PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Hingerl, J.C.* ; Martens, L.D. ; Karollus, A.* ; Manz, T.* ; Buenrostro, J.D.* ; Theis, F.J. ; Gagneur, J.

scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution.

Nat. Methods, DOI: 10.1038/s41592-025-02854-5 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Understanding how regulatory DNA elements shape gene expression across individual cells is a fundamental challenge in genomics. Joint RNA-seq and epigenomic profiling provides opportunities to build unifying models of gene regulation capturing sequence determinants across steps of gene expression. However, current models, developed primarily for bulk omics data, fail to capture the cellular heterogeneity and dynamic processes revealed by single-cell multi-modal technologies. Here, we introduce scooby, the first framework to model scRNA-seq coverage and scATAC-seq insertion profiles along the genome from sequence at single-cell resolution. For this, we leverage the pre-trained multi-omics profile predictor Borzoi as a foundation model, equip it with a cell-specific decoder, and fine-tune its sequence embeddings. Specifically, we condition the decoder on the cell position in a precomputed single-cell embedding resulting in strong generalization capability. Applied to a hematopoiesis dataset, scooby recapitulates cell-specific expression levels of held-out genes, and identifies regulators and their putative target genes through in silico motif deletion. Moreover, accurate variant effect prediction with scooby allows for breaking down bulk eQTL effects into single-cell effects and delineating their impact on chromatin accessibility and gene expression. We anticipate scooby to aid unraveling the complexities of gene regulation at the resolution of individual cells.
Impact Factor
Scopus SNIP
Altmetric
32.100
0.000
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Accessibility; Expression; Proteins
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen Deutsche Forschungsgemeinschaft via the IT Infrastructure for Computational Molecular Medicine
Helmholtz Association under the joint research school Munich School for Data Science
Deutsche Forschungsgemeinschaft (DFG)
European Research Council
Gene Regulation Observatory at the Broad Institute of MIT Harvard
National Institutes of Health (NIH)
NHGRI IGVF consortium
Deutsche Forschungsgemeinschaft (German Research Foundation)
DOI 10.1038/s41592-025-02854-5
WOS ID 001597842200001
Scopus ID 105019614371
PubMed ID 41125796
Erfassungsdatum 2025-10-23
[➜Korrektur melden]