MLU
INF.07651.02 - Computational Transcriptomics (Complete module description)
Original version English
INF.07651.02 5 CP
Module label Computational Transcriptomics
Module code INF.07651.02
Semester of first implementation
Faculty/Institute Institut für Informatik
Module used in courses of study / semesters
  • Biodiversity Sciences (MA120 LP) (Master) > Biologie BiodiversityMA120, Version of accreditation valid from SS 2021 > Project modules offered by the Institute of Computer Science (Nat Sci III)
Responsible person for this module
Further responsible persons
Prof. Dr. Ivo Große
Prerequisites
Statistical Data Analysis and Machine Learning in Biodiversity Research
Skills to be acquired in this module
  • Acquire an understanding of the fundamental principles and techniques of computational transcriptomics.
  • Being capable to critically evaluate and select appropriate computational transcriptomics approaches for addressing specific scientific problems transcriptomics.
  • Gaining the ability to effectively communicate and explain computational transcriptomics approaches to both technical and non-technical audiences including their limitations and implications of their findings.
  • Having competence of developing and advancing these approaches further for application to new and emerging scientific challenges in transcriptomics.
Module contents
Technology und data acquisition
Popular distance and dissimilarity measures and hierarchical clustering
Partitioning clustering and k-means algorithm
Expectation-maximization algorithm und Gibbs-sampling algorithm for Gaussian mixture models
Prediction of differentially expressed genes, exons, and isoforms
Forms of instruction Lecture (2 SWS)
Course
Exercises (2 SWS)
Exercises
Languages of instruction German, English
Duration (semesters) 1 Semester Semester
Module frequency jedes Sommersemester
Module capacity unlimited
Time of examination
Credit points 5 CP
Share on module final degree Course 1: %; Course 2: %; Course 3: %; Course 4: %.
Share of module grade on the course of study's final grade 1
Module course label Course type Course title SWS Workload of compulsory attendance Workload of preparation / homework etc Workload of independent learning Workload (examination and preparation) Sum workload
Course 1 Lecture Vorlesung 2 0
Course 2 Course Selbststudium 0
Course 3 Exercises Übung 2 0
Course 4 Exercises Bearbeiten der Übungsaufgaben 0
Workload by module 150 150
Total module workload 150
Examination Exam prerequisites Type of examination
Course 1
Course 2
Course 3
Course 4
Final exam of module
Aktive Teilnahme an den Übungen, Erfolgreiches Lösen der Übungs- und Programmieraufgaben, Erfolgreiches Vorrechnen und Erklären der Lösungen, 50% der Punkte der Übungsaufgaben
mündl. Prüfung oder Klausur
Exam repetition information
Prerequisites and conditions Prerequisites Frequency Compulsory attendance Share on module grade in percent
Course 1 Summer semester No %
Course 2 Summer semester No %
Course 3 Summer semester No %
Course 4 Summer semester No %