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Analyse multi-omique de cellules uniques par ARNm×Appel de pics ChIP-seq×
DomaineBio-informatiqueBio-informatique
FamilleProcess / pipelineProcess / pipeline
Année d'origine2015–2021 (rapid maturation with CITE-seq 2017; Seurat v4 2021)2007–2008
Auteur d'originePioneered by Rahul Satija (Seurat), Oliver Stegle and John Marioni (MOFA+), and the broader single-cell genomics communityJohnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008)
TypeIntegrative computational pipelineComputational genomics pipeline
Source fondatriceHao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zager, M., Hoffman, P., Stoeckius, M., Papalexi, E., Mimitou, E. P., Jain, J., Srivastava, A., Stuart, T., Fleming, L. M., Yeung, B., Rogers, A. J., McElrath, J. M., Blish, C. A., Gottardo, R., Smibert, P., & Satija, R. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573–3587.e29. link ↗Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-seq (MACS). Genome Biology, 9(9), R137. DOI ↗
AliasscMulti-omics, single-cell multi-omics, multimodal single-cell analysis, paired single-cell omicsChIP-seq analysis, peak detection, MACS peak calling, ChIP peak identification
Apparentées66
RésuméMulti-omics single-cell RNA-seq analysis integrates two or more molecular layers — such as gene expression (scRNA-seq), chromatin accessibility (scATAC-seq), or surface protein abundance (CITE-seq) — measured simultaneously or co-profiled in the same individual cells. By aligning these modalities in a shared low-dimensional space, researchers gain a mechanistically richer picture of cell identity, regulatory state, and phenotype than any single assay can provide.ChIP-seq peak calling is a computational pipeline that identifies genomic regions where a protein of interest — a transcription factor or histone modification — is enriched, based on sequencing reads from chromatin immunoprecipitation experiments. It converts raw sequencing data into a set of high-confidence binding or modification sites across the genome, enabling downstream analysis of gene regulation, chromatin state, and epigenetic mechanisms.
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ScholarGateComparer des méthodes: Multi-omics single-cell RNA-seq analysis · ChIP-seq Peak Calling. Consulté le 2026-06-17 sur https://scholargate.app/fr/compare