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Jan Aerts

Researcher at Katholieke Universiteit Leuven

Publications -  104
Citations -  14649

Jan Aerts is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Genome & Visualization. The author has an hindex of 32, co-authored 100 publications receiving 11587 citations. Previous affiliations of Jan Aerts include Parco Tecnologico Padano & Wageningen University and Research Centre.

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Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution

LaDeana W. Hillier, +174 more
- 09 Dec 2004 - 
TL;DR: A draft genome sequence of the red jungle fowl, Gallus gallus, provides a new perspective on vertebrate genome evolution, while also improving the annotation of mammalian genomes.
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SCENIC: single-cell regulatory network inference and clustering.

Sara Aibar, +13 more
- 09 Oct 2017 - 
TL;DR: On a compendium of single-cell data from tumors and brain, it is demonstrated that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states.
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Origins and functional impact of copy number variation in the human genome

Donald F. Conrad, +30 more
- 01 Apr 2010 - 
TL;DR: It is concluded that the heritability void left by genome-wide association studies will not be accounted for by common CNVs, and 30 loci with CNVs that are candidates for influencing disease susceptibility are identified.
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The Genome Sequence of Taurine Cattle: A Window to Ruminant Biology and Evolution

Christine G. Elsik, +328 more
- 24 Apr 2009 - 
TL;DR: To understand the biology and evolution of ruminants, the cattle genome was sequenced to about sevenfold coverage and provides a resource for understanding mammalian evolution and accelerating livestock genetic improvement for milk and meat production.
Posted ContentDOI

SCENIC: Single-Cell Regulatory Network Inference And Clustering

Sara Aibar, +14 more
- 31 May 2017 - 
TL;DR: SCENIC (Single Cell rEgulatory Network Inference and Clustering) is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs.