INF.07652.03 - Computational Molecular Phylogenetics (Complete module description)

INF.07652.03	5 CP
Module label	Computational Molecular Phylogenetics
Module code	INF.07652.03
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 molecular phylogenetics. Being capable to critically evaluate and select appropriate computational molecular phylogenetics approaches for addressing specific scientific problems in molecular phylogenetics. Gaining the ability to effectively communicate and explain computational molecular phylogenetics 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 molecular phylogenetics.
Module contents	Molecular basis of evolution, mutations, selection Models of evolution of DNA sequences (Jukes-Cantor, Kimura, Felsenstein, Hasegawa-Kishino-Yano) Models of evolution of RNA expression profiles (Ornstein-Uhlenbeck) Phylogenetic trees and reconstruction of phylogenetic trees Phylogenetic networks and reconstruction of phylogenetic networks
Forms of instruction	Lecture (2 SWS) Course Exercises (2 SWS) Exercises
Languages of instruction	German, English
Duration (semesters)	1 Semester Semester
Module frequency	jedes Wintersemester
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 independent learning	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	Frequency	Compulsory attendance	Share on module grade in percent
Course 1	Winter semester	No	%
Course 2	Winter semester	No	%
Course 3	Winter semester	No	%
Course 4	Winter semester	No	%