Course: Methods of Functional Genomic

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Course title Methods of Functional Genomic
Course code KMB/217
Organizational form of instruction Lecture
Level of course Bachelor
Year of study 2
Frequency of the course In each academic year, in the summer semester.
Semester Summer
Number of ECTS credits 5
Language of instruction English
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Chodáková Lenka, Mgr. Ph.D.
Course content
Content of lectures Proteomics: Mass spectrometry: what's the purpose? MS instrumentation Peptide fingerprinting: protein identification by MS Peptide sequencing by MS Identification of post-translational modifications LC-MSMS and large-scale proteomics, incl. examples Stable-isotope labeling and quantitative proteomics Analysis of intact proteins Mass spectrometry as a discovery tool in proteomics Mass spectrometry as a screening tool - from research to clinic? Mass spectrometry for structural biology Bioinformatics: Introduction: Biologists and Bioinformatics - What bioinformatics is - What bioinformatics can do - What it can't do - Current Challenges for bioinformatics - Data sizes, Read lengths Application 1: Microarrays - Analysis - Normalization - Design - Data Repositories and Mining Application 2: Next Generation Sequencing - The range of applications - Data analysis by application - What can go wrong and biases with counts Flow cytometry: The principles of flow cytometry Technology overview Assays, applications Genomics: Microarrays - overview of technologies & applications; Massively parallel sequencing - overview of technologies & applications; comparison with microarrays Principles of MPS data analysis Discovery and implications of genome structural variants qPCR - principles, applications, data. RNAi technology: Overview about silencing reagents used in RNAi screens siRNA design Transfection methods used for RNAi Genome-wide RNAi screen to identify cell division genes as an example for high-throughput applications Validation of RNAi results

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Skills training, Work activities (workshops)
  • Preparation for classes - 120 hours per semester
  • Class attendance - 20 hours per semester
Learning outcomes
Objectives of the course: a) to provide students with a comprehensive overview of current methods used in functional genomics with an emphasis on their practical aspects; b) to increase students' awareness through examples from real scientific projects about methods' potential they offer for solution of tasks the students may encounter during work on their diploma/PhD thesis.
Obtaining basic educaion in following fields: Proteomics, transcriptomics, genomics,
Prerequisites
Basic understanding of cell and molecular biology. Special course for Ph.D. study field Molecular and Cell Biology and Genetics

Assessment methods and criteria
Test

The exam is a multiple-choice test. Minimal score necessary is 50%.
Recommended literature
  • Ahrens CH, Brunner E, Qeli E, Basler K & Aebersold R: Generating and navigating proteome maps using mass spectrometry. Nature ReviewsMol Cell Bio (2010), 12, 789-801..
  • Erfle H, Neumann B, Rogers J, Bulkescher J, Ellenberg J and Pepperkok R: Work Flow for Multiplexing siRNA Assays by Solid-Phase Reverse Transfection in Multiwell Plates. J Biomol Screening (2008), 13, 575-80..
  • Gibson & Muse: A Primer of Genome Science, the 3rd edition (Sinauer Associates, 2009, ISBN 978-0-87893-236-8).
  • Han X, Aslanian A & Yates III JR: Mass spectrometry for proteomics. Current Opinion Chem Biol (2008), 12, 483-490..
  • Herzenberg LA, Parks D, Sahaf B, Perez O, Roederer M & Herzenberg LA: The history and future of fluoresence activated cell sorter and flow cytometry. Clinical Chemistry (2002), 48, 1819-1827..
  • Choudhary C & Mann M: Decoding signalling networks by mass-spectrometry-based proteomics. Nature ReviewsMol Cell Bio (2010), 12, 427-439..
  • Mann M: Functional and quantitative proteomics using SILAC. Nature ReviewsMol Cell Bio (2006), 7, 952-8..
  • Mardis ER: A decade's perspective on DNA sequencing technology. Nature (2011), 470, 198-203..
  • Medvedev P, Stanciu M, Brudno M: Computational Methods for Discovering Structural Variation with Next Generation Sequencing, Nature Methods (2009), 6:S13-S20, 2009..
  • Moffa J, Sabatini DM: Building mammalian signalling pathways with RNAi screens. Nature Reviews Mol Cell Bio (2006), 7, 177-87..
  • Neumann B, Walter T, Hériché JK, Bulkescher J, Erfle H, Conrad C, Rogers P, Poser I, Held M, Liebel U, Cetin C, Sieckmann F, Pau G, Kabbe R, Wünsche A, Satagopam V, Schmitz MH, Chapuis C, Gerlich DW, Schneider R, Eils R, Huber W, Peters JM, Hyman AA, Durbin R, Pepperkok R, Ellenberg J: Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature (2010), 464, 721-7..


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester