sequence data analysis

Short Name
Module Code
Module Coordinator
  • Andreas Gogol-Döring
  • Andreas Gogol-Döring
Short Description

The analysis of big amounts of DNA or RNA sequences generated by high-throughput-sequencing methods.

Learning Objectives

The students know basic methods for processing high-throughput-sequencing (HTS) data of different applications. They are able to analyze HTS data, independently or in a team, using self -developed or common software tools, and to correlate the data to each other and to information from data bases, in order to achieve biologically relevant results. Moreover, they are able to explain properly and comprehensively the methods they used for the data analysis as well as the obtained results.

  • Basic processing steps for HTS data, like quality control, read mapping, etc.
  • Various applications of HTS, e.g., RNA-Seq, ChIP-Seq, etc.
  • Integration and correlation of different data sets
  • Methods of data interpretation with the aid of information from public data bases
  • Independent processing of HTS data in a data analysis project.
Duration in Semester
Instruction Language
Total Effort
6.0 CrP; an estimated 180 hours, of which approximately 60 are spent in class.
Weekly School Hours
Method of Instruction

seminar 2 SWS, practical course 2 SWS

Requirements for the awarding of Credit Points

Examination prerequisite: Requirements for Test Participation: Regular (at least 70% of the sessions) and active participation. Autonomously conducting a data analysis project.

Examination: Credit Point Reward Requirement: oral exam including a presentation of the analysis results.

Evaluation Standard

according to examination regulations (§ 9)