Multi-Omics Approaches to Drug Discovery (Seminar) Jun.-Prof. Dr. Alexey Gurevich



Paper assignment

The papers were assigned as follows.

Software Papers  
1 [NPClassScore] Polina Soloveva
2 [DeepRiPP] Vanessa Siegel
3 [NPOmix] Baozhu Cai
4 [MetaMiner] Siwen... Read more

The papers were assigned as follows.

Software Papers  
1 [NPClassScore] Polina Soloveva
2 [DeepRiPP] Vanessa Siegel
3 [NPOmix] Baozhu Cai
4 [MetaMiner] Siwen Chen
5 [NRPminer] Aleksandra Kushnareva
6 [NPLinker] Ahmed Osman
7 [MetaboAnalyst] Ayesha Amin
Analysis Papers  
1 [ComparativeTranscriptomics] Md Adnan Karim
2 [Metabologenomics] Park Jieun
3 [FungiMetabologenomics] Pranjali Jain
4 [MultiOmics] Rahma Qadeer
5 [SARS-CoV] Ayesha Sarwar
6 [StreptoAntibiotic] Tanya Amit Tyagi
7 [AncientBacteria] Vishva Darji

Multi-Omics Approaches to Drug Discovery (Seminar)


Microbial natural products are a major source of bioactive compounds for drug discovery. Recent advances in biotechnologies, such as sequencing and mass spectrometry instruments, enabled the high-throughput and high-resolution exploration of genomes, transcriptomes, proteomes, and metabolomes of natural product-producing organisms. However, the interpretation of these data remains a bottleneck and requires dedicated computational approaches. In this seminar, we will learn how modern bioinformatics tools integrate at least two different omics levels (e.g., genomics and metabolomics) to shed light on natural product "dark matter" and discover novel compounds at an unprecedented level.

This block seminar is for M.Sc. students in Bioinformatics. We will mostly focus on the algorithmic ideas behind the papers and try to run the underlying software. No specific prerequisite knowledge is required.


General information

Tutor: Jun.-Prof. Dr. Alexey Gurevich

Language: English

Registration: send an email to before 23:59 on 23.4.2023 (also register in LSF to get up to 7 CP). Please provide information about your background/experience and a statement of motivation to attend this seminar (250 words maximum!)



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)
  • Application report:
    • Short report (terminal commands/screenshots/etc) about your attempts to test the described software on sample data provided by the authors or reproduce fragments of the computational analysis from the paper
  • Participation in the Q&A sessions.

The component weights are 50% (presentation) + 20% (summary) + 15% (application) + 15% (participation).

Useful materials


  1. [NPClassScoreEnhanced correlation-based linking of biosynthetic gene clusters to their metabolic products through chemical class matching (Louwen et al., Microbiome, 2023)
  2. [DeepRiPPDeepRiPP integrates multiomics data to automate discovery of novel ribosomally synthesized natural products (Merwin et al., PNAS, 2019)
  3. [NPOmixNPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters (Leão et al., PNAS Nexus, 2022)
  4. [MetaMiner] MetaMiner: A Scalable Peptidogenomics Approach for Discovery of Ribosomal Peptide Natural Products with Blind Modifications from Microbial Communities (Cao et al., Cell Systems, 2019)
  5. [NRPminerIntegrating genomics and metabolomics for scalable non-ribosomal peptide discovery (Behsaz et al., Nature Communications, 2021)
  6. [NPLinkerRanking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions (Eldjarn et al., PLOS Computational Biology, 2021)
  7. [MetaboAnalystMetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights (Pang et al., Nucl. Acid. Res., 2021) NOTE: You might need to read the previous papers (MetaboAnalyst 1.0 - 4.0)
  1. [ComparativeTranscriptomicsComparative transcriptomics as a guide to natural product discovery and biosynthetic gene cluster functionality
  2. [MetabologenomicsMetabologenomics: Correlation of Microbial Gene Clusters with Metabolites Drives Discovery of a Nonribosomal Peptide with an Unusual Amino Acid Monomer
  3. [FungiMetabologenomicsCorrelative metabologenomics of 110 fungi reveals metabolite–gene cluster pairs
  4. [MultiOmics] Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity
  5. [SARS-CoVMetabologenomics approach to the discovery of novel compounds from Streptomyces sp. GMR22 as anti-SARS-CoV-2 drugs
  6. [StreptoAntibioticA comparative metabologenomic approach reveals mechanistic insights into Streptomyces antibiotic crypticity
  7. [AncientBacteriaNatural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic (if you can't access it, please use this link)

Important dates

Kick-off meeting (introduction to the field by A.G., general questions, paper assignment): on week 8-12.5.2023. 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 the kick-off date + 1 week (fill out this form)

Deadline for feedback on your slides (optional):  2 weeks before the presentations

Presentations (i.e., the seminar day/s): 2 days in August/September. The exact date will be voted among the participants in June at the latest. The event will be in person in E2.1 (CBI), R1.06 (the seminar room). In special circumstances, it will be possible to join the seminar online -- contact the tutor in advance.

Summary & Application report submission deadline:  1 week after the presentations

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