This page contains the information and materials of the OMICS workshop.

The idea of this workshop is to make the students familiar with the basic tools to perform different OMICS analyses. We will start with a general introduction to Python and R allowing the students to write their own programs for their analyses. Additionally we will provide an overview over existing programs in the area of differential expression and orthology detection.

Part 1: Linux and Python (20-24.11)

Responsible: Elias Dohmen, Carsten Kemena

Day	Lecture	Short Description	Exercise	Data (needed for Exercises)	Materials
1	Linux Introduction	Introduction to basic Linux commands	Exercise Solutions	Data	Python referenece documentation Python style guide General good programming practices Project
2	Python 1	Basics of programming in Python (variables, loops, compare basic conditions)	Exercise
3	Python 2	Strings and functions are introduced and how they are used to read/write from/to files	Exercise
4	Python 3	Further important pre-existing data types like dict, set etc. are introduced	Exercise	Data
5	Python 4	ErrorHandling and modules from the standard library are introduced	Exercise	Data

Part 2: Statistics and Visualization in R (27-28.11)

Responsible: Evelien Jongepier

Requirements: Basic stats knowledge

Day	Lecture	Short Description	Exercise	Data (needed for Exercises)	Materials
6	R basics lecture	R syntax and data types	R basics exercises		CRAN RStudio Cheatsheet
7	R plots lecture	R data analyses and visualization	R plots exercises	GEOdata

Part 3: Transcriptomics (29-30.11)

Responsible: Evelien Jongepier

Requirements: Basic Bash knowledge, Basic R knowledge

Day	Lecture	Short Description	Exercise	Data (needed for Exercises)	Materials
8	Transcr lecture	Transcriptome assembly using Trinity	Transcr exercises	RNAseq data
9	DE lecture	Differential expression analyses using DESeq2	DE exercises	DE data

Part 4: Proteomics and Orthology (4-6.12)

Responsible: Elias Dohmen, Evelien Jongepier, Carsten Kemena

Requirements: Basic Linux knowledge, Basic R knowledge for the enrichment analysis

Day	Lecture	Short Description	Exercise	Data (needed for Exercises)	Materials
10	Orthology	Orthology clustering in the context of genomes and proteomes.	Exercise Orthology		The Pfam database Domain arrangement evolution
11	Protein Domains	Proteomics with Protein Domains as Markers for Protein Orthology.	Exercise Domains	results_Danio_rerio.pfam
12	Enrichment lecture	GO-term analysis and visualization	Enrichment exercises

Part 5: Bring your own data (7-8.12)

This section is dedicated to helping you with problems you have with your own data. You can bring your own data and we will help you with questions concerning the topics we covered in this course.