E5020 Analysis of Nontarget MS Data

Faculty of Science
Spring 2025
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
Helge Hecht, M.Sc. (lecturer)
Elliott James Price, PhD (lecturer)
Mgr. Eva Budinská, Ph.D. (lecturer)
doc. Ing. Vlad Popovici, PhD (lecturer)
Guaranteed by
Mgr. Eva Budinská, Ph.D.
RECETOX – Faculty of Science
Contact Person: doc. Ing. Vlad Popovici, PhD
Supplier department: RECETOX – Faculty of Science
Prerequisites
Fundamental understanding of separation methods (chromatography) and mass spectrometry;
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: 0/30, 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 (i) the fundamentals of mass spectrometry data acquired for non-target studies, (ii) theory behind methods and algorithms (iii) how to use the knowledge gained to choose software tools to process data.
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 a focus on liquid and gas chromatography - know basic data formats used in MS data processing - based on data type, methodology used and molecule type, select methods and 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 using Galaxy
Syllabus
  • 1. Fundamentals of Instrumental Analysis (Chromatography, Mass Spectrometry); 2. Fundamentals of applications and experiment design (-omics, sample specifics, hypothesis); 3. Instrumental methods & data characteristics (acquisition methods, terminology, data characteristics); 4. Methods and algorithms for MS data processing 5. Software for MS Data processing; The exacts content of each class is announced in the interactive syllabus each semester.
Literature
    recommended literature
  • PETERS, K, J BRADBURY, S BERGMANN, M CAPUCCINI, M CASCANTE, Atauri P DE, TMD EBBELS, C FOGUET, R GLEN, A GONZALEZ-BELTRAN, UL GUNTHER, E HANDAKAS, T HANKEMEIER, K HAUG, S HERMAN, Petr HOLUB, M IZZO, D JACOB, D JOHNSON, F JOURDAN, N KALE, I KARAMAN, B KHALILI, PE KHONSARI, K KULTIMA, S LAMPA, A LARSSON, C LUDWIG, P MORENO, S NEUMANN, JA NOVELLA, O Donovan C, JTM PEARCE, A PELUSO, ME PIRAS, L PIREDDU, MAC REED, P ROCCA-SERRA, P ROGER, A ROSATO, R RUEEDI, C RUTTKIES, N SADAWI, RM SALEK, SA SANSONE, V SELIVANOV, O SPJUTH, D SCHOBER, EA THEVENOT, M TOMASONI, M VAN RIJSWIJK, M VAN VLIET, MR VIANT, RJM WEBER, G ZANETTI and C STEINBECK. PhenoMeNal: processing and analysis of metabolomics data in the cloud. GIGASCIENCE. OXFORD: OXFORD UNIV PRESS, 2019, vol. 8, No 2, 12 pp. ISSN 2047-217X. Available from: https://dx.doi.org/10.1093/gigascience/giy149. URL info
  • Mass spectrometry in metabolomics : methods and protocols. Edited by Daniel Raftery. New York: Humana Press, 2014, xvi, 360. ISBN 9781493912575. info
    not specified
  • SKOOG, Douglas A., F. James HOLLER and Stanley R. CROUCH. Principles of instrumental analysis. Seventh edition. Boston: Cengage Learning, 2018, xx, 959. ISBN 9781305577213. info
  • GROSS, Jürgen H. Mass spectrometry : a textbook. Edited by Peter Roepstorff. 2nd ed. Berlin: Springer, 2011, xxiv, 753. ISBN 9783642107092. info
Teaching methods
The course is taught in English.
Assessment methods
written test
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
Teacher's information
https://www.recetox.muni.cz/en/services/data-services-2/spectrometric-data-processing-and-analysis
The course is also listed under the following terms Spring 2023, Spring 2024.
  • Enrolment Statistics (Spring 2025, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2025/E5020