Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction
Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction
Abstract The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.
Brors Benedikt、Cadenas Cristina、Ebert Peter、Barann Matthias、Zipprich Gideon、Eils J¨1rgen、Hamann Alf、Lengauer Thomas、Rosenstiel Philip、Felder B?rbel、Hengstler Jan G.、Gasparoni Nina、Schulz Marcel H.、Amirabad Azim Dehghani、Nordstr?m Karl、Walter J?rn、Gianmoena Kathrin、Polansky Julia K.、Xiong Jieyi、Schmidt Florian、Hutter Barbara、Sinha Anupam、Gasparoni Gilles、Chen Wei、Fr?hler Sebastian、Ardakani Fatemeh Behjati
Applied BioinformaticsLeibniz Research Centre for Working Environment and Human Factors IfADoComputational Biology & Applied Algorithmics, Max Planck Institute for Informatics||International Max Planck Research School for Computer ScienceInstitute of Clinical Molecular Biology, Christian-Albrechts-UniversityData Management and Genomics ITData Management and Genomics ITInternational Max Planck Research School for Computer ScienceComputational Biology & Applied Algorithmics, Max Planck Institute for InformaticsInstitute of Clinical Molecular Biology, Christian-Albrechts-UniversityData Management and Genomics ITLeibniz Research Centre for Working Environment and Human Factors IfADoDepartment of Genetics, University of SaarlandCluster of Excellence for Multimodal Computing and Interaction||Computational Biology & Applied Algorithmics, Max Planck Institute for InformaticsCluster of Excellence for Multimodal Computing and Interaction||Computational Biology & Applied Algorithmics, Max Planck Institute for Informatics||International Max Planck Research School for Computer ScienceDepartment of Genetics, University of SaarlandDepartment of Genetics, University of SaarlandLeibniz Research Centre for Working Environment and Human Factors IfADoExperimental Rheumatology, German Rheumatism Research CentreBerlin Institute for Medical Systems Biology, Max-Delbr¨1ck Center for Molecular MedicineCluster of Excellence for Multimodal Computing and Interaction||Computational Biology & Applied Algorithmics, Max Planck Institute for InformaticsApplied BioinformaticsInstitute of Clinical Molecular Biology, Christian-Albrechts-UniversityDepartment of Genetics, University of SaarlandBerlin Institute for Medical Systems Biology, Max-Delbr¨1ck Center for Molecular MedicineBerlin Institute for Medical Systems Biology, Max-Delbr¨1ck Center for Molecular MedicineCluster of Excellence for Multimodal Computing and Interaction||Computational Biology & Applied Algorithmics, Max Planck Institute for Informatics
生物科学研究方法、生物科学研究技术分子生物学遗传学
Brors Benedikt,Cadenas Cristina,Ebert Peter,Barann Matthias,Zipprich Gideon,Eils J¨1rgen,Hamann Alf,Lengauer Thomas,Rosenstiel Philip,Felder B?rbel,Hengstler Jan G.,Gasparoni Nina,Schulz Marcel H.,Amirabad Azim Dehghani,Nordstr?m Karl,Walter J?rn,Gianmoena Kathrin,Polansky Julia K.,Xiong Jieyi,Schmidt Florian,Hutter Barbara,Sinha Anupam,Gasparoni Gilles,Chen Wei,Fr?hler Sebastian,Ardakani Fatemeh Behjati.Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction[EB/OL].(2025-03-28)[2025-05-10].https://www.biorxiv.org/content/10.1101/081935.点此复制
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