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09.03.2023

16.03 o godz. 13:00 w naszej siedzibie (Varso Tower ul. Chmielna 69) odbędzie się wykład prof. Dariusza Plewczyńskiego, zatytułowany “Human Genome Topology at the Population Scale: combining machine learning models, polymer biophysical simulations and experimental 3D genomics methods to understand the link between DNA sequence, structure, and function”.

Wydarzenie jest darmowe, ale z uwagi na ograniczoną liczbę miejsc wymagana jest rejestracja.

✏️ Wiadomości ze zgłoszeniem na wykład prosimy kierować do 15.03 na adres: registration@ideas-ncbr.pl.

W spotkaniu można uczestniczyć również zdalnie:

https://us06web.zoom.us/j/89436139569?pwd=YklablhmOGIwQ0tCQmJXN0hnak9UZz09

Identyfikator spotkania 894 3613 9569

Kod dostępu 114700

Dariusz Plewczynski’s interests are focused on functional and structural genomics. His functional attempts use the vast wealth of data produced by high-throughput genomics projects, such as 4DNucleome (structural genomics consortium), 1000 Genomes Project, UK BioBank, Simons Genome Diversity Project, Earth BioGenome Project, ENCODE, and many others.

The major tools that are used in this interdisciplinary research endeavor include statistical data analysis (GWAS studies, clustering, machine learning), genomic variation analysis using diverse data sources (karyotyping, confocal microscopy, aCGH microarrays, next generation sequencing), bioinformatics (protein sequence analysis, protein structure prediction), and finally biophysics (polymer theory and simulations) and genomics (epigenetics, genome domains, three dimensional structure of chromatin).



He is presently involved in several Big Data projects both in US (4DN at Jackson Laboratory for Genomic Medicine, Earth BioGenome at University of California Davis), EU (INDEPTH, INC COST actions, ENHPATHY ITN) and in Poland (CeNT University of Warsaw and MINI Warsaw Technical University). He is actively participating in two large consortia projects, namely bioinformatics and genomic analysis of 1000 Genomes Project population data for structural variants (SV) and single nucleotide polymorphism (SNP) identification in the context of 3D nuclear structure; biophysical modeling and deep learning epitensor representation of three-dimensional chromatin conformation within 4Dnucleome project for multiple human cell lines using HiC and ChIA-PET techniques.

His goal is to combine SV, epigenomic, transcriptional, and super-resolution imaging data with spatial and temporal nucleus structure for a better understanding of the biological function of genomes, the genomic structural variation within populations of cells and between individuals from different species, the spatial constrains for the natural selection during the evolutionary processes, mammalian cell differentiation, and finally cancer origin and development.

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