes
Functional Plant Bioinformatics (PLAZA)
Teacher: Klaas Vandepoele, Michiel Van Bel
Goals
- Introduction plant comparative genomics (genes and gene families) + introduction PLAZA platform
- Functional characterization of plant genes (Gene families, GO, InterPro)
- Synteny analysis in polyploid cereals
- Gene set analysis for different plant -omics data sets:
- Functional interpretation of RNA-Seq and ChIP-Seq genes using the PLAZA Workbench
- Comparing gene sets between different di/polyploidy crop species
Required skills
Basic knowledge of genes, genomes, transcript profiling, BLAST and homology (no programming skills are required).
FlowSOM for handling cytometry data
Teacher: Sofie Van Gassen
Goals
Participants will learn how to handle cytometry data in R, including quality control, visualization, clustering and comparison of samples. This will be demonstrated on an example dataset, but participants are welcome to bring some of their own fcs files to explore in addition.
Required skills
If you have no experience with R you should follow our R introduction course.
Bring your own data: yes