Computational methods in mass spectrometry-based metabolomics (Proseminar)

Computational methods in mass spectrometry-based metabolomics (Proseminar)

About

Metabolomics studies metabolites, small molecules involved in cell metabolism. Out of all omics, metabolomics has the closest link to the phenotype making it of the utmost importance for personalized medicine and drug discovery. Mass spectrometry (MS) represents the key analytic technique in metabolomics. Modern MS instruments can trace picograms of material at a very high resolution. However, metabolomics MS data interpretation remains a challenging computational problem due to the unprecedented chemical diversity of metabolites. This proseminar covers the latest findings in computational metabolomics as well as fundamental publications in this field.

This block proseminar is for B.Sc. students in Bioinformatics. We will mostly focus on the algorithmic ideas behind the papers. No specific prerequisite knowledge is required.

 

General information

Tutors: Jun.-Prof. Dr. Alexey Gurevich, Prof. Dr. Olga Kalinina

Language: preferably English, though German is possible

Registration: send an email to gurevich@cs.uni-saarland.de before 23:59  06.11.2022  (also register in LSF to get up to 5 CP)

 

Grading

The final grade will rely on the following course components:

• Presentation:
  • Talk of approx. 30 minutes
  • Answering the questions from the audience after the presentation
• Text summary:
  • Short description of your presented topic
  • Ca. 2 pages of text (with or without subsections), excluding title page, references, figures, tables, etc.
  • It is recommended to write the report using LaTeX in order to train scientific writing (11 pt, 1.5 line spacing)
• Participation in the Q&A sessions.

The component weights are 50% (presentation) + 30% (summary) + 20% (participation).

Useful materials

• Presentation preparation
  • Guidelines & checklist (borrowed from the CCB group)
  • UdS templates (MS PowerPoint & LaTeX); Simple Overleaf template (LaTex)
• Text summary preparation
  • "How to Summarize a Research Article" (borrowed from UConn)
  • Simple Overleat template (LaTeX)

Papers

1. Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships (machine learning inspired by natural language processing) -- Anna-Kristin Lang 

2. MolDiscovery: learning mass spectrometry fragmentation of small molecules (statistical learning)

3. Fragmentation Trees Reloaded (combinatorics, probability theory) -- Andreas Baldauf

4. [CANOPUS] Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra (deep neural networks)

5. [CycloNovo] De Novo Peptide Sequencing Reveals Many Cyclopeptides in the Human Gut and Other Environments (graph algorithms) -- Ilya Olkhovsky

6. MSNovelist: de novo structure generation from mass spectra (neural networks) -- Azat Tagirdzhanov

Important dates

Kick-off meeting (introduction to the field by A.G., general questions, paper assignment): on the week 14-18.11.2022. The exact date/time will be doodled among the registered participants. The event will probably be online via MS Teams.

Deadline for paper selection: 23:59 on 24.11.2022 (email to gurevich@cs.uni-saarland.de)

Deadline for feedback (optional): 23:59 on 21.03.2023 (2 weeks before the presentations)

Presentations (i.e., the seminar day): from 10:00 on 04.04.2023 (Tuesday) (approx. till 13:00). Hopefully, it will be conducted in person in E2.1 (CBI), R1.06 (the seminar room); subject to the Corona situation in April 2023.

Summary submission deadline: 23:59 on 28.03.2023  (1 week after before the presentations)