Metagenomics

Next iteration of this course will be April 2026

https://teaching.genetik.biologie.uni-muenchen.de/teaching/courses/courses/metagenomics 

This is an advanced R course on 16S amplicon based metagenomic analysis. It is designed for master students in their second semester or above. The course consists of a practical and a seminar. The seminar will cover topics related to plant microbe interactions, community ecology, analysis of sequencing data, and ecological concepts.

In the practical you will be handed a dataset from a large experiment. You will learn how to deal with short read data in R, how to infer amplicon variants and how to assign amplicon variants to a taxonomic unit. You will then proceed to further analyzing microbial communities using more advanced statistics to draw ecologically meaningful conclusions. Finally, you will interpret and discuss your results with the other members of the course. 

The course has been designed for remote-learning and has previously been online-only, however the seminar benefits from in-person discussion, and will be held in person.

P16 - Computer

Inhalt

In den Übungen werden grundlegende Kenntnisse der Computergestützen Biologie, insbesondere Datenauswertung mit R, vermittelt.

Qualifikationsziele

Die Studierenden haben einen elementaren Überblick über die Funktionsweise von Computern im wissenschaftlichen Kontext, Grundlegende Programierkenntnisse, sowie ein Verständnis für Prinzipien der Datenwisschenschaften. 

Sie lernen beispielhaft Einsatzmöglichkeiten dieser computergestützten Methode in der Biologie und können einfache biologische Probleme, für die Programmierkenntnisse notwendig sind, unter Anleitung lösen.

LSF Vorlesung:

https://lsf.verwaltung.uni-muenchen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=1135714&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung

LSF Übung:

https://lsf.verwaltung.uni-muenchen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=1135715&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung

Introduction to Python programming in genomic research

General information

This course is a combined lecture, seminar and practical course. It is open for master and Phd students. The course is interdisciplinary and not linked to any module specifically.

Python is one of many programming languages. Different than other it often uses English keywords for coding than punctuation. With this freeware (open-source-basis) you are able to work quickly and integrate systems more effectively.

In this course you will learn what the language “Python” is about.

Moreover, you practice how to write first scripts, how to define functions and how to use them. Additionally, in the practical part you will analyse genomic datas, use models and packages for gene analysis as well as visualize data frames.

Workload

Preparation for seminar: 45 h

Course attendance: 7.5 h x 8 = 60 h

Writing final report: 7.5 h x 10 = 75 h



Practical course: Analysis of High-Throughput Genomic Data

General information

This course is a combined seminar and practical course. It is open for master and Phd students. The course is interdisciplinary and not linked to any module specifically.

IMPORTANT: This course is in presence but includes online interaction with a high-throughput server at the LMU. 


Course schedule
  • will be announced (soon)       

  • 13.05. - 30.05. 
      from 10am - 4pm        
     Course days incl. final presentation

     13.05. - 23.05.25 in room G00.037
     26.05. - 30.05.25 in room G00.039
    
  • until 30.05.25                                              
     Finish projects and write report

This course will be held in person. In addition the participants will connect to a compute server in the university.


Content

New high-throughput DNA sequencing technologies allow us to decipher virtually every genome we are interested in. This master course will teach the basic principles of data analysis and how to analyze genomic data in particular including hands-on experience.

  • Learn the current genomic technologies, the data they generate, and how to analyze those
  • Obtain skills on basic Linux commands


Workload

Preparation for seminar: 45 h

Course attendance: 7.5 h x 8 = 60 h

Writing final report: 7.5 h x 10 = 75 h