Proseminar 'Proteins and Disease' SS 2014

Type Seminar (2 SWS)
ECTS 3 (FPO 2007) 4 (FPO 2013)
Lecturer Burkhard Rost
Time Monday, Group 1: 12:00 - 13:30; Group 2: 13:30 - 15:00
Room MI 01.09.034
Language English


Application / Registration

Application is organised centrally for all bioinformatics seminars. After you have been assigned to our seminar, we will distribute the topics.


Topics related to the research interests of the group: protein sequence analysis, sequence based predictions, 
protein structure prediction and analysis; interaction networks. The list below shows topics from the last seminar WS12/13. The topics for the new seminar will be updated, but you can expect similar talks.


tba in Room MI 01.09.034

The rules and hints for preparation of the seminar discussed  in the pre-meeting are also summarised in our Checklist and on these slides.

Slides from the pre-meeting. Topics and schedule have been moved to the table below. Timetable last updated at Feb. 24th 

Schedule for SS 14

Date Topic Supervisor Slot1 Slot2
Apr. 7th

Potassium channels: Structure and function

Kloppmann Ammer Scheller
Apr. 14th

Sequence alignment: Local and global

Richter Wörheide Madin
Apr. 21st Easter / Ostern      
Apr. 28th

PolyPhobius: Prediction of transmembrane

regions in protein sequences

Kloppmann Müller Wilzbach
May 5th

Multiple sequence alignment

Hecht Ansari Lassauzaie
May 12th

Sequence alignment and searches:

Heuristic methods

Richter Yerlikaya Barias
May 19th

Sequence searches using profiles

Hecht Wirth Weck
May 26th

Biological databases

Cejuela Ofner Maier
Jun 2nd

PiNat: Assessment of protein networks

Goldberg Rasp Schneider
Jun 9th Whitsun / Pfingsten      
Jun 16th

Conditional random Fields for

named-entity recognition

Cejuela Hartebrodt Daon
Jun 23rd

Predicting subcellular localization using

functional hierarchies

Goldberg Zehntner Hölzlwimmer
Jun 30th

SignalP 4: Predicition of signal peptides

in protein sequences

Kloppmann Spaczek Sturm
Jul 7th

Introduction to cheminformatics

Richter Gergen Eis






Sequence alignment: local and global

Dr. Lothar Richter

Finding an alignment of two protein sequences is the basis of all techniques to infer knowledge by homology. This talk shall review well-known local and global alignment methods (Smith-Waterman, Needleman-Wunsch).



Sequence alignment and searches: heuristic methods

Dr. Lothar Richter

This talk shall explain the heuristic approximations made to speed up sequence alignment and sequence searches (BLAST, FASTA).


Korf, Ian et al. BLAST. O'Reilly Media, Inc. 2003

Introduction to Cheminformatics

Dr. Lothar Richter

This talk shall explain basic concepts and tools of cheminformatics.


A. Leach & V. Gillet, An Introduction to Chemoinformatics


Potassium channels: structure and function

Dr. Edda Kloppmann

Potassium channels comprise a large and important group of integral membrane receptors and are the most widely distributed class of ion channels. The 2003 Nobel Prize for Chemistry was awarded jointly to Roderick MacKinnon and Peter Agre "for discoveries concerning channels in cell membranes". Rod MacKinnon in particular worked on potassium channels. This talk shall introduce the structure, function and molecular mechanism of potassium channels.


  • Alberts
  • ...


PolyPhobius: Prediction of transmembrane in protein sequences

Dr. Edda Kloppmann

PolyPhobius uses hidden markov models (HMMs) to predict transmembrane helices in protein sequences. This talk shall introduce transmembrane proteins, HMMs and sequence-based transmembrane helix prediction at the example of PolyPhobius.


  • Alberts
  • Bioinformatics
  • Lukas Käll, Anders Krogh and Erik Sonnhammer. An HMM posterior decoder for sequence feature prediction that includes homology information. Bioinformatics, 21 (Suppl 1):i251-i257, June 2005.


SignalP 4: Prediction of signal peptides in protein sequences

Dr. Edda Kloppmann



  • Alberts
  • Bioinformatics
  • Thomas Nordahl Petersen, Søren Brunak, Gunnar von Heijne & Henrik Nielsen. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nature Methods8:785-786, 2011


PiNat: Assessment of protein networks

Tatyana Goldberg

A platform for data integration shall be presented in this talk. The platform generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment.


Predicting subcellular localization using functional hierarchies

Tatyana Goldberg

Identification of a protein’s subcellular localization is an important step towards elucidating its function. In this seminar, a machine-learning-based method for predicting localization in prokaryotes and eukaryotes shall be presented. The method is original in incorporating a hierarchical ontology of subcellular localization classes. Furthermore, it uses predicted features like the secondary structure of a protein and evolutionary information in form of sequence profiles to improve prediction accuracy considerably.


  • Alberts B, Bray D, Lewis J, Raff M, Roberts K, Watson JD. Molecular Biology of the Cell. New York: Garland Science, 2002

Conditional Random Fields for Named-Entity Recognition

Juan Miguel Cejuela

Conditional random fields (CRF) are popular methods in named-entity recognition (NER) and generally in sequential labeling tasks. This talk shall present the CRF models and their advantages in comparison to other popular models like hidden markov models (HMMs). An example case will focus on the recognition of protein names.

  • Andrew McCallum Charles Sutton. An Introduction to Conditional Random Fields for Relational Learning. In Lise Getoor and Ben Taskar, editors, Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning), chapter 4
  • John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proceedings of the Eighteenth International Conference on Machine Learning, ICML ’01,

Biological Databases

Juan Miguel Cejuela

Huge volumes of primary data are archived in numerous open-access databases, and with new generation technologies becoming more common in laboratories. This seminar shall give an overview of different Databases, how to access them and problems associated.

  • Arthur M. Lesk. Introduction to bioinformatics (Third Edition) Oxford University Press

Sequence searches using profiles (PSI-Blast et al.)

Maximilian Hecht

This talk shall explain why and how profiles help in searching sequence databases and how the profile searches work technically.


Multiple sequence alignment

Maximilian Hecht

This talk shall explain the methods used to generate multiple sequence alignments, the complexity of the problem and the approximations made.


  • A.M Lesk Bioinformatik: Eine Einführung. Spektrum Akademischer Verlag, 2002.
  • Thompson JD, Higgins DG, Gibson TJ (1994). CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.Nucleic Acids Res 22: 4673–4680.
  • Edgar RC (2006). Multiple Sequence Alignment. Curr opin struct biol 16: 368-373.