ArticleGenome biology2020
Integrative analyses of single-cell transcriptome and regulome using MAESTRO.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers.
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Who cites it
138 citing papers in PubMed.
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- Flow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis.bioRxiv : the preprint server for biology · 2026Article
- scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.Genomics, proteomics & bioinformatics · 2026Article
- Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.NAR genomics and bioinformatics · 2026Article
- Accurate, scalable and cross-platform cell identification for high-resolution spatial transcriptomics.Nature genetics · 2026Article
- Quantifying Cross-Modal Association Confidence for Single-Cell RNA-ATAC Integration.bioRxiv : the preprint server for biology · 2026Article
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- Single-cell chromatin accessibility and cis-regulatory element analyses in plants using the scPlantReg platform.Nature plants · 2026Article
- Single-cell data integration across weakly linked modalities.PLoS computational biology · 2026Article
- Benchmarking component choices for unpaired single cell RNA and epigenomic integration.Genome biology · 2026Article
- Cell neighborhood topology directs rare cell population identification.Nature communications · 2026Article
- A pipeline for single-cell chromatin accessibility data analysis.Blood science (Baltimore, Md.) · 2026Article
- BARTsc identifies key transcriptional regulators from single-cell omics data.bioRxiv : the preprint server for biology · 2026Article
- Bridging unpaired single-cell multimodal data for integrative analyses with SuperMap.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- BiCLUM: Bilateral contrastive learning for unpaired single-cell multi-omics integration.PLoS computational biology · 2026Article
- Data driven network inference and longitudinal transcriptomics unveil dynamic regulation in Chronic Lymphocytic Leukaemia models.NPJ systems biology and applications · 2026Article
- Pan-Cancer Analysis of a Depression-Related Gene Signature Identifies an Immunosuppressive Tumor Microenvironment and Links to Immunotherapy Response in Breast Cancer.Breast cancer (Dove Medical Press) · 2026Article
- Integration of tumor microenvironment and metabolic signatures reveals TMPI-defined prognostic and immunotherapy-relevant phenotypes in breast cancer.Frontiers in pharmacology · 2026Article
- Multi-omics to study chronic respiratory diseases and viral infections.European respiratory review : an official journal of the European Respiratory Society · 2026Review
78 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.