{"doi":"10.1101/gr.169508.113","title":"ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs","abstract":"<jats:p>Accurate reconstruction of the regulatory networks that control gene expression is one of the key current challenges in molecular biology. Although gene expression and chromatin state dynamics are ultimately encoded by constellations of binding sites recognized by regulators such as transcriptions factors (TFs) and microRNAs (miRNAs), our understanding of this regulatory code and its context-dependent read-out remains very limited. Given that there are thousands of potential regulators in mammals, it is not practical to use direct experimentation to identify which of these play a key role for a particular system of interest. We developed a methodology that models gene expression or chromatin modifications in terms of genome-wide predictions of regulatory sites and completely automated it into a web-based tool called ISMARA (<jats:underline>I</jats:underline>ntegrated<jats:underline>S</jats:underline>ystem for<jats:underline>M</jats:underline>otif<jats:underline>A</jats:underline>ctivity<jats:underline>R</jats:underline>esponse<jats:underline>A</jats:underline>nalysis). Given only gene expression or chromatin state data across a set of samples as input, ISMARA identifies the key TFs and miRNAs driving expression/chromatin changes and makes detailed predictions regarding their regulatory roles. These include predicted activities of the regulators across the samples, their genome-wide targets, enriched gene categories among the targets, and direct interactions between the regulators. Applying ISMARA to data sets from well-studied systems, we show that it consistently identifies known key regulators ab initio. We also present a number of novel predictions including regulatory interactions in innate immunity, a master regulator of mucociliary differentiation, TFs consistently disregulated in cancer, and TFs that mediate specific chromatin modifications.</jats:p>","journal":"Genome Research","year":2014,"id":17060,"datarank":9.414042225297495,"base_score":5.8971538676367405,"endowment":5.8971538676367405,"self_citation_contribution":0.8845730801455112,"citation_network_contribution":8.529469145151984,"self_endowment_contribution":0.8845730801455112,"citer_contribution":8.529469145151984,"corpus_percentile":null,"corpus_rank":null,"citation_count":363,"citer_count":200,"citers_with_citation_signal":200,"citers_with_endowment":200,"datacite_reuse_total":25,"is_dataset":false,"is_dataset_confidence":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":123390,"name":"Mikhail Pachkov","orcid":null,"position":1,"is_corresponding":false},{"id":123391,"name":"Phil Arnold","orcid":null,"position":2,"is_corresponding":false},{"id":123392,"name":"Andreas J. Gruber","orcid":null,"position":3,"is_corresponding":false},{"id":123393,"name":"Mihaela Zavolan","orcid":null,"position":4,"is_corresponding":false},{"id":77715,"name":"Erik van Nimwegen","orcid":"0000-0001-6338-1312","position":5,"is_corresponding":false},{"id":77607,"name":"Piotr J. Balwierz","orcid":"0000-0002-1548-4605","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":5.8971538676367405,"endowment":5.8971538676367405,"datacite_reuse_total":25,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"24515121","pmcid":"PMC4009616","openalex_id":"https://openalex.org/W2115260093","authors":[],"funders":[{"funder_name":"Swiss National Science Foundation","grant_id":"118318","title":null}],"total_grants":1,"fwci":null,"citation_percentile":null,"influential_citations":29,"citation_trend":[{"year":2014,"count":4},{"year":2015,"count":12},{"year":2016,"count":25},{"year":2017,"count":24},{"year":2018,"count":20},{"year":2019,"count":37},{"year":2020,"count":39},{"year":2021,"count":33},{"year":2022,"count":47},{"year":2023,"count":47},{"year":2024,"count":42},{"year":2025,"count":24},{"year":2026,"count":9}],"oa_status":"bronze","license":"cc-by-nc","oa_locations":[{"url":"http://genome.cshlp.org/content/24/5/869.full.pdf","host_type":"journal"},{"url":"http://genome.cshlp.org/content/24/5/869.full.pdf","host_type":"HYBRID"},{"url":"http://genome.cshlp.org/content/24/5/869.full.pdf","host_type":"publisher"},{"url":"https://syndication.highwire.org/content/doi/10.1101/gr.169508.113","host_type":"publisher"},{"url":"https://doi.org/10.1101/gr.169508.113","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/24515121","host_type":"repository"},{"url":"http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1211bs4q2j2qe8","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4009616","host_type":"repository"},{"url":"http://genome.cshlp.org/cgi/content/short/24/5/869","host_type":"repository"},{"url":"https://kops.uni-konstanz.de/server/api/core/bitstreams/63e6f5e7-b085-415f-8a56-e82eb584487b/content","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC4009616","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC4009616?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Cancer-related molecular mechanisms research","Genomics and Chromatin Dynamics","RNA Research and Splicing","Medicine","Biology","Computer Science","Algorithms","Animals","Chromatin Assembly and Disassembly","Genome, Human","Humans","Mice","Models, Genetic","Nucleotide Motifs","Regulatory Sequences, Nucleic Acid","Sequence Analysis, DNA"],"mesh_terms":["Algorithms","Animals","Humans","Models, Genetic","Regulatory Sequences, Nucleic Acid","Genome, Human","Sequence Analysis, DNA","Chromatin Assembly and Disassembly","Mice","Nucleotide Motifs"],"keywords":["Chromatin","Biology","Computational biology","Regulation of gene expression","microRNA","ChIA-PET","Genetics","Transcription factor","Gene","Regulator","Genome","Gene regulatory network","Context (archaeology)","Regulatory sequence","Gene expression","Chromatin remodeling"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Peace, Justice and strong institutions"}],"linked_datasets":[{"doi":"10.6084/m9.figshare.12284981.v1","title":"Additional file 1 of Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.12284981","title":"Additional file 1 of Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.12285044.v1","title":"Additional file 8 of Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.12285044","title":"Additional file 8 of Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.14379629.v1","title":"Additional file 1 of The transcriptional landscape of a hepatoma cell line grown on scaffolds of extracellular matrix proteins","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.14379629","title":"Additional file 1 of The transcriptional landscape of a hepatoma cell line grown on scaffolds of extracellular matrix proteins","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.19029216.v1","title":"Additional file 1 of Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.19029216","title":"Additional file 1 of Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.23961997.v1","title":"Additional file 1 of Predicting the impact of sequence motifs on gene regulation using single-cell data","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.23961997","title":"Additional file 1 of Predicting the impact of sequence motifs on gene regulation using single-cell data","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.23962000.v1","title":"Additional file 2 of Predicting the impact of sequence motifs on gene regulation using single-cell data","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.23962000","title":"Additional file 2 of Predicting the impact of sequence motifs on gene regulation using single-cell data","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.23962003.v1","title":"Additional file 3 of Predicting the impact of sequence motifs on gene regulation using single-cell data","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.23962003","title":"Additional file 3 of Predicting the impact of sequence motifs on gene regulation using single-cell data","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.26560703.v1","title":"Additional file 1 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.26560703","title":"Additional file 1 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.26560706.v1","title":"Additional file 2 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.26560706","title":"Additional file 2 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.26560709.v1","title":"Additional file 3 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.26560709","title":"Additional file 3 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.26560721","title":"Additional file 7 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"Dataset"},{"doi":"10.6084/m9.figshare.26560721.v1","title":"Additional file 7 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"Dataset"},{"doi":"10.6084/m9.figshare.26560715","title":"Additional file 5 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"Dataset"},{"doi":"10.6084/m9.figshare.26560724","title":"Additional file 8 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"Dataset"},{"doi":"10.6084/m9.figshare.26560724.v1","title":"Additional file 8 of xcore: an R package for inference of gene expression regulators","publisher":"figshare","resource_type":"Dataset"}],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"geo"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-02T16:26:16.413601Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}