Researcher in Laboratory © HIPS

Human-Microbe Systems Bioinformatics

Jun Prof Dr Alexey Gurevich

The human body encompasses fewer human cells than microbes. They constantly interact with each other and the host and greatly affect an individual's health and well-being. In our group, we develop and apply state-of-the-art bioinformatics software to study the human-microbe systems and aim to discover natural products involved in communication between the two realms.

Our research and approach

Natural products are a promising source of new pharmaceuticals, such as anti-infectives. They also mediate human-microbe and microbe-microbe interactions and could serve clinicians as biomarkers of health and disease. Breakthroughs in omics technologies enabled the rapid acquisition of voluminous data on natural products and their producers, but the computational analysis and interpretation of these data remain a bottleneck.

We aim to turn natural product discovery into a high-throughput technology by developing and applying specialized computational tools. The close interaction with computer scientists and natural product experts makes the created software robust, efficient, and genuinely suitable for the local and international research community. The access to unique single- and multi-omics datasets available within collaborative projects represents a gold mine for tools’ validation and fine-tuning and ultimately leads to biomedically important discoveries and enhanced understanding of the complex human-microbe systems.

Team members

Research projects

Multi-omics approaches to natural products discovery

Integration of data from several omics technologies has great potential in natural product research. Recently, we utilized paired genomics and metabolomics datasets for the high-throughput discovery of ribosomal (RiPPs) and nonribosomal (NRPs) peptides. We work on extending this effort to other classes of natural products. We also consider adding more omics levels (transcriptomics, proteomics, metagenomics) to make the tools more specific and sensitive.

Tracking of biosynthetic gene clusters

Biosynthetic gene clusters (BGCs) are responsible for the production of natural products and thus play a vital role in shaping microbial ecosystems and host-microbe interactions.

We predict BGCs from various microbiomes with genome mining software, correlate BGC abundance with metadata, such as the health state of the associated hosts, and identify the most clinically-important BGCs. To simplify further experimental validation, we develop computational methods for linking BGCs to their tentative final products.

Establishing the standards in metagenomics data analysis

Metagenomic sequencing enables the studying of complex microbiomes. However, metagenomic data interpretation requires multi-step computational pipelines involving dozens of tools, such as short/long read assemblers, binners, and taxonomic classifiers. We participate in the Critical Assessment of Metagenome Interpretation (CAMI) initiative that provides researchers and software developers with guidelines and best practices in metagenomic data analysis. In particular, we develop and maintain metaQUAST and QUAST, the leading software for (meta)genome assembly evaluation.



On the Transformation of LL(k)-linear to LL(1)-linear Grammars

Olkhovsky I, Okhotin A (2023)

Theory Comput Syst 67 (2): 234-262DOI: 10.1007/s00224-022-10108-6

HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search

Lee Y, Guler M, Chigumba D, Wang S, Mittal N, Miller C, Krummenacher B, Liu H, Cao L, Kannan A, …, Kersten R, Mohimani H (2023)

Nat Commun 14 (1)DOI: 10.1038/s41467-023-39905-4

ABC-HuMi: the Atlas of Biosynthetic Gene Clusters in the Human Microbiome

Hirsch P, Tagirdzhanov A, Kushnareva A, Olkhovskii I, Graf S, Schmartz G, Hegemann J, Bozhüyük K, Müller R, Keller A, Gurevich A (2023)

Nucleic Acids ResDOI: 10.1093/nar/gkad1086

WebQUAST: online evaluation of genome assemblies

Mikheenko A, Saveliev V, Hirsch P, Gurevich A (2023)

Nucleic Acids Res 51 (W1): 601-DOI: 10.1093/nar/gkad406


NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters

Leão T, Wang M, Da Silva R, Gurevich A, Bauermeister A, Gomes P, Brejnrod A, Glukhov E, Aron A, Louwen J, …, Bandeira N, Dorrestein P (2022)

PNAS nexus 1 (5)DOI: 10.1093/pnasnexus/pgac257

NPvis: An Interactive Visualizer of Peptidic Natural Product-MS/MS Matches

Kunyavskaya O, Mikheenko A, Gurevich A (2022)

Metabolites 12 (8)DOI: 10.3390/metabo12080706