Bi7528 Analysis of genomic and proteomic data

Faculty of Science
Autumn 2011 - acreditation

The information about the term Autumn 2011 - acreditation is not made public

Extent and Intensity
2/0/0. 2 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), graded credit, z (credit).
Teacher(s)
Mgr. Eva Budinská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX - Faculty of Science
Contact Person: Mgr. Eva Budinská, Ph.D.
Prerequisites (in Czech)
Bi5040 Biostatistics - basic course
Bi5040 Biostatistika – základní kurz, Bi8600 Vícerozměrné statistické metody, DSMBz01 Molekulární biologie a genetika, Bi3060 Obecná genetika Nutným předpokladem je dobrá znalost základní metodologie biostatistiky a základů molekulární biologie a genetiky . Doporučeno je absolvování předmětu Vícerozměrné statické metody (Bi8600).
Course Enrolment Limitations
The course is offered to students of any study field.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30
Course objectives
The course belongs to advanced courses in data analysis of medical and biological data and is oriented to analysis of data of high-density genomic and proteomic technologies such as micorarrays, mass spectrometry, 2D gel electrophoresis etc. In the first part, students acquire the basic knowledge about the technologies and their principles. Next part of the course summarizes the general principles of the data analysis, common for all the technologies. First, students are introduced to data pre-processing: image analysis, quality control and normalization (differences between particular technologies are emphasized), followed by methods for first-level data-analysis. This part is dedicated to methods of differential gene/protein expression analysis. Further, second-level analysis is explained, including methods for group discovery and building classifiers. In the last part, students learn how to connect results of these analysis with external data-sources (clinical parameters, gene-ontologies, etc...), and how to interpret the results from the complex biological point of view. Concrete examples will be used to demonstrate the process of the analysis. All the learning material and examples are available in online e-learning form.
Syllabus
  • 1. Introduction 2. Recent challenges of genomic and proteomic analysis 3. Recent technologies for analysis of genome and proteome 4. Basic principles of „high-density“ data analysis 5. Pre-processing – image analysis, quality control, normalization 6. First level data analysis - differential expression analysis 7. Second level data analysis - group discovery and prediction 8. Specific issues in data analysis of particular methods: 8a. Gene expression microarrays 8b. arrayCGH analysis 8c. ChIP-chip 8d. Mass spectrometry 8e. 2D-gel elektrophoresis 9. Pathway analysis 10. Meta-analysis 11. Data interpretation
Literature
  • Meta-analysis and combining information in genetics and genomics. Edited by Rudy Guerra - Darlene Renee Goldstein. Boca Raton: CRC Press, 2010. xxiii, 335. ISBN 9781584885221. info
  • Data analysis and visualization in genomics and proteomics. Edited by Francisco Azuaje - Joaquín Dopazo. Hoboken, NJ: John Wiley, 2005. xv, 267. ISBN 0470094397. info
Teaching methods
Lectures with online e-learning material.
Assessment methods
The teaching is performed in form of a block course and finished by a written exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: in blocks.
General note: předmět vyučován blokově.
Information on course enrolment limitations: Doporučení absolvovat Bi8600, DSMBz01, Bi3060
The course is also listed under the following terms Autumn 2010 - only for the accreditation, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020.