{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T17:05:44Z","timestamp":1779296744350,"version":"3.51.4"},"reference-count":169,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"BOF starting","award":["BOF\/STA\/201909\/030"],"award-info":[{"award-number":["BOF\/STA\/201909\/030"]}]},{"name":"Bijzonder Onderzoeksfonds","award":["01D28520"],"award-info":[{"award-number":["01D28520"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.<\/jats:p>","DOI":"10.1093\/bib\/bbae382","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T12:50:33Z","timestamp":1724935833000},"source":"Crossref","is-referenced-by-count":25,"title":["A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7329-1772","authenticated-orcid":false,"given":"Jens Uwe","family":"Loers","sequence":"first","affiliation":[{"name":"Lab for Computational Biology , Integromics and Gene Regulation (CBIGR), , Corneel Heymanslaan 10, 9000 Ghent , Belgium"},{"name":"Cancer Research Institute Ghent (CRIG) , Integromics and Gene Regulation (CBIGR), , Corneel Heymanslaan 10, 9000 Ghent , Belgium"},{"name":"Department of Biomedical Molecular Biology, Ghent University , Zwijnaarde-Technologiepark 71, 9052 Ghent , Belgium"},{"name":"Department of Biomolecular Medicine, Ghent University , Corneel Heymanslaan 10, 9000 Ghent , Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1975-0712","authenticated-orcid":false,"given":"Vanessa","family":"Vermeirssen","sequence":"additional","affiliation":[{"name":"Lab for Computational Biology , Integromics and Gene Regulation (CBIGR), , Corneel Heymanslaan 10, 9000 Ghent , Belgium"},{"name":"Cancer Research Institute Ghent (CRIG) , Integromics and Gene Regulation (CBIGR), , Corneel Heymanslaan 10, 9000 Ghent , Belgium"},{"name":"Department of Biomedical Molecular Biology, Ghent University , Zwijnaarde-Technologiepark 71, 9052 Ghent , Belgium"},{"name":"Department of Biomolecular Medicine, Ghent University , Corneel Heymanslaan 10, 9000 Ghent , Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"2024082912501513500_ref1","doi-asserted-by":"crossref","first-page":"6453","DOI":"10.1007\/s00018-021-03903-w","article-title":"Transcriptional enhancers and their communication with gene promoters","volume":"78","author":"Ray-Jones","year":"2021","journal-title":"Cell Mol Life Sci"},{"key":"2024082912501513500_ref2","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1186\/s13059-021-02322-1","article-title":"Mechanisms of enhancer action: the known and the unknown","volume":"22","author":"Panigrahi","year":"2021","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref3","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1038\/s41576-018-0089-8","article-title":"Chromatin accessibility and the regulatory epigenome","volume":"20","author":"Klemm","year":"2019","journal-title":"Nat Rev Genet"},{"key":"2024082912501513500_ref4","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.molcel.2022.12.032","article-title":"Deciphering the multi-scale, quantitative cis-regulatory code","volume":"83","author":"Kim","year":"2023","journal-title":"Mol Cell"},{"key":"2024082912501513500_ref5","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/s41576-023-00580-2","article-title":"Methods and applications for single-cell and spatial multi-omics","volume":"24","author":"Vandereyken","year":"2023","journal-title":"Nat Rev Genet"},{"key":"2024082912501513500_ref6","doi-asserted-by":"crossref","first-page":"3622","DOI":"10.1111\/ene.15819","article-title":"Molecular systems biology approaches to investigate mechanisms of gut\u2212brain communication in neurological diseases","volume":"30","author":"Vandemoortele","year":"2023","journal-title":"Eur J Neurol"},{"key":"2024082912501513500_ref7","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref8","doi-asserted-by":"crossref","first-page":"R83","DOI":"10.1186\/gb-2011-12-8-r83","article-title":"Cistrome: an integrative platform for transcriptional regulation studies","volume":"12","author":"Liu","year":"2011","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref9","doi-asserted-by":"crossref","DOI":"10.1038\/s41592-023-01938-4","article-title":"SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks","volume":"20","author":"Bravo, Gonz\u00e1lez-Blas","year":"2023","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref10","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41540-024-00340-w","article-title":"Evaluation of single-sample network inference methods for precision oncology","volume":"10","author":"Deschildre","year":"2024","journal-title":"npj Syst Biol Appl"},{"key":"2024082912501513500_ref11","doi-asserted-by":"crossref","first-page":"535","DOI":"10.3389\/fgene.2019.00535","article-title":"Inferring interaction networks from multi-omics data","volume":"10","author":"Hawe","year":"2019","journal-title":"Front Genet"},{"key":"2024082912501513500_ref12","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1038\/nmeth.3773","article-title":"Inferring causal molecular networks: empirical assessment through a community-based effort","volume":"13","author":"Hill","year":"2016","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref13","doi-asserted-by":"crossref","first-page":"14149","DOI":"10.1038\/s41598-020-70941-y","article-title":"Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms","volume":"10","author":"Morgan","year":"2020","journal-title":"Sci Rep"},{"key":"2024082912501513500_ref14","doi-asserted-by":"crossref","first-page":"3493","DOI":"10.1038\/s41467-020-17217-1","article-title":"Gene regulatory network inference from sparsely sampled noisy data","volume":"11","author":"Aalto","year":"2020","journal-title":"Nat Commun"},{"key":"2024082912501513500_ref15","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1186\/s13059-023-02959-0","article-title":"Inferring cell-type-specific causal gene regulatory networks during human neurogenesis","volume":"24","author":"Ayg\u00fcn","year":"2023","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref16","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1038\/s41587-022-01311-4","article-title":"Scalable single-cell RNA sequencing from full transcripts with Smart-seq3xpress","volume":"40","author":"Hagemann-Jensen","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref17","doi-asserted-by":"crossref","first-page":"1900188","DOI":"10.1002\/adbi.201900188","article-title":"Single-cell analysis using droplet microfluidics","volume":"4","author":"Matu\u0142a","year":"2020","journal-title":"Adv Biosyst"},{"key":"2024082912501513500_ref18","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1038\/s41580-023-00615-w","article-title":"The technological landscape and applications of single-cell multi-omics","volume":"24","author":"Baysoy","year":"2023","journal-title":"Nat Rev Mol Cell Biol"},{"key":"2024082912501513500_ref19","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1007\/s00018-022-04482-0","article-title":"Sample-multiplexing approaches for single-cell sequencing","volume":"79","author":"Zhang","year":"2022","journal-title":"Cell Mol Life Sci"},{"key":"2024082912501513500_ref20","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1186\/s13059-018-1603-1","article-title":"Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics","volume":"19","author":"Stoeckius","year":"2018","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref21","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1126\/science.aam8999","article-title":"Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding","volume":"360","author":"Rosenberg","year":"2018","journal-title":"Science"},{"key":"2024082912501513500_ref22","doi-asserted-by":"crossref","first-page":"21.29.1","DOI":"10.1002\/0471142727.mb2129s109","article-title":"ATAC-seq: a method for assaying chromatin accessibility genome-wide","volume":"109","author":"Buenrostro","year":"2015","journal-title":"Curr Protoc Mol Biol"},{"key":"2024082912501513500_ref23","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1038\/s41576-022-00509-1","article-title":"Characterizing cis-regulatory elements using single-cell epigenomics","volume":"24","author":"Preissl","year":"2022","journal-title":"Nat Rev Genet"},{"key":"2024082912501513500_ref24","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1126\/science.aab1601","article-title":"Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing","volume":"348","author":"Cusanovich","year":"2015","journal-title":"Science"},{"key":"2024082912501513500_ref25","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1101\/gr.276655.122","article-title":"Atlas-scale single-cell chromatin accessibility using nanowell-based combinatorial indexing","volume":"33","author":"O\u2019Connell","year":"2023","journal-title":"Genome Res"},{"key":"2024082912501513500_ref26","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1038\/s41594-019-0323-x","article-title":"An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome","volume":"26","author":"Zhu","year":"2019","journal-title":"Nat Struct Mol Biol"},{"key":"2024082912501513500_ref27","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1016\/j.cell.2020.09.056","article-title":"Chromatin potential identified by shared single-cell profiling of RNA and chromatin","volume":"183","author":"Ma","year":"2020","journal-title":"Cell"},{"key":"2024082912501513500_ref28","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1038\/s41587-019-0290-0","article-title":"High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell","volume":"37","author":"Chen","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref29","doi-asserted-by":"crossref","first-page":"6847","DOI":"10.1038\/s41467-022-34681-z","article-title":"A flexible cross-platform single-cell data processing pipeline","volume":"13","author":"Battenberg","year":"2022","journal-title":"Nat Commun"},{"key":"2024082912501513500_ref30","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s13059-016-0881-8","article-title":"A survey of best practices for RNA-seq data analysis","volume":"17","author":"Conesa","year":"2016","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref31","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1016\/j.csbj.2020.06.012","article-title":"Single-cell ATAC sequencing analysis: from data preprocessing to hypothesis generation","volume":"18","author":"Baek","year":"2020","journal-title":"Comput Struct Biotechnol J"},{"key":"2024082912501513500_ref32","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13059-020-1929-3","article-title":"From reads to insight: a hitchhiker\u2019s guide to ATAC-seq data analysis","volume":"21","author":"Yan","year":"2020","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref33","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1186\/s13059-019-1898-6","article-title":"Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis","volume":"20","author":"Sun","year":"2019","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref34","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1038\/s41576-023-00586-w","article-title":"Best practices for single-cell analysis across modalities","volume":"24","author":"Heumos","year":"2023","journal-title":"Nat Rev Genet"},{"key":"2024082912501513500_ref35","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1038\/s41586-022-05688-9","article-title":"Dissecting cell identity via network inference and in silico gene perturbation","volume":"614","author":"Kamimoto","year":"2023","journal-title":"Nature"},{"key":"2024082912501513500_ref36","doi-asserted-by":"crossref","first-page":"27151","DOI":"10.1073\/pnas.1911536116","article-title":"Deep learning for inferring gene relationships from single-cell expression data","volume":"116","author":"Yuan","year":"2019","journal-title":"Proc Natl Acad Sci"},{"key":"2024082912501513500_ref37","doi-asserted-by":"crossref","first-page":"e8","DOI":"10.1371\/journal.pbio.0050008","article-title":"Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles","volume":"5","author":"Faith","year":"2007","journal-title":"PLoS Biol"},{"key":"2024082912501513500_ref38","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1186\/1471-2105-7-S1-S7","article-title":"ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context","volume":"7","author":"Margolin","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2024082912501513500_ref39","doi-asserted-by":"crossref","first-page":"e12776","DOI":"10.1371\/journal.pone.0012776","article-title":"Inferring regulatory networks from expression data using tree-based methods","volume":"5","author":"Huynh-Thu","year":"2010","journal-title":"PloS One"},{"key":"2024082912501513500_ref40","doi-asserted-by":"crossref","first-page":"R36","DOI":"10.1186\/gb-2006-7-5-r36","article-title":"The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo","volume":"7","author":"Bonneau","year":"2006","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref41","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1039\/b908108a","article-title":"Transcription regulatory networks in Caenorhabditis elegans inferred through reverse engineering of gene expression profiles constitute biological hypotheses for metazoan development","volume":"5","author":"Vermeirssen","year":"2009","journal-title":"Mol Biosyst"},{"key":"2024082912501513500_ref42","doi-asserted-by":"crossref","first-page":"e1003252","DOI":"10.1371\/journal.pcbi.1003252","article-title":"Integrated module and gene-specific regulatory inference implicates upstream Signaling networks","volume":"9","author":"Roy","year":"2013","journal-title":"PLoS Comput Biol"},{"key":"2024082912501513500_ref43","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1186\/s12859-018-2217-z","article-title":"Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data","volume":"19","author":"Chen","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2024082912501513500_ref44","doi-asserted-by":"crossref","first-page":"bbab325","DOI":"10.1093\/bib\/bbab325","article-title":"DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data","volume":"22","author":"Chen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024082912501513500_ref45","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1093\/bioinformatics\/btx575","article-title":"SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles","volume":"34","author":"Papili Gao","year":"2018","journal-title":"Bioinformatics"},{"key":"2024082912501513500_ref46","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cels.2017.08.014","article-title":"Gene regulatory network inference from single-cell data using multivariate information measures","volume":"5","author":"Chan","year":"2017","journal-title":"Cell Systems"},{"key":"2024082912501513500_ref47","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.1093\/bioinformatics\/btx194","article-title":"SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation","volume":"33","author":"Matsumoto","year":"2017","journal-title":"Bioinformatics"},{"key":"2024082912501513500_ref48","doi-asserted-by":"crossref","article-title":"A Variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization","author":"Mahmood","DOI":"10.1101\/2022.09.09.507305"},{"key":"2024082912501513500_ref49","first-page":"vbac016","article-title":"decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics","volume":"2","author":"Badia-i-Mompel","year":"2022","journal-title":"Advances"},{"key":"2024082912501513500_ref50","doi-asserted-by":"crossref","first-page":"10934","DOI":"10.1093\/nar\/gkad841","article-title":"Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities","volume":"51","author":"M\u00fcller-Dott","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref51","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1186\/s13059-018-1575-1","article-title":"VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies","volume":"19","author":"Chen","year":"2018","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref52","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1038\/nmeth.2016","article-title":"Wisdom of crowds for robust gene network inference","volume":"9","author":"The DREAM5 Consortium","year":"2012","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref53","doi-asserted-by":"crossref","first-page":"4656","DOI":"10.1105\/tpc.114.131417","article-title":"Arabidopsis ensemble reverse engineered gene regulatory network discloses interconnected transcription factors in oxidative stress","volume":"26","author":"Vermeirssen","year":"2014","journal-title":"Plant Cell"},{"key":"2024082912501513500_ref54","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/s41592-019-0690-6","article-title":"Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data","volume":"17","author":"Pratapa","year":"2020","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref55","doi-asserted-by":"crossref","article-title":"Benchmarking joint multi-omics dimensionality reduction approaches for cancer study","author":"Cantini","DOI":"10.1038\/s41467-020-20430-7"},{"key":"2024082912501513500_ref56","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2019.00155","article-title":"A comprehensive survey of tools and software for active subnetwork identification","volume":"10","author":"Nguyen","year":"2019","journal-title":"Front Genet"},{"key":"2024082912501513500_ref57","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/s41592-019-0372-4","article-title":"Evaluating measures of association for single-cell transcriptomics","volume":"16","author":"Skinnider","year":"2019","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref58","doi-asserted-by":"crossref","first-page":"jkad004","DOI":"10.1093\/g3journal\/jkad004","article-title":"Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data","volume":"13","author":"McCalla","year":"2023","journal-title":"G3 Genes|Genomes|Genetics"},{"key":"2024082912501513500_ref59","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2021.617282","article-title":"Evaluating the reproducibility of single-cell gene regulatory network inference algorithms","volume":"12","author":"Kang","year":"2021","journal-title":"Front Genet"},{"key":"2024082912501513500_ref60","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1101\/gr.277488.122","article-title":"Dissecting and improving gene regulatory network inference using single-cell transcriptome data","volume":"33","author":"Xue","year":"2023","journal-title":"Genome Res"},{"key":"2024082912501513500_ref61","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/s41540-021-00208-3","article-title":"Constructing gene regulatory networks using epigenetic data","volume":"7","author":"Sonawane","year":"2021","journal-title":"npj Syst Biol Appl"},{"key":"2024082912501513500_ref62","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1101\/gr.260877.120","article-title":"MEDEA: analysis of transcription factor binding motifs in accessible chromatin","volume":"30","author":"Mariani","year":"2020","journal-title":"Genome Res"},{"key":"2024082912501513500_ref63","doi-asserted-by":"crossref","first-page":"eaav1898","DOI":"10.1126\/science.aav1898","article-title":"The chromatin accessibility landscape of primary human cancers","volume":"362","author":"Corces","year":"2018","journal-title":"Science"},{"key":"2024082912501513500_ref64","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1038\/s41592-021-01282-5","article-title":"Single-cell chromatin state analysis with Signac","volume":"18","author":"Stuart","year":"2021","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref65","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1016\/j.molcel.2018.06.044","article-title":"Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data","volume":"71","author":"Pliner","year":"2018","journal-title":"Mol Cell"},{"key":"2024082912501513500_ref66","doi-asserted-by":"crossref","first-page":"e10979","DOI":"10.15252\/msb.202210979","article-title":"Changes in chromatin accessibility are not concordant with transcriptional changes for single-factor perturbations","volume":"18","author":"Kiani","year":"2022","journal-title":"Mol Syst Biol"},{"key":"2024082912501513500_ref67","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1101\/gr.249326.119","article-title":"Accessibility of promoter DNA is not the primary determinant of chromatin-mediated gene regulation","volume":"29","author":"Chereji","year":"2019","journal-title":"Genome Res"},{"key":"2024082912501513500_ref68","doi-asserted-by":"crossref","first-page":"e21","DOI":"10.1093\/nar\/gkw1160","article-title":"A prior-based integrative framework for functional transcriptional regulatory network inference","volume":"45","author":"Siahpirani","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref69","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1016\/j.cels.2022.08.004","article-title":"Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions","volume":"13","author":"Jiang","year":"2022","journal-title":"Cell Systems"},{"key":"2024082912501513500_ref70","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1186\/s13059-022-02682-2","article-title":"Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG","volume":"23","author":"Duren","year":"2022","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref71","doi-asserted-by":"crossref","first-page":"e11627","DOI":"10.15252\/msb.202311627","article-title":"GRaNIE and GRaNPA: inference and evaluation of enhancer-mediated gene regulatory networks","volume":"19","author":"Kamal","year":"2023","journal-title":"Mol Syst Biol"},{"key":"2024082912501513500_ref72","doi-asserted-by":"crossref","first-page":"7966","DOI":"10.1093\/nar\/gkab598","article-title":"ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination","volume":"49","author":"Xu","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref73","doi-asserted-by":"crossref","first-page":"100166","DOI":"10.1016\/j.xgen.2022.100166","article-title":"Functional inference of gene regulation using single-cell multi-omics","volume":"2","author":"Kartha","year":"2022","journal-title":"Cell Genomics"},{"key":"2024082912501513500_ref74","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1038\/s43588-024-00597-5","article-title":"Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data","volume":"4","author":"Osorio","year":"2024","journal-title":"Nat Comput Sci"},{"key":"2024082912501513500_ref75","doi-asserted-by":"crossref","first-page":"btae143","DOI":"10.1093\/bioinformatics\/btae143","article-title":"Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS","volume":"40","author":"Trimbour","year":"2024","journal-title":"Bioinformatics"},{"key":"2024082912501513500_ref76","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1186\/s13059-019-1854-5","article-title":"Assessment of computational methods for the analysis of single-cell ATAC-seq data","volume":"20","author":"Chen","year":"2019","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref77","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1038\/s41588-021-00790-6","article-title":"ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis","volume":"53","author":"Granja","year":"2021","journal-title":"Nat Genet"},{"key":"2024082912501513500_ref78","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1038\/s41592-019-0367-1","article-title":"cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data","volume":"16","author":"Bravo Gonz\u00e1lez-Blas","year":"2019","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref79","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1186\/s13059-019-1812-2","article-title":"MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions","volume":"20","author":"Baran","year":"2019","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref80","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1038\/s41587-023-01716-9","article-title":"SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data","volume":"41","author":"Persad","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref81","doi-asserted-by":"crossref","first-page":"3505","DOI":"10.1038\/s41467-022-31104-x","article-title":"Diagonal integration of multimodal single-cell data: potential pitfalls and paths forward","volume":"13","author":"Xu","year":"2022","journal-title":"Nat Commun"},{"key":"2024082912501513500_ref82","first-page":"1","article-title":"SIMBA: single-cell embedding along with features","volume":"21","author":"Chen","year":"2023","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref83","first-page":"eabl7393","article-title":"DIRECT-NET: an efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data. Science","volume":"8","author":"Zhang","year":"2022","journal-title":"Advances"},{"key":"2024082912501513500_ref84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41586-022-05279-8","article-title":"Inferring and perturbing cell fate regulomes in human brain organoids","volume":"621","author":"Fleck","year":"2023","journal-title":"Nature"},{"key":"2024082912501513500_ref85","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1038\/s41587-022-01284-4","article-title":"Multi-omics single-cell data integration and regulatory inference with graph-linked embedding","volume":"40","author":"Cao","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41587-023-02040-y","article-title":"Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS","volume":"42","author":"He","year":"2024","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref87","doi-asserted-by":"crossref","first-page":"293\u2013304","DOI":"10.1038\/s41587-023-01767-y","article-title":"Dictionary learning for integrative, multimodal and scalable single-cell analysis","volume":"42","author":"Hao","year":"2024","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref88","doi-asserted-by":"crossref","first-page":"D316","DOI":"10.1093\/nar\/gkab996","article-title":"ReMap 2022: a database of human, mouse, drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments","volume":"50","author":"Hammal","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref89","doi-asserted-by":"crossref","first-page":"D794","DOI":"10.1093\/nar\/gkx1081","article-title":"The Encyclopedia of DNA elements (ENCODE): data portal update","volume":"46","author":"Davis","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref90","doi-asserted-by":"crossref","first-page":"2728","DOI":"10.1016\/j.csbj.2022.05.045","article-title":"Analysis of the landscape of human enhancer sequences in biological databases","volume":"20","author":"Mulero Hern\u00e1ndez","year":"2022","journal-title":"Comput Struct Biotechnol J"},{"key":"2024082912501513500_ref91","doi-asserted-by":"crossref","first-page":"gkz980","DOI":"10.1093\/nar\/gkz980","article-title":"EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue\/cell types across nine species","volume":"48","author":"Gao","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref92","doi-asserted-by":"crossref","first-page":"e21856","DOI":"10.7554\/eLife.21856","article-title":"An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites","volume":"6","author":"Skene","year":"2017","journal-title":"Elife"},{"key":"2024082912501513500_ref93","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1038\/s41467-019-09982-5","article-title":"CUT&Tag for efficient epigenomic profiling of small samples and single cells","volume":"10","author":"Kaya-Okur","year":"2019","journal-title":"Nat Commun"},{"key":"2024082912501513500_ref94","doi-asserted-by":"crossref","first-page":"2293411","DOI":"10.1080\/15592294.2023.2293411","article-title":"Cut&tag: a powerful epigenetic tool for chromatin profiling","volume":"19","author":"Fu","year":"2024","journal-title":"Epigenetics"},{"key":"2024082912501513500_ref95","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1038\/s41587-021-00869-9","article-title":"Single-cell CUT&tag profiles histone modifications and transcription factors in complex tissues","volume":"39","author":"Bartosovic","year":"2021","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref96","doi-asserted-by":"crossref","first-page":"101490","DOI":"10.1016\/j.xpro.2022.101490","article-title":"High-throughput sequencing SELEX for the determination of DNA-binding protein specificities in vitro","volume":"3","author":"Pantier","year":"2022","journal-title":"STAR Protocols"},{"key":"2024082912501513500_ref97","doi-asserted-by":"crossref","first-page":"e20","DOI":"10.1093\/nar\/gkad1240","article-title":"Less-is-more: selecting transcription factor binding regions informative for motif inference","volume":"52","author":"Xu","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref98","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1093\/bioinformatics\/btr064","article-title":"FIMO: scanning for occurrences of a given motif","volume":"27","author":"Grant","year":"2011","journal-title":"Bioinformatics"},{"key":"2024082912501513500_ref99","doi-asserted-by":"crossref","first-page":"W670","DOI":"10.1093\/nar\/gkac312","article-title":"RSAT 2022: regulatory sequence analysis tools","volume":"50","author":"Santana-Garcia","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref100","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.molcel.2010.05.004","article-title":"Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities","volume":"38","author":"Heinz","year":"2010","journal-title":"Mol Cell"},{"key":"2024082912501513500_ref101","volume-title":"motifmatchr: Fast Motif Matching in R","author":"Schep","year":"2023"},{"key":"2024082912501513500_ref102","article-title":"GimmeMotifs: an analysis framework for transcription factor motif analysis","author":"Bruse","year":"2018","journal-title":"bioRxiv"},{"key":"2024082912501513500_ref103","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1038\/s41467-023-37960-5","article-title":"Transcription factor binding site orientation and order are major drivers of gene regulatory activity","volume":"14","author":"Georgakopoulos-Soares","year":"2023","journal-title":"Nat Commun"},{"key":"2024082912501513500_ref104","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.gde.2017.02.007","article-title":"Transcription factor\u2013DNA binding: beyond binding site motifs","volume":"43","author":"Inukai","year":"2017","journal-title":"Curr Opin Genet Dev"},{"key":"2024082912501513500_ref105","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1038\/nmeth.4401","article-title":"chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data","volume":"14","author":"Schep","year":"2017","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref106","volume":"24","journal-title":"Brief Bioinform"},{"key":"2024082912501513500_ref107","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1007\/s00438-020-01675-9","article-title":"Pioneer factors and their in vitro identification methods","volume":"295","author":"Yu","year":"2020","journal-title":"Mol Genet Genomics"},{"key":"2024082912501513500_ref108","doi-asserted-by":"crossref","first-page":"4267","DOI":"10.1038\/s41467-020-18035-1","article-title":"ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation","volume":"11","author":"Bentsen","year":"2020","journal-title":"Nat Commun"},{"key":"2024082912501513500_ref109","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nbt.2798","article-title":"Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape","volume":"32","author":"Sherwood","year":"2014","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref110","doi-asserted-by":"crossref","first-page":"e201","DOI":"10.1093\/nar\/gkt850","article-title":"Wellington: a novel method for the accurate identification of digital genomic footprints from DNase-seq data","volume":"41","author":"Piper","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref111","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s13059-019-1642-2","article-title":"Identification of transcription factor binding sites using ATAC-seq","volume":"20","author":"Li","year":"2019","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref112","doi-asserted-by":"crossref","first-page":"105359","DOI":"10.1016\/j.isci.2022.105359","article-title":"IReNA: integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles","volume":"25","author":"Jiang","year":"2022","journal-title":"iScience"},{"key":"2024082912501513500_ref113","doi-asserted-by":"crossref","article-title":"Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multi-omics","author":"Wang","DOI":"10.1101\/2022.09.14.508036"},{"key":"2024082912501513500_ref114","doi-asserted-by":"crossref","first-page":"e1010863","DOI":"10.1371\/journal.pcbi.1010863","article-title":"maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks","volume":"19","author":"Cazares","year":"2023","journal-title":"PLoS Comput Biol"},{"key":"2024082912501513500_ref115","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1186\/s13059-022-02723-w","article-title":"BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin","volume":"23","author":"Kshirsagar","year":"2022","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref116","first-page":"vbad003","article-title":"scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference. Bioinformatics","volume":"3","author":"Li","year":"2023","journal-title":"Advances"},{"key":"2024082912501513500_ref117","doi-asserted-by":"crossref","first-page":"6249","DOI":"10.1093\/nar\/gkab443","article-title":"Altering transcription factor binding reveals comprehensive transcriptional kinetics of a basic gene","volume":"49","author":"Popp","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref118","doi-asserted-by":"crossref","first-page":"554","DOI":"10.3390\/genes10070554","article-title":"Chromatin interaction analysis with updated ChIA-PET tool (V3)","volume":"10","author":"Li","year":"2019","journal-title":"Genes (Basel)"},{"key":"2024082912501513500_ref119","article-title":"Promoter capture hi-C: high-resolution, genome-wide profiling of promoter interactions","volume":"136","author":"Schoenfelder","year":"2018","journal-title":"J Vis Exp"},{"key":"2024082912501513500_ref120","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1038\/s41587-021-00998-1","article-title":"Single-cell measurement of higher-order 3D genome organization with scSPRITE","volume":"40","author":"Arrastia","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref121","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1038\/nature11082","article-title":"Topological domains in mammalian genomes identified by analysis of chromatin interactions","volume":"485","author":"Dixon","year":"2012","journal-title":"Nature"},{"key":"2024082912501513500_ref122","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1016\/j.cell.2014.11.021","article-title":"A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping","volume":"159","author":"Rao","year":"2014","journal-title":"Cell"},{"key":"2024082912501513500_ref123","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1016\/j.cell.2016.09.037","article-title":"Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters","volume":"167","author":"Javierre","year":"2016","journal-title":"Cell"},{"key":"2024082912501513500_ref124","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.ajhg.2021.01.001","article-title":"Topologically associating domain boundaries that are stable across diverse cell types are evolutionarily constrained and enriched for heritability","volume":"108","author":"McArthur","year":"2021","journal-title":"Am J Hum Genet"},{"key":"2024082912501513500_ref125","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1038\/s41596-021-00651-w","article-title":"Capture-C: a modular and flexible approach for high-resolution chromosome conformation capture","volume":"17","author":"Downes","year":"2022","journal-title":"Nat Protoc"},{"key":"2024082912501513500_ref126","doi-asserted-by":"crossref","first-page":"lqac023","DOI":"10.1093\/nargab\/lqac023","article-title":"scREMOTE: using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model","volume":"4","author":"Tran","year":"2022","journal-title":"NAR Genomics and Bioinformatics"},{"key":"2024082912501513500_ref127","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1038\/s41467-022-29697-4","article-title":"The 4D Nucleome data portal as a resource for searching and visualizing curated nucleomics data","volume":"13","author":"Reiff","year":"2022","journal-title":"Nat Commun"},{"key":"2024082912501513500_ref128","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s13072-019-0260-2","article-title":"Combined analysis of dissimilar promoter accessibility and gene expression profiles identifies tissue-specific genes and actively repressed networks","volume":"12","author":"Starks","year":"2019","journal-title":"Epigenetics Chromatin"},{"key":"2024082912501513500_ref129","doi-asserted-by":"crossref","first-page":"6380","DOI":"10.1038\/s41467-021-26629-6","article-title":"A compendium of chromatin contact maps reflecting regulation by chromatin remodelers in budding yeast","volume":"12","author":"Jo","year":"2021","journal-title":"Nat Commun"},{"key":"2024082912501513500_ref130","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1038\/s41588-024-01682-1","article-title":"Tissue-specific enhancer\u2013gene maps from multimodal single-cell data identify causal disease alleles","volume":"56","author":"Sakaue","year":"2024","journal-title":"Nat Genet"},{"key":"2024082912501513500_ref131","doi-asserted-by":"crossref","first-page":"10347","DOI":"10.1093\/nar\/gkab841","article-title":"Predict long-range enhancer regulation based on protein\u2013protein interactions between transcription factors","volume":"49","author":"Wang","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref132","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1093\/hmg\/ddg180","article-title":"A long-range Shh enhancer regulates expression in the developing limb and fin and is associated with preaxial polydactyly","volume":"12","author":"Lettice","year":"2003","journal-title":"Hum Mol Genet"},{"key":"2024082912501513500_ref133","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s13059-020-1932-8","article-title":"scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles","volume":"21","author":"Jin","year":"2020","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref134","doi-asserted-by":"crossref","first-page":"E4914","DOI":"10.1073\/pnas.1704553114","article-title":"Modeling gene regulation from paired expression and chromatin accessibility data","volume":"114","author":"Duren","year":"2017","journal-title":"Proc Natl Acad Sci"},{"key":"2024082912501513500_ref135","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1038\/s41467-021-21583-9","article-title":"Comprehensive analysis of single cell ATAC-seq data with SnapATAC","volume":"12","author":"Fang","year":"2021","journal-title":"Nat Commun"},{"key":"2024082912501513500_ref136","first-page":"1","article-title":"A fast, scalable and versatile tool for analysis of single-cell omics data","author":"Zhang","year":"2024","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref137","doi-asserted-by":"crossref","first-page":"e64832","DOI":"10.1371\/journal.pone.0064832","article-title":"Passing messages between biological networks to refine predicted interactions","volume":"8","author":"Glass","year":"2013","journal-title":"PloS One"},{"key":"2024082912501513500_ref138","doi-asserted-by":"crossref","first-page":"e9506","DOI":"10.15252\/msb.20209506","article-title":"Integrated regulatory models for inference of subtype-specific susceptibilities in glioblastoma","volume":"16","author":"Liu","year":"2020","journal-title":"Mol Syst Biol"},{"key":"2024082912501513500_ref139","doi-asserted-by":"crossref","first-page":"e38","DOI":"10.1093\/nar\/gkad053","article-title":"Using single cell atlas data to reconstruct regulatory networks","volume":"51","author":"Song","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2024082912501513500_ref140","first-page":"1","article-title":"Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data","volume":"42","author":"Yuan","year":"2024","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref141","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/s41586-018-0414-6","article-title":"RNA velocity of single cells","volume":"560","author":"La Manno","year":"2018","journal-title":"Nature"},{"key":"2024082912501513500_ref142","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1038\/s41587-020-0591-3","article-title":"Generalizing RNA velocity to transient cell states through dynamical modeling","volume":"38","author":"Bergen","year":"2020","journal-title":"Nat Biotechnol"},{"key":"2024082912501513500_ref143","doi-asserted-by":"crossref","first-page":"e1010492","DOI":"10.1371\/journal.pcbi.1010492","article-title":"RNA velocity unraveled","volume":"18","author":"Gorin","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"2024082912501513500_ref144","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1038\/s41592-024-02303-9","article-title":"CellRank 2: unified fate mapping in multiview single-cell data","volume":"21","author":"Weiler","year":"2024","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref145","doi-asserted-by":"crossref","first-page":"3064","DOI":"10.1038\/s41467-023-38637-9","article-title":"Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets","volume":"14","author":"Zhang","year":"2023","journal-title":"Nat Commun"},{"key":"2024082912501513500_ref146","doi-asserted-by":"crossref","first-page":"i394","DOI":"10.1093\/bioinformatics\/btad267","article-title":"scKINETICS: inference of regulatory velocity with single-cell transcriptomics data","volume":"39","author":"Burdziak","year":"2023","journal-title":"Bioinformatics"},{"key":"2024082912501513500_ref147","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1186\/s12864-022-08637-y","article-title":"Harnessing changes in open chromatin determined by ATAC-seq to generate insulin-responsive reporter constructs","volume":"23","author":"Merrill","year":"2022","journal-title":"BMC Genomics"},{"key":"2024082912501513500_ref148","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1101\/gr.257063.119","article-title":"Time course regulatory analysis based on paired expression and chromatin accessibility data","volume":"30","author":"Duren","year":"2020","journal-title":"Genome Res"},{"key":"2024082912501513500_ref149","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1038\/s41594-023-01027-2","article-title":"The mediator complex regulates enhancer-promoter interactions","volume":"30","author":"Ramasamy","year":"2023","journal-title":"Nat Struct Mol Biol"},{"key":"2024082912501513500_ref150","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1186\/s13059-023-02934-9","article-title":"Epiphany: predicting hi-C contact maps from 1D epigenomic signals","volume":"24","author":"Yang","year":"2023","journal-title":"Genome Biol"},{"key":"2024082912501513500_ref151","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.cell.2021.12.018","article-title":"Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches","volume":"185","author":"Guilliams","year":"2022","journal-title":"Cell"},{"key":"2024082912501513500_ref152","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s41586-019-1373-2","article-title":"A human liver cell atlas reveals heterogeneity and epithelial progenitors","volume":"572","author":"Aizarani","year":"2019","journal-title":"Nature"},{"key":"2024082912501513500_ref153","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1038\/s41576-023-00618-5","article-title":"Gene regulatory network inference in the era of single-cell multi-omics","volume":"24","author":"Badia-i-Mompel","year":"2023","journal-title":"Nat Rev Genet"},{"key":"2024082912501513500_ref154","doi-asserted-by":"crossref","first-page":"1191961","DOI":"10.3389\/fbinf.2023.1191961","article-title":"Omics data integration in computational biology viewed through the prism of machine learning paradigms","volume":"3","author":"Fouch\u00e9","year":"2023","journal-title":"Front Bioinform"},{"key":"2024082912501513500_ref155","article-title":"ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double dipping","author":"Song","year":"2023","journal-title":"bioRxiv"},{"key":"2024082912501513500_ref156","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1038\/nmeth.4077","article-title":"OmniPath: guidelines and gateway for literature-curated signaling pathway resources","volume":"13","author":"T\u00fcrei","year":"2016","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref157","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1038\/nmeth.4177","article-title":"Pooled CRISPR screening with single-cell transcriptome readout","volume":"14","author":"Datlinger","year":"2017","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref158","doi-asserted-by":"crossref","first-page":"e1441","DOI":"10.1002\/widm.1441","article-title":"Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results","volume":"12","author":"Nie\u00dfl","year":"2022","journal-title":"WIREs Data Mining and Knowledge Discovery"},{"key":"2024082912501513500_ref159","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.devcel.2019.10.003","article-title":"Reconstruction of the global neural crest gene regulatory network In vivo","volume":"51","author":"Williams","year":"2019","journal-title":"Dev Cell"},{"key":"2024082912501513500_ref160","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41540-023-00312-6","article-title":"Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data","volume":"9","author":"Kim","year":"2023","journal-title":"npj Syst Biol Appl"},{"key":"2024082912501513500_ref161","doi-asserted-by":"crossref","DOI":"10.1101\/2024.01.25.577262","article-title":"A biophysical model for ATAC-seq data analysis","author":"Felce","year":"2024"},{"key":"2024082912501513500_ref162","article-title":"MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data","author":"Browaeys","year":"2023","journal-title":"bioRxiv"},{"key":"2024082912501513500_ref163","doi-asserted-by":"crossref","first-page":"btad765","DOI":"10.1093\/bioinformatics\/btad765","article-title":"ENTRAIN: integrating trajectory inference and gene regulatory networks with spatial data to co-localize the receptor\u2013ligand interactions that specify cell fate","volume":"39","author":"Kyaw","year":"2023","journal-title":"Bioinformatics"},{"key":"2024082912501513500_ref164","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1101\/gr.277960.123","article-title":"Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE","volume":"34","author":"Lin","year":"2024","journal-title":"Genome Res"},{"key":"2024082912501513500_ref165","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41592-024-02201-0","article-title":"scGPT: toward building a foundation model for single-cell multi-omics using generative AI","volume":"21","author":"Cui","year":"2024","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref166","article-title":"CeSpGRN: inferring cell-specific gene regulatory networks from single cell multi-omics and spatial data","author":"Zhang","year":"2023"},{"key":"2024082912501513500_ref167","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1042\/EBC20190033","article-title":"Sensitivity of transcription factors to DNA methylation","volume":"63","author":"H\u00e9berl\u00e9","year":"2019","journal-title":"Essays Biochem"},{"key":"2024082912501513500_ref168","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41592-022-01461-y","article-title":"NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells","volume":"19","author":"Chen","year":"2022","journal-title":"Nat Methods"},{"key":"2024082912501513500_ref169","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1038\/nmeth.4380","article-title":"Simultaneous epitope and transcriptome measurement in single cells","volume":"14","author":"Stoeckius","year":"2017","journal-title":"Nat Methods"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/5\/bbae382\/58963023\/bbae382.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/5\/bbae382\/58963023\/bbae382.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T19:19:34Z","timestamp":1732475974000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae382\/7745032"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,25]]},"references-count":169,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,7,25]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae382","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,9]]},"published":{"date-parts":[[2024,7,25]]},"article-number":"bbae382"}}