Research group

Bioinformatics Group

The bioinformatics group is dedicated to the development of efficient computational methods for important real-world biological and medical problems. The projects cover a diverse range of exciting topic matters from analyzing metagenomic sequencing data, to gene expression analysis and metabolomics.

The group is based at the Computer Technologies Department at ITMO University and supported by JetBrains. The group actively collaborates with Maxim Artyomov’s laboratory at Washington University in St. Louis and Dmitry Alexeev’s laboratory at MIPT.

Main research projects

Algorithms for comparative metagenomics

Metagenomic projects usually deal with a lot of data: up to several hundred gigabytes of sequencing data per project. To be able to effectively analyze such amounts of data and perform a comparative analysis, a software called MetaFast was developed. It implements a light-weight assembly algorithm which is something between a traditional k-mer spectrum analysis and a full metagenomic assembly. The algorithm allows MetaFast to combine the advantages of both methods: high computational performance with more informative and better interpretable extracted features, all without using reference genome sequences.

Analysis of open gene expression databases

Currently, there are several big databases, such as TCGA and GEO Omnibus, which store the data from a huge number of experiments. Analysing these experiments and finding hidden interrelations is a very promising field. As a part of the project, a web-service GeneQuery is being developed, which allows experiments with similar patterns of gene regulation to be found. Using the results of such a search can lead to the discovery of unobvious connections, which can also then facilitate hypothesis generation.

Computational methods for studying metabolic regulation

Recently a metabolic regulation role was recognized as one of the immune system and cancer hallmarks. In this project, a number of methods are being developed to analyze gene expression and metabolomics data to study such regulation. Currently, some of the methods are available as a web-service GAM for integrative network analysis of gene expression and metabolomics profiling data.

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