Bi5020 Analysis of Nontarget MS Data

Faculty of Science
Spring 2020
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Mgr. Eva Budinská, Ph.D. (lecturer)
RNDr. Ivana Ihnatová, Ph.D. (lecturer)
PharmDr. Zdeněk Spáčil, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX - Faculty of Science
Contact Person: Mgr. Eva Budinská, Ph.D.
Supplier department: RECETOX - Faculty of Science
Knowledge of advanced statistics, mutlivariable analysis and knowledge of command-line in Linux and R - program for statistical analysis of data.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 1/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30
fields of study / plans the course is directly associated with
Course objectives
The aim of this course is to teach students to analyze data from LC and GC mass spectrometers with applications in proteomics, metabolomics and analysis of small chemical compounds.
Learning outcomes
At the end of this course, the students: - know the principles of mass spectrometry - know different approaches to separation and detection of molecules with focus on liquid and gas chromatograpy - know basic raw data types and can convert them according to specific needs of the methods for their analysis - based on data type, methodology used and molecule type, select methods and design algorithms for data pre-processing (normalization, filtering of signal, deconvolution, peak detection,...) and apply it to the data - are able to work with specialized SW and platforms for MS data analysis (MZmine,OpenMS, R packages, galaxy, ...) - are able to design MS experiment based on the biological hypothesis - perform statistical analysis of the pre-processed data - group comparison, group discovery, biomarker detection, pathway analysis
  • 1. Mass spectrometry methods and principles, LC,GC-MS, MS vs MS/MS mode, EI, NCI, ... applications in metabolomics,proteomics and small compound analysis 2. Introduction o common principles and basic stapes of MS data analysis,data types, differences in analysis of GC-MS vs LC-MS data of different types 3. Methods of baseline correction and signal filtering 4. Methods of signal normalization and smoothing 5. Alignment of spectra and peaks 6. Peak detection algorithms 7. Methods for deconvolution of GC-MS and LC-MS/MS data 8. Identification of components in databases 9. Pipeline for analysis of proteomic data 10. Pipeline for analysis of metabolome data 11. Pipeline for analysis of GC-MS small compound data 12. Detection of biomarkers, group comparison, clustering, pathway analysis
    recommended literature
  • VIDOVÁ, Veronika and Zdeněk SPÁČIL. A review on mass spectrometry-based quantitative proteomics: Targeted and data independent acquisition. Analytica Chimica Acta, Amsterdam: Elsevier Science publishers, 2017, vol. 964, April, p. 7-23. ISSN 0003-2670. doi:10.1016/j.aca.2017.01.059. URL info
  • DU, Xiuxia and Steven H ZEISEL. Spectral deconvolution for gas chromatography mass spectrometry - based metabolomics: Current status and future perspectives. Computational and Structural Biotechnology Journal, 2013, vol. 4, No 5. info
  • GREPLOVÁ, Kristína, Radomír PILNÝ, Eva BUDINSKÁ, Lenka DUBSKÁ, Radek LAKOMÝ, Rostislav VYZULA, Bořivoj VOJTĚŠEK and Dalibor VALÍK. When one chip is not enough: Augmenting the validity of SELDI-TOF proteomic profiles of clinical specimens. Lab on a Chip, 2009, vol. 2009, No 9, p. 1014 - 1017. ISSN 1473-0197. URL info
  • SUMNER, L.W., A AMBERG, D BARRETT and ET. AL. Proposed minimum reporting standards for chemical analysis. Metabolomics, Springer US, 2007, vol. 3, No 3, p. 211–221. ISSN 1573-3882. info
Teaching methods
The course has a form of lectures combined with hands-on exercises using selected SW on Linux platform. The attendance is compulsory.
Assessment methods
Te exam will be oral. Student will have to describe his/hers algorithm of analysis of example data. All steps of the analysis have to be sufficiently reasoned. The student will have 30 minutes for preparation during which he/she can use all the study materials available.
Language of instruction
Further Comments
The course is taught annually.
The course is taught: every week.

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