Proseminar 'Proteins and Disease' SoSe 2017

Type Seminar (2 SWS)
ECTS 3 (FPO 2007) 4 (FPO 2013)
Lecturer Burkhard Rost
Time Monday, 12:00 - 14:00
Room MI 01.09.034
Language English

News

 

 

Meeting time

Presentations

  • Presentations are held in English
  • Students assigned the same topic can present it either in a common presentation of 30 minutes or in two separate presentations, 20 mins each
  • If students give two separate presentations, then they are held after each other and the audience for both presentations remains the same
  • If students share one presentation, then they need to make sure that they speak an equal amount of time

Final report

Report review

  • After the last report is submitted, the students will peer-review each other reports
  • For review, the group presenting first sends its report to the group presenting second. The group presenting second sends the report to the group presenting third and so on
  • Groups will have one week to review the reports which will need to be sent to the supervisors of the corresponding group. For example, the group presenting first sends its review of the group that presented last to the last group's supervisor
  • For guidelines on how to write a report, please see Bioinformatics reviewer guidelines (section: Completing the Referee Report)

 

Application / Registration

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

 

Content

Topics related to the research interests of the group: protein sequence analysis, sequence based predictions, protein structure prediction and analysis and commonly used tools. The list below shows topics from proseminars of the last years and might still be extended if needed.

 

Pre-meeting

Feb 14th, 14:00, Room MI 01.09.034

The rules and hints for preparation of the seminar will be discussed in the pre-meeting and are also summarised in our Checklist (some points apply to the Hauptseminar).

Slides from the pre-meeting

 

Schedule for SoSe 17

Date Topic Supervisor Student 1 Student 2

24.04.

SignalP 4: Prediction of signal peptides in protein sequences

Jonas Reeb

Vincent Roller

Nicolas Goedert

08.05.

PolyPhobius: Prediction of transmembrane in protein sequences

Jonas Reeb

Octavia-Andreea Ciora

 

15.05.

Sequence alignment and searches: heuristic methods

Lothar Richter

Tongyan Wu

 

22.05.

LocTree3: Prediction of subcelluar localization of proteins

Maria Schelling

Alexander Karollus

Thomas Mauermeier

29.05.

Radiomics: the process and the challenges

Michael Bernhofer

Sophia Metz

Andreas Stelzer

12.06.

Protein disorder--a breakthrough invention of evolution?

Michael Bernhofer

Zuzanna Slawinska

Leo Kaindl

19.06.

General Overview of Structure, Function & Prediction of noncoding RNA (ncRNA)

Michael Heinzinger

Sophia Descho

Ishbah Zainab Farid

 

Topics

and the corresponding supervisors.

 

1. 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).

Literature:

  • A.M. Lesk. Bioinformatik: Eine Einführung. Spektrum Akademischer Verlag, 2002.
  • A.M. Lesk. Introduction to bioinformatics
  • David Mount, Bioinformatics: Sequence and Genome Analysis, Second Edition, CSH Laboratory Press

 

2. 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).

Literature:

  • Korf, Ian et al. BLAST. O'Reilly Media, Inc. 2003
  • David Mount, Bioinformatics: Sequence and Genome Analysis, Second Edition, CSH Laboratory Press

 

3. PolyPhobius: Prediction of transmembrane in protein sequences

Jonas Reeb

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.

Literature:

  • 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.

 

4. SignalP 4: Prediction of signal peptides in protein sequences

Jonas Reeb

Signal peptides are an essential tool of protein transport. This talk will first introduce their basic concept and roles and then discuss the fourth iteration of the popular prediction tool SignalP which predicts the location of signal peptides from sequence alone. In SignalP4 special care was taken to distinguish signal peptides from biophysically similar transmembrane helices to further improve performance.

Literature:

  • 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 (in particular the supplemental material)

 

5. Radiomics: the process and the challenges

Michael Bernhofer

Radiomics data can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. This talk shall present how radiomics data is generated and the challenges thereof, as well as how this data is used to build predictive models.

Literature:

  • Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ (2012). Radiomics: the process and the challenges. Magn Reson Imaging, 30, 9:1234-48. https://www.ncbi.nlm.nih.gov/pubmed/22898692
  • Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G, Lambin P, Haibe-Kains B, Mak RH, Aerts HJ (2015). CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol, 114, 3:345-50. https://www.ncbi.nlm.nih.gov/pubmed/25746350
  • Vallières M, Freeman CR, Skamene SR, El Naqa I (2015). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol, 60, 14:5471-96. https://www.ncbi.nlm.nih.gov/pubmed/26119045

 

6. Protein disorder--a breakthrough invention of evolution?

Michael Bernhofer

The regions in proteins that do not adopt regular three-dimensional structures in isolation are called disordered regions. In this seminar the functional and structural aspects of disordered proteins shall be discussed. Though only one literature source is provided, the student is expected to use and refer to additional sources in his presentation for a detailed understanding of protein disorder.

Literature:

  • Schlessinger A, Schaefer C, Vicedo E, Schmidberger M, Punta M, Rost B  (2011). Protein disorder--a breakthrough invention of evolution? Curr Opin Struct Biol. Jun;21(3):412-8 http://www.ncbi.nlm.nih.gov/pubmed/21514145

 

7. LocTree3: Prediction of subcelluar localization of proteins

Maria Schelling

LocTree3 is a machine-learning-based method for predicting localization using evolutionary information and sequence homology information from BLAST. This talk shall introduce basic mechanisms of cellular sorting and the prediction of subcellular localization from sequence as  done by Loctree3.

Literature:

  • Goldberg T, Hecht M, Hamp T, Karl T, Yachdav G, Nielsen H, Rost B et al. (2014). LocTree3 prediction of localization. LocTree3 prediction of localization. Nucleic Acids Res., 42. http://www.ncbi.nlm.nih.gov/pubmed/24848019

  • Molecular Biology of the cell - Alberts

 

8. General Overview of Structure, Function & Prediction of noncoding RNA (ncRNA)

Michael Heinzinger

Give a short overview of the diverse nature of ncRNAs (e.g. long noncoding RNA), including tertiary structure and its implications on the function. Summarize one current structure prediction tool (e.g. RNAstructure) and discuss possible pitfalls, compared to protein structure prediction.

Literature: