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Showing 1–17 of 17 results
Advanced filters: Author: "Stein Aerts" Clear advanced filters

  • Deep learning models were used to design synthetic cell-type-specific enhancers that work in fruit fly brains and human cell lines, an approach that also provides insights into these gene regulatory elements.

    • Ibrahim I. Taskiran
    • Katina I. Spanier
    • Stein Aerts
    ResearchOpen Access
    Nature
    Volume: 626, P: 212-220

  • A chromatin accessibility atlas of 240,919 cells in the adult and developing Drosophila brain reveals 95,000 enhancers, which are integrated in cell-type specific enhancer gene regulatory networks and decoded into combinations of functional transcription factor binding sites using deep learning.

    • Jasper Janssens
    • Sara Aibar
    • Stein Aerts
    Research
    Nature
    Volume: 601, P: 630-636

  • The authors show that the transcription factor Grainy head (Grh) is necessary and sufficient for opening of epithelial enhancers, but not for their activation. Grh is shown to function as a pioneer factor, displacing nucleosomes and paving the way for other transcription factors to activate enhancers.

    • Jelle Jacobs
    • Mardelle Atkins
    • Stein Aerts
    Research
    Nature Genetics
    Volume: 50, P: 1011-1020

  • A genome-wide analysis of DNA and RNA sequences, gene expression and DNA modifications in 200 samples of acute myeloid leukaemia sets the stage for data integration and verification that will enhance our understanding of this cancer.

    • Stein Aerts
    • Jan Cools
    News & Views
    Nature
    Volume: 499, P: 35-36

  • The key regulators that allow transition from proliferative to invasive phenotype in melanoma cells have not been identified yet. The authors perform chromatin and transcriptome profiling followed by comprehensive bioinformatics analysis identifying new candidate regulators for two distinct cell states of melanoma.

    • Annelien Verfaillie
    • Hana Imrichova
    • Stein Aerts
    ResearchOpen Access
    Nature Communications
    Volume: 6, P: 1-16

  • New computational method uses convolutional neural networks for cis-regulatory sequence analysis to analyze and cluster scATAC-seq data.

    • Stein Aerts
    News & Views
    Nature Methods
    Volume: 19, P: 1041-1043

  • This Primer on chromatin accessibility profiling methods discusses differences in the methods commonly used to determine chromatin states in different cell types, including ATAC-seq and ChIP–seq. The authors summarize applications in different areas of research, from single cells to tissues and whole organisms.

    • Liesbeth Minnoye
    • Georgi K. Marinov
    • Stein Aerts
    Reviews
    Nature Reviews Methods Primers
    Volume: 1, P: 1-24

  • SCENIC is a computational pipeline to predict cell-type-specific transcription factors through network inference and motif enrichment. Here the authors describe a detailed protocol for pySCENIC: a faster, container-based implementation in Python.

    • Bram Van de Sande
    • Christopher Flerin
    • Stein Aerts
    Protocols
    Nature Protocols
    Volume: 15, P: 2247-2276