The goals of our group are to uncover the mechanisms underlying epigenetic regulation in humans and other animals and to identify the role of these mechanisms in cell differentiation and aging. The projects topics cover effective Next Generation Sequencing data processing tools, scalable computational pipelines, visualization approaches and meta analysis of existing epigenomic databases.
The group is based at JetBrains.
Main research project — joint research project of Healthy Aging in collaboration Maxim Artyomov’s laboratory at Washington University in St. Louis. The group provides bioinformatics analysis and develops novel approaches and algorithm for epigenetic analysis.
Aging is accompanied by alterations happening in the whole organism and within the individual cells. This study aims to comprehensively characterize changes happening in the distinct human cell type and its environment during the process of healthy aging. We have compared classical CD14+CD16- monocytes obtained from blood of two sex- and race-matched cohorts: 20 young individuals (24-30 years old) and 20 older people (57-70 years old) without any acute or chronic inflammatory conditions, no history of smoking, and with comparable body-mass indices. We comprehensively characterized the plasma and classical monocytes from both cohorts using proteomic and metabolomic profiling, RNA-Seq, RRBS (DNA methylation) and ULI-ChIP-seq for 5 major chromatin modifications (H3K27ac, H3K27me3, H3K36me3, H3K4me1, H3K4me3).
The comparative epigenetic study of this scale has never been undertaken previously in the context of human aging. Since the peak calling routine presents significant challenge in working with large scale human epigenetic data, we developed a novel semi-supervised peak calling approach applicable to datasets of this scale.
- SPAN Peak Analyzer - semi-supervised peak analyzer
- JBR Genome Browser - fast and reliable genome browser
Full projects list is available here.
Possible students topics
- JBR Genome Browser
- SPAN Semi-supervised Peak Analyzer
- ChipQuery search engine for open ChIP-Seq data
- GMQL query language
- NEW What’s the buzz about / trends analysis in publications of Aging
- NEW Deep learning approaches for changes detection
If you are interested in working on these topics with us, please contact Oleg Shpynov.
- Eugene Bakin, “ChipQuery - Chipseq data comparison”
- Sergey Chernov, "A comprehensive comparison of tools for differential ChIP-seq analysis"
- Dmitriy Groshev, “Comparing the bisulphite sequencing data”
- Anna Atamanova, “Generalizing data on bins for randomly sized genomic loci"
- Alexey Dievsky, Master Thesis “Modeling difference in ChIP-seq data”
- Sergei Lebedev, Master Thesis “Bisulphite sequencing data modeling”