Gert Hulselmans

发表

A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain

P. Verstreken, S. Aerts, T. Voet, 2018, Cell.

SCENIC: Single-cell regulatory network inference and clustering

J. Aerts, P. Geurts, S. Aerts, 2017, Nature Methods.

Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic

S. Aerts, Kristofer Davie, V. Christiaens, 2016, Genome research.

The transcription factor Grainyhead primes epithelial enhancers for spatiotemporal activation by displacing nucleosomes

S. Aerts, G. Halder, Kristofer Davie, 2018, Nature Genetics.

Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference.

Pierre Geurts, Stein Aerts, Gert Hulselmans, 2014, Cell reports.

Exome sequencing identifies mutation in CNOT3 and ribosomal genes RPL5 and RPL10 in T-cell acute lymphoblastic leukemia

Stein Aerts, Gert Hulselmans, Jan Cools, 2012, Nature Genetics.

Identification of genomic enhancers through spatial integration of single‐cell transcriptomics and epigenomics

S. Aerts, Kristofer Davie, Duygu Koldere, 2020, Molecular systems biology.

Identification of genomic enhancers through spatial integration of single‐cell transcriptomics and epigenomics

S. Aerts, Kristofer Davie, Duygu Koldere, 2019, bioRxiv.

Cross-species analysis of enhancer logic using deep learning

L. Zon, S. Aerts, É. Cadieu, 2020, Genome research.

Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state

S. Aerts, G. Halder, J. Marine, 2015, Nature Communications.

Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma

S. Aerts, J. Marine, Kristofer Davie, 2020, Nature Cell Biology.

Single-cell gene regulatory network analysis reveals new melanoma cell states and transition trajectories during phenotype switching

S. Aerts, J. Marine, Kristofer Davie, 2019, bioRxiv.

Cis-topic modelling of single-cell epigenomes

Stein Aerts, Carmen Bravo González-Blas, Liesbeth Minnoye, 2018, bioRxiv.

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

S. Aerts, H. Ceulemans, V. Christiaens, 2017, Genome Medicine.

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

S. Aerts, H. Ceulemans, V. Christiaens, 2017, bioRxiv.

Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

Y. Saeys, S. Aerts, R. Seurinck, 2020, bioRxiv.

Conserved Transcription Factors Steer Growth-Related Genomic Programs in Daphnia

S. Aerts, Gert Hulselmans, K. Spanier, 2017, Genome biology and evolution.

Shared enhancer gene regulatory networks between wound and oncogenic programs

S. Aerts, G. Halder, Duygu Koldere, 2022, bioRxiv.

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

S. Aerts, S. Aibar, V. Christiaens, 2022, bioRxiv.

Integrative analysis of regulatory tracks for the identification of direct TF-target interactions

Stein Aerts, Gert Hulselmans, Hana Imrichova, 2013 .

iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections

Stein Aerts, Gert Hulselmans, Valerie Christiaens, 2014, PLoS Comput. Biol..

JAK3 mutants transform hematopoietic cells through JAK1 activation, causing T-cell acute lymphoblastic leukemia in a mouse model.

S. Aerts, Gert Hulselmans, M. Fiers, 2014, Blood.

Decoding gene regulation in the fly brain

Gabriel N. Aughey, S. Aerts, Jasper Janssens, 2022, Nature.

A scalable SCENIC workflow for single-cell gene regulatory network analysis

Yvan Saeys, Robrecht Cannoodt, Wouter Saelens, 2020, Nature Protocols.

Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning

S. Aerts, V. Christiaens, Gert Hulselmans, 2021, Genome research.

Decoding gene regulation in the fly brain

Gabriel N. Aughey, S. Aerts, Jasper Janssens, 2021, Nature.

cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data

Stein Aerts, Liesbeth Minnoye, Dafni Papasokrati, 2019, Nature Methods.

Cell type diversity in a developing octopus brain

G. Fiorito, N. Rajewsky, S. Aerts, 2022, bioRxiv.

HyDrop: droplet-based scATAC-seq and scRNA-seq using dissolvable hydrogel beads

S. Aerts, Jasper Janssens, V. Christiaens, 2021, bioRxiv.

The transcription factor Grainy head primes epithelial enhancers for spatiotemporal activation by displacing nucleosomes

S. Aerts, G. Halder, Duygu Koldere, 2018, Nature Genetics.

i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly

Stein Aerts, Gert Hulselmans, Delphine Potier, 2015, Nucleic Acids Res..

Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads

S. Aerts, Jasper Janssens, V. Christiaens, 2022, eLife.

A scalable SCENIC workflow for single-cell gene regulatory network analysis

Y. Saeys, S. Aerts, R. Seurinck, 2020, Nature Protocols.

Comprehensive Analysis of Transcriptome Variation Uncovers Known and Novel Driver Events in T-Cell Acute Lymphoblastic Leukemia

Stein Aerts, Gert Hulselmans, Frank Speleman, 2013, PLoS genetics.

Analysis of long and short enhancers in melanoma cell states

S. Aerts, M. de Waegeneer, V. Christiaens, 2021, bioRxiv.

Enhancer grammar of liver cell types and hepatocyte zonation states

S. Aerts, G. Halder, V. Christiaens, 2022, bioRxiv.

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

S. Aerts, H. Ceulemans, V. Christiaens, 2017, bioRxiv.

Identification of Lineage-Specific Cis-Regulatory Modules Associated with Variation in Transcription Factor Binding and Chromatin Activity Using Ornstein–Uhlenbeck Models

Stein Aerts, Gert Hulselmans, Valerie Christiaens, 2015, Molecular biology and evolution.

Rescuing early Parkinson-induced hyposmia prevents dopaminergic system failure

P. Verstreken, S. Aerts, Jasper Janssens, 2023, bioRxiv.

cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data

S. Aerts, S. Aibar, V. Christiaens, 2019, Nature Methods.

Exome sequencing identifies mutation of the ribosome in T-ALL

S. Aerts, B. Hassan, J. Soulier, 2013 .

CD45 antigen negativity in T‐lineage ALL correlates with PTPRC mutation and sensitivity to a selective JAK inhibitor

S. Aerts, R. Foà, S. Chiaretti, 2015, British journal of haematology.

A single-cell catalogue of regulatory states in the ageing Drosophila brain

P. Verstreken, S. Aerts, J. Lammertyn, 2017, bioRxiv.