Computational analysis of transcriptomes and proteomes

Title Computational analysis of transcriptomes and proteomes
Sprache/Language englisch
VV-Nr./Course No. 136123
Modulverantwortlich/Responsible Dr. Carsten Kemena
Anbieter/Teachers Prof. Dr. E. Bornberg-Bauer; Dr. M. Harrison; Dr. A. Lange
Typ/Type Seminar und Praktikum
SWS/Semerster periods per week
Arbeitslast(h)/Work load 150
KP/Credit points 5
Zuordnung/Classification Fortgeschrittenen-Modul
Semester/Semester SoSe
Studierende/Students MSc Biowissenschaften
MSc Biotechnologie
MSc Molekulare Biomedizin
Zeit/Date Block 3: 24.06.24-19.07.2024
Ort/Location Hüfferstr. 1
Beginn/Start 24.06.2024 - 10.00
Vorbesprechung/Obligatory pre-meeting
Voraussetzung/Prerequisite keine
Anmeldung/Registration Online-Anmeldung
Leistungskontrollen/Performance assessments To be determined
Termine f. Leistungskontrollen/Date for performance assessments
max. NP/Max. grade points 200
Ziele/Aims The goal of this course is to teach theoretical and practical knowledge about transcriptomic and proteomic analyses
Inhalte/Content I got my sequencing data and now what? I know there are genomes online, but how can I analyse them? How can I organise my data effectively? ... If you are keen to learn the basics of transcriptomics and proteomics, this course will help you along.
In this course we will go through the entire pipeline from genome assembly and annotation to current methods of proteomics and transcriptomics

1. Part: Linux Command Line & Research Data Management
Here, we introduce the Linux command line, which provides a lot of useful text manipulation tools that can be used to get a first impression of your data and prepare it for further analyses. We‘ll use the HPC cluster for these analyses.
Futhermore, we will have a look at good research data management: How to store your data, keeping records of your analysis steps and an overview of your results.

2. Part: Assembly & Annotation
We will assess the quality of the sequencing data and assemble the genomes. Further steps like purging, scaffolding and polishing are going to be introduced.
In a next step, we will annotate genomes and analyse genome and proteome quality.

3. Part: Transcriptomics & Proteomics
During the transcriptomics part, we mainly focus on differential expression analyses.
For the proteomics part we will discuss and apply different analyses like orthology prediction, domain based analyses.

4. Part: Analysing data
Here, you will do an analysis of data using the methods taught in the previous parts.

Methoden/Methods - Linux Command Line
- Orthology prediction using OrthoFinder
- Domain based analyses (e.g. DOGMA, DomRates)
- Differential expression analyses
Berufsrelevante und interdisziplinäre Komponenten/Occupational and interdisciplinary skills
Voraussetzung für/Prerequisite for
Präsenzpflicht/Compulsory presence
Plätze/Number of participants 12
Gruppengröße/Group size
Sonstiges/Further information


Elemente of the module:
Titel/Title Zeit (von...bis)/Time ( Ort(Raum)/Location
Übungen/Practical exercises
Legende: / Legend:

= Modul gehört zum SPP Imoplant / Module is part of the SSP Imoplant
= Modul gehört zum SPP Evolution /Module is part of the SSP Evolution
= Modul gehört zum SPP Bioanalytics and Biochemistry /Module is part of the SSP Bioanalytics and Biochemistry
= Modul gehört zum SPP Neuroscience and Behaviour /Module is part of the SSP Neuroscience and Behaviour
= Modul gehört zum SPP Quantitative Cell Biology /Module is part of the SSP Quantitative Cell Biology