Applied bioinformatics for the identification of regulatory elements

A general method applicable to the search for similarities in the amino acid sequence of two proteins

Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288

Initial sequencing and comparative analysis of the mouse genome

Mouse Genome Sequencing Consortium (2002) · Nature · doi:10.1038/nature01262 · Cited by 7,187

A genomic regulatory network for development

Davidson, E. H., Rast, J. P., Oliveri, P., Ransick, A., Calestani, C., Yuh, C.-H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C., et al. (2002) · Science · doi:10.1126/science.1069883 · Cited by 1,567

LAGAN and Multi-LAGAN: Efficient tools for large-scale multiple alignment of genomic DNA

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The β-globin nuclear compartment in development and erythroid differentiation

Palstra, R.-J., Tolhuis, B., Splinter, E., Nijmeijer, R., Grosveld, F., & de Laat, W. (2003) · Nature Genetics · doi:10.1038/ng1244 · Cited by 559

Phylogenetic Shadowing of Primate Sequences to Find Functional Regions of the Human Genome

Boffelli, D., McAuliffe, J., Ovcharenko, D., Lewis, K. D., Ovcharenko, I., Pachter, L., & Rubin, E. M. (2003) · Science · doi:10.1126/science.1081331 · Cited by 516

Evolution of Transcription Factor Binding Sites in Mammalian Gene Regulatory Regions: Conservation and Turnover

Dermitzakis, E. T., & Clark, A. G. (2002) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a004169 · Cited by 463

The Human Transcription Factors

Lambert, S. A., Jolma, A., Campitelli, L. F., Das, P. K., Yin, Y., Albu, M., Chen, X., Taipale, J., Hughes, T. R., & Weirauch, M. T. (2018) · Cell · doi:10.1016/j.cell.2018.01.029 · Cited by 3,399

Transcriptional control of human p53-regulated genes

Riley, T., Sontag, E., Chen, P., & Levine, A. (2008) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2395 · Cited by 1,921

JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles

Castro-Mondragon, J. A., Riudavets-Puig, R., Rauluseviciute, I., Berhanu Lemma, R., Turchi, L., Blanc-Mathieu, R., Lucas, J., Boddie, P., Khan, A., Manosalva Pérez, N., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1113 · Cited by 1,935

JASPAR 2020: update of the open-access database of transcription factor binding profiles

Fornes, O., Castro-Mondragon, J. A., Khan, A., van der Lee, R., Zhang, X., Richmond, P. A., Modi, B. P., Correard, S., Gheorghe, M., Baranašić, D., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1001 · Cited by 1,763

iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections

Janky, R., Verfaillie, A., Imrichová, H., Van de Sande, B., Standaert, L., Christiaens, V., Hulselmans, G., Herten, K., Naval Sanchez, M., Potier, D., et al. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003731 · Cited by 900

Highly Conserved Non-Coding Sequences Are Associated with Vertebrate Development

Woolfe, A., Goodson, M., Goode, D. K., Snell, P., McEwen, G. K., Vavouri, T., Smith, S. F., North, P., Callaway, H., Kelly, K., et al. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0030007 · Cited by 970

Crosstalk between cancer cells and tumor associated macrophages is required for mesenchymal circulating tumor cell-mediated colorectal cancer metastasis

Wei, C., Yang, C., Wang, S., Shi, D., Zhang, C., Lin, X., Liu, Q., Dou, R., & Xiong, B. (2019) · Molecular Cancer · doi:10.1186/s12943-019-0976-4 · Cited by 833

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

Bravo González-Blas, C., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01938-4 · Cited by 778

Gene regulatory network inference: Data integration in dynamic models—A review

Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R. (2009) · Biosystems · doi:10.1016/j.biosystems.2008.12.004 · Cited by 755

Myogenic gene expression signature establishes that brown and white adipocytes originate from distinct cell lineages

Timmons, J. A., Wennmalm, K., Larsson, O., Walden, T. B., Lassmann, T., Petrovic, N., Hamilton, D. L., Gimeno, R. E., Wahlestedt, C., Baar, K., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610615104 · Cited by 732

Determinants of enhancer and promoter activities of regulatory elements

Andersson, R., & Sandelin, A. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0173-8 · Cited by 713

Machine learning for integrating data in biology and medicine: principles, practice, and opportunities

Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675

Regulation of gene expression via the core promoter and the basal transcriptional machinery

Juven-Gershon, T., & Kadonaga, J. T. (2010) · Developmental Biology · doi:10.1016/j.ydbio.2009.08.009 · Cited by 490

The Genetics of Transcription Factor DNA Binding Variation

Deplancke, B., Alpern, D., & Gardeux, V. (2016) · Cell · doi:10.1016/j.cell.2016.07.012 · Cited by 390

Genome-wide analyses reveal properties of redundant and specific promoter occupancy within the ETS gene family

Hollenhorst, P. C., Shah, A. A., Hopkins, C., & Graves, B. J. (2007) · Genes & Development · doi:10.1101/gad.1561707 · Cited by 290

The transcriptional landscape of mouse beta cells compared to human beta cells reveals notable species differences in long non-coding RNA and protein-coding gene expression

Benner, C., van der Meulen, T., Cacéres, E., Tigyi, K., Donaldson, C. J., & Huising, M. O. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-620 · Cited by 302

Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

Liu, L., Liu, C., Quintero, A., Wu, L., Yuan, Y., Wang, M., Cheng, M., Leng, L., Xu, L., Dong, G., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08205-7 · Cited by 243

Intersection of population variation and autoimmunity genetics in human T cell activation

Ye, C. J., Feng, T., Kwon, H.-K., Raj, T., Wilson, M. T., Asinovski, N., McCabe, C., Lee, M. H., Frohlich, I., Paik, H.-I., et al. (2014) · Science · doi:10.1126/science.1254665 · Cited by 236

Transcription Factor Cooperativity in Early Adipogenic Hotspots and Super-Enhancers

Siersbæk, R., Rabiee, A., Nielsen, R., Sidoli, S., Traynor, S., Loft, A., Poulsen, L. L. C., Rogowska-Wrzesinska, A., Jensen, O. N., & Mandrup, S. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.04.042 · Cited by 234

Genomic location analysis by ChIP‐Seq

Barski, A., & Zhao, K. (2009) · Journal of Cellular Biochemistry · doi:10.1002/jcb.22077 · Cited by 169

Characterization of tissue-specific differential DNA methylation suggests distinct modes of positive and negative gene expression regulation

Wan, J., Oliver, V. F., Wang, G., Zhu, H., Zack, D. J., Merbs, S. L., & Qian, J. (2015) · BMC Genomics · doi:10.1186/s12864-015-1271-4 · Cited by 165

Autophagosome–lysosome fusion triggers a lysosomal response mediated by TLR9 and controlled by OCRL

De Leo, M. G., Staiano, L., Vicinanza, M., Luciani, A., Carissimo, A., Mutarelli, M., Di Campli, A., Polishchuk, E., Di Tullio, G., Morra, V., et al. (2016) · Nature Cell Biology · doi:10.1038/ncb3386 · Cited by 165

Ligand-dependent dynamics of retinoic acid receptor binding during early neurogenesis

Mahony, S., Mazzoni, E. O., McCuine, S., Young, R. A., Wichterle, H., & Gifford, D. K. (2011) · Genome Biology · doi:10.1186/gb-2011-12-1-r2 · Cited by 142

Promoter or enhancer, what’s the difference? Deconstruction of established distinctions and presentation of a unifying model

Andersson, R. (2015) · BioEssays · doi:10.1002/bies.201400162 · Cited by 127

Robust shifts in S100a9 expression with aging: A novel mechanism for chronic inflammation

Swindell, W. R., Johnston, A., Xing, X., Little, A., Robichaud, P., Voorhees, J. J., Fisher, G., & Gudjonsson, J. E. (2013) · Scientific Reports · doi:10.1038/srep01215 · Cited by 124

A Polymorphic p53 Response Element in KIT Ligand Influences Cancer Risk and Has Undergone Natural Selection

Zeron-Medina, J., Wang, X., Repapi, E., Campbell, M. R., Su, D., Castro-Giner, F., Davies, B., Peterse, E. F. P., Sacilotto, N., Walker, G. J., et al. (2013) · Cell · doi:10.1016/j.cell.2013.09.017 · Cited by 118

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

González-Blas, C. B., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2022) · doi:10.1101/2022.08.19.504505 · Cited by 778

Polymorphisms affecting gene regulation and mRNA processing: Broad implications for pharmacogenetics

JOHNSON, A., WANG, D., & SADEE, W. (2005) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2004.11.001 · Cited by 97

A regulatory toolbox of MiniPromoters to drive selective expression in the brain

Portales-Casamar, E., Swanson, D. J., Liu, L., Leeuw, C. N. D., Banks, K. G., Ho Sui, S. J., Fulton, D. L., Ali, J., Amirabbasi, M., Arenillas, D. J., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1009158107 · Cited by 77

Positive selection of somatically mutated clones identifies adaptive pathways in metabolic liver disease

Wang, Z., Zhu, S., Jia, Y., Wang, Y., Kubota, N., Fujiwara, N., Gordillo, R., Lewis, C., Zhu, M., Sharma, T., et al. (2023) · Cell · doi:10.1016/j.cell.2023.03.014 · Cited by 72

Large-scale analysis of transcriptional cis-regulatory modules reveals both common features and distinct subclasses

Li, L., Zhu, Q., He, X., Sinha, S., & Halfon, M. S. (2007) · Genome Biology · doi:10.1186/gb-2007-8-6-r101 · Cited by 69

Microglia recapitulate a hematopoietic master regulator network in the aging human frontal cortex

Wehrspaun, C. C., Haerty, W., & Ponting, C. P. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.04.008 · Cited by 64

Computational methods for the detection of cis-regulatory modules

Van Loo, P., & Marynen, P. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp025 · Cited by 62

Design of synthetic promoters for cyanobacteria with generative deep-learning model

Seo, E., Choi, Y.-N., Shin, Y. R., Kim, D., & Lee, J. W. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad451 · Cited by 61

CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experiments

Kuttippurathu, L., Hsing, M., Liu, Y., Schmidt, B., Maskell, D. L., Lee, K., He, A., Pu, W. T., & Kong, S. W. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btq707 · Cited by 48

Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning

Atak, Z. K., Taskiran, I. I., Demeulemeester, J., Flerin, C., Mauduit, D., Minnoye, L., Hulselmans, G., Christiaens, V., Ghanem, G.-E., Wouters, J., et al. (2021) · Genome Research · doi:10.1101/gr.260851.120 · Cited by 48

Identification of Genomic Features in Environmentally Induced Epigenetic Transgenerational Inherited Sperm Epimutations

Guerrero-Bosagna, C., Weeks, S., & Skinner, M. K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100194 · Cited by 45

Transcription factor Acj6 controls dendrite targeting via a combinatorial cell-surface code

Xie, Q., Li, J., Li, H., Udeshi, N. D., Svinkina, T., Orlin, D., Kohani, S., Guajardo, R., Mani, D. R., Xu, C., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.04.026 · Cited by 42

Navigating the non-coding genome in heart development and Congenital Heart Disease

Chahal, G., Tyagi, S., & Ramialison, M. (2019) · Differentiation · doi:10.1016/j.diff.2019.05.001 · Cited by 31

SEPALLATA-driven MADS transcription factor tetramerization is required for inner whorl floral organ development

Hugouvieux, V., Blanc-Mathieu, R., Janeau, A., Paul, M., Lucas, J., Xu, X., Ye, H., Lai, X., Le Hir, S., Guillotin, A., et al. (2024) · The Plant Cell · doi:10.1093/plcell/koae151 · Cited by 26

Integrative single-nucleus multi-omics analysis prioritizes candidate cis and trans regulatory networks and their target genes in Alzheimer’s disease brains

Gamache, J., Gingerich, D., Shwab, E. K., Barrera, J., Garrett, M. E., Hume, C., Crawford, G. E., Ashley-Koch, A. E., & Chiba-Falek, O. (2023) · Cell & Bioscience · doi:10.1186/s13578-023-01120-5 · Cited by 20