Publications

SCENIC+

González-Blas CB, De Winter S, Hulselmans G, Hecker N, Matetovici I, Christiaens V, Poovathingal S, Wouters J, Aibar S & Aerts S. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. BioRxiv 2022 August; DOI: 10.1101/2022.08.19.504505

SCENIC & pySCENIC

Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine JC, Geurts P, Aerts J, van den Oord J, Atak ZK, Wouters J, Aerts S. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017 October; 14(11):1083-1086 DOI: 10.1038/nmeth.4463 We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data.

SCENIC Protocols

Van de Sande B., Flerin C., Davie K., De Waegeneer M., Hulselmans G., Aibar S., Seurinck R., Saelens W., Cannoodt R., Rouchon Q., Verbeiren T., De Maeyer D., Reumers J., Saeys Y. & Aerts S. A scalable SCENIC workflow for single-cell gene regulatory network analysis Nat Protoc. 2019 June; 15, pages2247–2276(2020) DOI: 10.1038/s41596-020-0336-2 This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines.

SCope

Davie K, Janssens J, Koldere D, De Waegeneer M, Pech U, Kreft Ł, Aibar S, Makhzami S, Christiaens V, Bravo González-Blas C, Poovathingal S, Hulselmans G, Spanier KI, Moerman T, Vanspauwen B, Geurs S, Voet T, Lammertyn J, Thienpont B, Liu S, Konstantinides N, Fiers M, Verstreken P, Aerts S. A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain. Cell. 2018 June; 174(4):982-998.e20 DOI: 10.1016/j.cell.2018.05.057 A single-cell atlas of the adult fly brain during aging: Network inference reveals regulatory states related to oxidative phosphorylation Cell identity is retained during aging despite exponential decline of gene expression SCope: An online tool to explore and compare single-cell datasets across species

cisTopic

Bravo González-Blas C, Minnoye L, Papasokrati D, Aibar S, Hulselmans G, Christiaens V, Davie K, Wouters J, Aerts S. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat Methods. 2019 April; 16(5):397-400 DOI: 10.1038/s41592-019-0367-1 Single-cell epigenomics provides new opportunities to decipher genomic regulatory programs from heterogeneous samples and dynamic processes. We present a probabilistic framework called cisTopic, to simultaneously discover “cis-regulatory topics” and stable cell states from sparse single-cell epigenomics data.

ScoMAP

Bravo González‐Blas C., Quan X.-J., Duran‐Romaña R., Ihsan Taskiran I., Koldere D., Davie K., Christiaens V., Makhzami S., Hulselmans G., De Waegeneer M., Mauduit M., Poovathingal S., Aibar S., Aerts S. Identification of genomic enhancers through spatial integration of single‐cell transcriptomics and epigenomics Molcular System Biology Mol Syst Biol (2020)16:e9438 DOI: https://doi.org/10.15252/msb.20209438 Single‐cell technologies allow measuring chromatin accessibility and gene expression in each cell, but jointly utilizing both layers to map bona fide gene regulatory networks and enhancers remains challenging. Here, we generate independent single‐cell RNA ‐seq and single‐cell ATAC ‐seq atlases of the Drosophila eye‐antennal disc and spatially integrate the data into a virtual latent space that mimics the organization of the 2D tissue using ScoMAP (Single‐Cell Omics Mapping into spatial Axes using Pseudotime ordering).

iRegulon

Verfaillie A, Imrichova H, Janky R, Aerts S. iRegulon and i-cisTarget: Reconstructing Regulatory Networks Using Motif and Track Enrichment. Curr Protoc Bioinformatics. 2015 December; 52:2.16.1-39 DOI: 10.1002/0471250953.bi0216s52

Janky R, Verfaillie A, Imrichová H, Van de Sande B, Standaert L, Christiaens V, Hulselmans G, Herten K, Naval Sanchez M, Potier D, Svetlichnyy D, Kalender Atak Z, Fiers M, Marine JC, Aerts S. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol. 2014 July; 10(7):e1003731 DOI: 10.1371/journal.pcbi.1003731

GRNBoost2 and Arboreto

Moerman T, Aibar S, González-Blas CB, Simm J, Moreau Y, Aerts J, Aerts S. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics. 2018 November DOI: 10.1093/bioinformatics/bty916