The goals of our group are to uncover the mechanisms underlying epigenetic regulation in humans and animals and to identify the role of these mechanisms in cell differentiation and aging. The project topics cover effective Next Generation Sequencing data processing tools, scalable computational pipelines, visualization approaches, and meta-analysis of existing epigenomic databases.
Project topics cover:
- Effective Next Generation Sequencing data processing tools.
- Scalable and reproducible computational pipelines.
- Visualization approaches.
- Meta-analysis of existing epigenomic databases.
The group is based at JetBrains.
Contact person: Oleg Shpynov.
Multiomics dissection of healthy human aging
A joint research project of Healthy Aging in collaboration with Maxim Artyomov’s laboratory at Washington University in St. Louis. The group provides bioinformatic analysis and develops novel approaches and the algorithm for epigenetic analysis.
Aging is accompanied by alterations throughout the whole organism and within every individual cell. This study aims to comprehensively characterize the changes happening in a distinct human cell type and its environment during the process of healthy aging. We have compared classical CD14+CD16- monocytes obtained from the 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, with no history of smoking, and with comparable body-mass indices. We have 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 a significant challenge to 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 with supervised peaks markup and analysis
Full projects list is available here.
Github page - all the source code is here.
Journal Club - here you can find different materials including internal presentations, journal club and talks.
- 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”