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Proteomics & Bioinformatics (2cr)

for the MBI, Master's Degree Program in Helsinki, Finland

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Projects

Slides

Course Material

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“Proteomics & Bioinformatics” course gives an introduction to the available proteomic technologies and the data mining tools. It covers the essential elements of the proteomic technology and its applications. With the usage of several examples the importance of the proteomics applications will be illustrated.

Lecture Schedule

7.5.2007	Introduction into the –ome terms: Genome, Transcriptome, Proteome, Reactome The Proteome Overview of Analytical Proteomics Separation of Protein Mixtures Two-Dimensional Gel Electrophoresis High Performance Liquid Chromatography (HPLC) Mass Spectrometers for Protein & Peptide Analysis Applications of Proteomics
8.5.2007	Gel Image Analysis Data Acquisition Gel Processing/ Visualization Spot Detection & Quantification Gel Matching, Gel Comparison Gel Annotation, Building a Database (Swiss-2DPage, World2DPage, Make2Ddb) Protein Identification by Peptide Mass Fingerprinting Data (PMF) Software Tools for PMF: Scoring the Results (Mascot, MS-Fit, PeptIdent, ProFound, Aldente)
9.5.2007	Peptide Sequence Analysis & Protein Identification with Tandem Mass Spectrometry Data De Novo versus Peptide Fragment Fingerprinting (PFF) Algorithms & Software Tools (Sequest, Mascot)
10.5.2007	Proteomics Databases and Protein Characterization Tools
11.5.2007	Mining Proteomes Protein Expression Profiling Comparative Proteomics with 2D Gels Comparative Proteomics with LC-MS & Isotope Tags Identifying Protein-Protein Interactions Proteomics Discovery of Biomarkers Diagnostic Proteomics Clinical Proteomics Intro to Biological Systems Protein-Protein Interactions Ontologies

Project reports

Exercises

Time and Place

Lectures and exercises will take place at Exactum, Kumpula

Lectures: 7.5-11.5.2007 Monday-Friday 10.15-12.00 in room C221
Exercises: 8.5 and 10.5.2007 Tuesday and Thursday 13.15-15.00 in room B121

Teaching language will be English.

Registration/Grading

Please use the registration form of the Computer Science Department to enrol for the course.

There will be no course exam.

2 credits will be given to students successfully passing the course.

To successfully pass the course a project needs to be carried out. The projects will be graded in the scale from 0-5 with 5 being excellent and 1 the lowest passing grade.

Projects

Projects will have to be submitted electronically to: skossida@bioacademy.gr by the 25th of May 2007 at 20:00.

Project reports:

A comparison of naive Bayes and logistic regression classifiers for mass spectrometry data

Cytoscape and its plugins

Introduction to Mass Spectrometry

Overview on the BioPAX and SBML standards

Preprocessing of Mass Spectrometry Data in the field of Proteomics

Searching molecular sequence databases using BLAST

Wood proteomics _high through put methods for understanding wood formation

Lecture slides:

Lecture 1