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Module Guide before WS 2016/17

Modul CS1400-KP04, CS1400

Introduction to Bioinformatics (EinBioinfo)

Duration:


1 Semester
Turnus of offer:


each winter semester
Credit points:


4
Course of studies, specific field and terms:
  • Bachelor MES 2020 (optional subject), computer science / electrical engineering
  • Bachelor Computer Science 2019 (compulsory), Canonical Specialization Bioinformatics and Systems Biology, 1st semester
  • Bachelor Computer Science 2019 (optional subject), Introductory Module Computer Science, 1st semester
  • Bachelor MLS 2018 (compulsory), life sciences, 5th semester
  • Bachelor MES 2014 (optional subject), computer science / electrical engineering, 5th semester
  • Bachelor Computer Science 2016 (optional subject), Introductory Module Computer Science, 1st semester
  • Bachelor Computer Science 2016 (compulsory), Canonical Specialization Bioinformatics, 1st semester
  • Bachelor MLS 2016 (compulsory), life sciences, 5th semester
  • Bachelor Medical Informatics 2014 (compulsory), medical computer science, 3rd semester
  • Bachelor Computer Science 2014 (compulsory), specialization field bioinformatics, 1st semester
  • Bachelor Medical Informatics 2011 (compulsory), medical computer science, 3rd semester
  • Bachelor MLS 2009 (compulsory), life sciences, 5th semester
  • Bachelor MES 2011 (optional subject), medical engineering science, 3rd or 5th semester
  • Bachelor Computer Science 2012 (compulsory), specialization field bioinformatics, 1st semester
Classes and lectures:
  • Introduction to Bioinformatics (lecture, 2 SWS)
  • Introduction to Bioinformatics (exercise, 1 SWS)
Workload:
  • 45 Hours in-classroom work
  • 20 Hours exam preparation
  • 55 Hours private studies
Contents of teaching:
  • Life, Evolution & the Genome
  • Sequence assembly - Industrial reading of genetic information
  • DNA sequence models & hidden markov models
  • Viterbi-Algoritm
  • Sequence alignment & dynamic programming
  • Unsupervised data analysis (k-means, PCA, ICA)
  • DNA microarrays & GeneChip technologies
Qualification-goals/Competencies:
  • Students are able to explain the basic concepts of coding, transcription and translation of information in living beings.
  • They are able to explain how a solution of the shortest common superstring problem can be estimated with a simple greedy algorithm.
  • They are able to create a Markov chain or a Hidden Markov Model (HMM) for a given modelling problem.
  • They are able to give examples on how to solve a problem using dynamic programming.
  • They are able to implement the introduced algorithms (in Matlab)
  • They are able to use unsupervised learning methods and they are able to interpret the results.
  • They are able to explain basic Microarray-and DNA-Chip-Technologies.
Grading through:
  • portfolio exam
Responsible for this module:
  • Prof. Dr. rer. nat. Amir Madany Mamlouk
Teachers:
  • Prof. Dr. rer. nat. Amir Madany Mamlouk
Literature:
  • H. Lodish, A. Berk, S. L. Zipursky and J. Darnell: Molekulare Zellbiologie - Spektrum Akademischer Verlag, 4. Auflage, 2001, ISBN-13: 978-3827410771
  • A. M. Lesk: Introduction to Bioinformatics - Oxford University Press, 3. Auflage, 2008, ISBN-13: 978-0199208043
  • R. Merkl and S. Waack: Bioinformatik Interaktiv: Grundlagen, Algorithmen, Anwendungen - Wiley-VCH Verlag, 2. Auflage, 2009, ISBN-13: 978-3527325948
  • M. S. Waterman: Introduction to Computational Biology - Chapman and Hall, 1995
Language:
  • offered only in German
Notes:

For students of the master programme Infection Biology, this is not a stand-alone module, but rather part of the module CS4011.

Prerequisites for attending the module:
- None


Computer Science students get a B certificate.

Letzte Änderung:
20.8.2021