Καταχωρήθηκε: Δευτέρα 27 Μάρ 2023
Presenter: Prof. Spyridonidis
Participants: Dr Liga, Dr Tsokanas
Duration: 35 Mins

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics,
currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource
of 1,635 open-access datasets from four donors (30 tissues 3 15 assays). The datasets are mapped to
matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million
allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allelespecific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing
genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables
models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, ENTEx provides rich data and generalizable models for more accurate personal functional genomics.

See the full article on https://www.cell.com/action/showPdf?pii=S0092-8674%2823%2900161-7&fbclid=IwAR2bVasuCXu7mJK87tl0IcOM_cgcXL-PoT2Xdr5ZcSM16xTnMhE3M9uBtlw


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