The BLUEPRINT Data Analysis Portal
Open accessAbstract
The impact of large and complex epigenomic datasets on biological insights or clinical applications is limited by the lack of accessibility by easy, intuitive, and fast tools. Here, we describe an epigenomics comparative cyber-infrastructure (EPICO), an open-access reference set of libraries to develop comparative epigenomic data portals. Using EPICO, large epigenome projects can make available their rich datasets to the community without requiring specific technical skills. As a first instance of EPICO, we implemented the BLUEPRINT Data Analysis Portal (BDAP). BDAP provides a desktop for the comparative analysis of epigenomes of hematopoietic cell types based on results, such as the position of epigenetic features, from basic analysis pipelines. The BDAP interface facilitates interactive exploration of genomic regions, genes, and pathways in the context of differentiation of hematopoietic lineages. This work represents initial steps toward broadly accessible integrative analysis of epigenomic data across international consortia. EPICO can be accessed at https://github.com/inab, and BDAP can be accessed at http://blueprint-data.bsc.es.
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Large-scale annotation projects such as the Roadmap Epigenomics Project, the Blueprint Project, and PsychENCODE aim to generate, and make publicly available, high-quality reference annotation maps across a wide variety of human cell and tissue types.
“This search has been facilitated by large-scale annotation projects such as the Roadmap Epigenomics Project [180], the Blueprint Project [181], and PsychENCODE [182]. These projects aim to generate, and make publicly available, high-quality reference annotation maps across a wide variety of human cell and tissue types.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [181]
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The Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400 · see citing works
The Entity Relationship Model — Toward a Unified View of Data
Chen, P. P.-S. (2002) · Springer eBooks · doi:10.1007/978-3-642-59412-0_18 · Cited by 5,751 · see citing works
The entity-relationship model—toward a unified view of data
Chen, P. P.-S. (1976) · ACM Transactions on Database Systems · doi:10.1145/320434.320440 · Cited by 4,708 · see citing works
GENCODE: The reference human genome annotation for The ENCODE Project
Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B. L., Barrell, D., Zadissa, A., Searle, S., et al. (2012) · Genome Research · doi:10.1101/gr.135350.111 · Cited by 4,585 · see citing works
Self-citationThe OBO Foundry: coordinated evolution of ontologies to support biomedical data integration
Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W. J., Ceusters, W. M., Goldberg, L. J., Eilbeck, K. L., Ireland, A., Mungall, C., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1346 · Cited by 2,546 · see citing works
The NIH Roadmap Epigenomics Mapping Consortium
Bernstein, B. E., Stamatoyannopoulos, J. A., Costello, J. F., Ren, B., Milosavljevic, A., Meissner, A., Kellis, M., Marra, M. A., Beaudet, A. L., Ecker, J. R., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt1010-1045 · Cited by 1,892 · see citing works
The Reactome pathway knowledgebase
Croft, D., Mundo, A. F., Haw, R., Milacic, M., Weiser, J., Wu, G., Caudy, M., Garapati, P., Gillespie, M., Kamdar, M. R., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1102 · Cited by 1,889 · see citing works
Extending the database relational model to capture more meaning
Codd, E. F. (1979) · ACM Transactions on Database Systems · doi:10.1145/320107.320109 · Cited by 1,559 · see citing works
The Reactome pathway Knowledgebase
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 1,201 · see citing works
Flicek, P., Amode, M., Barrell, D., Beal, K., Billis, K., Brent, S., Carvalho-Silva, D., Clapham, P., Coates, G., Fitzgerald, S., et al. (2013) · Nucleic Acids Res. · doi:10.1093/nar/gkt1196 · Cited by 926 · see citing works
Self-citationYe, R. D., Boulay, F., Wang, J. M., Dahlgren, C., Gerard, C. J., Parmentier, M., Serhan, C. N., & Murphy, P. M. (2009) · Pharmacological Reviews · doi:10.1124/pr.109.001578 · Cited by 767 · see citing works
Modeling sample variables with an Experimental Factor Ontology
Malone, J., Holloway, E., Adamusiak, T., Kapushesky, M., Zheng, J., Kolesnikov, N. N., Zhukova, A., Brāzma, A., & Parkinson, H. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq099 · Cited by 571 · see citing works
Bard, J., Rhee, S. Y., & Ashburner, M. (2005) · Genome biology · doi:10.1186/gb-2005-6-2-r21 · Cited by 416 · see citing works
BLUEPRINT to decode the epigenetic signature written in blood
Adams, D., Altucci, L., Antonarakis, S., Ballesteros, J., Beck, S., Bird, A., Bock, C., Boehm, B., Campo, E., Caricasole, A., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2153 · Cited by 374 · see citing works
Self-citationAPPRIS: annotation of principal and alternative splice isoforms
Rodriguez, J. M., Maietta, P., Ezkurdia, I., Pietrelli, A., Wesselink, J.-J., Lopez, G., Valencia, A., & Tress, M. L. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks1058 · Cited by 232 · see citing works
Self-citationInternational Cancer Genome Consortium
Berger, D. (2013) · Im Focus Onkologie · doi:10.1007/s15015-013-0268-6 · Cited by 152 · see citing works
Kurotaki, D., Yamamoto, M., Nishiyama, A., Uno, K., Ban, T., Ichino, M., Sasaki, H., Matsunaga, S., Yoshinari, M., Ryo, A., et al. (2014) · Nature Communications · doi:10.1038/ncomms5978 · Cited by 149 · see citing works
Sarntivijai, S., Lin, Y., Xiang, Z., Meehan, T. F., Diehl, A. D., Vempati, U. D., Schürer, S. C., Pang, C., MALONE, J. M., Parkinson, H., et al. (2014) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-5-37 · Cited by 127 · see citing works
DeepBlue epigenomic data server: programmatic data retrieval and analysis of epigenome region sets
Albrecht, F., List, M., Bock, C., & Lengauer, T. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw211 · Cited by 71 · see citing works
Using the Wash U Epigenome Browser to Examine Genome‐Wide Sequencing Data
Zhou, X., & Wang, T. (2012) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1010s40 · Cited by 68 · see citing works
Ontology based molecular signatures for immune cell types via gene expression analysis
Meehan, T. F., Vasilevsky, N., Mungall, C., Dougall, D. S., Haendel, M. A., Blake, J. A., & Diehl, A. D. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-263 · Cited by 15 · see citing works
Arai, E., Miura, F., Totoki, Y., Yamashita, S., Tian, Y., Gotoh, M., Ojima, H., Nakagawa, H., Takahashi, Y., Nakamura, H. K., et al. (2016) · Cancer Research · doi:10.1158/1538-7445.am2016-4517 · Cited by 2 · see citing works
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A DNA methylation atlas of normal human cell types
Loyfer, N., Magenheim, J., Peretz, A., Cann, G., Bredno, J., Klochendler, A., Fox-Fisher, I., Shabi-Porat, S., Hecht, M., Pelet, T., et al. (2023) · Nature · doi:10.1038/s41586-022-05580-6 · Cited by 656 · see citing works
The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery
Stunnenberg, H. G., Abrignani, S., Adams, D. J., de Almeida, M., Altucci, L., Amin, V. R., Amit, I., Antonarakis, S. E., Aparicio, S. A. J. R., Arima, T., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.007 · Cited by 485 · see citing works
Self-citationNon-coding RNAs in cardiovascular diseases: diagnostic and therapeutic perspectives
Poller, W. C., Dimmeler, S., Heymans, S. R. B., Zeller, T., Haas, J., Karakas, M., Leistner, D. M., Jakob, P., Nakagawa, S., Blankenberg, S., et al. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx165 · Cited by 412 · see citing works
Quantifying tumor-infiltrating immune cells from transcriptomics data
Finotello, F., & Trajanoski, Z. (2018) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-018-2150-z · Cited by 402 · see citing works
Big data analytics for personalized medicine
Cirillo, D., & Valencia, A. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.03.004 · Cited by 275 · see citing works
Self-citationsnakePipes: facilitating flexible, scalable and integrative epigenomic analysis
Bhardwaj, V., Heyne, S., Sikora, K., Rabbani, L., Rauer, M., Kilpert, F., Richter, A. S., Ryan, D. P., & Manke, T. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz436 · Cited by 191 · see citing works
A pathogenic and clonally expanded B cell transcriptome in active multiple sclerosis
Ramesh, A., Schubert, R. D., Greenfield, A. L., Dandekar, R., Loudermilk, R. P., Sabatino, J. J., Koelzer, M. T., Tran, E. B., Koshal, K., Kim, K., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2008523117 · Cited by 189 · see citing works
Haemopedia RNA-seq: a database of gene expression during haematopoiesis in mice and humans
Choi, J., Baldwin, T., Wong, M., Bolden, J. E., Fairfax, K., Lucas, E. C., Cole, R., Biben, C., Morgan, C., Ramsay, K., et al. (2018) · Nucleic Acids Res. · doi:10.1093/nar/gky1020 · Cited by 151 · see citing works
scMatch: a single-cell gene expression profile annotation tool using reference datasets
Hou, R., Denisenko, E., & Forrest, A. R. R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz292 · Cited by 130 · see citing works
Genetic architecture of Alzheimer's disease
Neuner, S. M., TCW, J., & Goate, A. M. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104976 · Cited by 126 · see citing works
Translational Perspective on Epigenetics in Cardiovascular Disease
van der Harst, P., Windt, L. J. D., & Chambers, J. C. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.05.067 · Cited by 117 · see citing works
Went, M., Sud, A., Försti, A., Halvarsson, B.-M., Weinhold, N., Kimber, S. T., van Duin, M., Þorleifsson, G., Holroyd, A., Johnson, D. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04989-w · Cited by 113 · see citing works
Anand, P., Guillaumet-Adkins, A., Dimitrova, V., Yun, H., Drier, Y., Sotudeh, N., Rogers, A. J., Ouseph, M. M., Nair, M. S., Potdar, S. V., et al. (2020) · Blood · doi:10.1182/blood.2019004547 · Cited by 94 · see citing works
DiseaseEnhancer: a resource of human disease-associated enhancer catalog
Zhang, G., Shi, J., Zhu, S., Lan, Y., Xu, L., Yuan, H., Liao, G., Liu, X., Zhang, Y., Xiao, Y., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx920 · Cited by 88 · see citing works
Chung, F. F., & Herceg, Z. (2020) · Environmental Health Perspectives · doi:10.1289/ehp6104 · Cited by 86 · see citing works
Comprehensive cell type decomposition of circulating cell-free DNA with CelFiE
Caggiano, C., Celona, B., Garton, F. C., Mefford, J., Black, B. L., Henderson, R. D., Lomen‐Hoerth, C., Dahl, A., & Zaitlen, N. (2021) · Nature Communications · doi:10.1038/s41467-021-22901-x · Cited by 86 · see citing works
A cell-free DNA metagenomic sequencing assay that integrates the host injury response to infection
Cheng, A. P., Burnham, P., Lee, J. R., Cheng, M. P., Suthanthiran, M., Dadhania, D. M., & Vlaminck, I. D. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906320116 · Cited by 82 · see citing works
Enhancer and super‐enhancer: Positive regulators in gene transcription
Peng, Y., & Zhang, Y. (2018) · Animal Models and Experimental Medicine · doi:10.1002/ame2.12032 · Cited by 74 · see citing works
The leukemic oncogene EVI1 hijacks a MYC super-enhancer by CTCF-facilitated loops
Ottema, S., Mulet‐Lazaro, R., Erpelinck-Verschueren, C. A. J., van Herk, S., Havermans, M., Varea, A. A., Vermeulen, M., Beverloo, H. B., Gröschel, S., Haferlach, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25862-3 · Cited by 69 · see citing works
ENdb: a manually curated database of experimentally supported enhancers for human and mouse
Bai, X., Shi, S., Ai, B., Jiang, Y., Liu, Y., Han, X., Xu, M., Pan, Q., Wang, F., Wang, Q., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz973 · Cited by 62 · see citing works
Cortés-López, M., Schulz, L., Enculescu, M., Paret, C., Spiekermann, B., Quesnel-Vallières, M., Torres-Diz, M., Unic, S., Busch, A., Orekhova, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31818-y · Cited by 63 · see citing works
Fridlich, O., Peretz, A., Fox-Fisher, I., Piyanzin, S., Dadon, Z., Shcolnik, E., Sadeh, R., Fialkoff, G., Sharkia, I., Moss, J., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101074 · Cited by 60 · see citing works
Zheng, S., Gillespie, E., Naqvi, A. S., Hayer, K. E., Ang, Z., Torres-Diz, M., Quesnel-Vallières, M., Hottman, D. A., Bagashev, A., Chukinas, J., et al. (2021) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-21-0087 · Cited by 59 · see citing works
Morante-Palacios, O., Godoy‐Tena, G., Calafell-Segura, J., Ciudad, L., Martínez-Cáceres, E. M., Sardina, J. L., & Ballestar, E. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac941 · Cited by 57 · see citing works
Dynamic transcriptional reprogramming leads to immunotherapeutic vulnerabilities in myeloma
Frede, J., Anand, P., Sotudeh, N., Pinto, R. A., Nair, M. S., Stuart, H., Yee, A. J., Vijaykumar, T., Waldschmidt, J. M., Potdar, S. V., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-021-00766-y · Cited by 54 · see citing works
Sun, Y., Sedgwick, A. J., Palarasah, Y., Mangiola, S., & Barrow, A. D. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.668391 · Cited by 51 · see citing works
Li, T., Ortiz‐Fernández, L., Andrés‐León, E., Ciudad, L., Javierre, B. M., López‐Isac, E., del Castillo, A. G., Simeón‐Aznar, C. P., Ballestar, E., & Martin, J. J. (2020) · Genome Medicine · doi:10.1186/s13073-020-00779-6 · Cited by 51 · see citing works
Barry, A., McNulty, M. T., Jia, X., Gupta, Y., Dêbiec, H., Luo, Y., Nagano, C., Horinouchi, T., Jung, S., Colucci, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37985-w · Cited by 50 · see citing works
de la Calle‐Fabregat, C., Calafell-Segura, J., Gardet, M., Dunsmore, G., Mulder, K., Ciudad, L., Silvin, A., Moreno‐Càceres, J., Corbí, A. L., Muñoz‐Pinedo, C., et al. (2024) · Science Advances · doi:10.1126/sciadv.adq5226 · Cited by 47 · see citing works
Cell-free DNA profiling informs all major complications of hematopoietic cell transplantation
Cheng, A. P., Cheng, M. P., Loy, C., Lenz, J. S., Chen, K., Smalling, S., Burnham, P., Timblin, K. M., Orejas, J. L., Silverman, E. A., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113476118 · Cited by 47 · see citing works
Resolving mechanisms of immune‐mediated disease in primary CD4 T cells
Bourges, C., Groff, A. F., Burren, O. S., Gerhardinger, C., Mattioli, K., Hutchinson, A., Hu, T. X., Anand, T., Epping, M. W., Wallace, C., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202012112 · Cited by 45 · see citing works
CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology
Bernstein, M. N., Ma, Z., Gleicher, M., & Dewey, C. N. (2021) · iScience · doi:10.1016/j.isci.2020.101913 · Cited by 45 · see citing works
Went, M., Sud, A., Speedy, H. E., Sunter, N. J., Försti, A., Law, P., Johnson, D. C., Mirabella, F., Holroyd, A., Li, N., et al. (2018) · Blood Cancer Journal · doi:10.1038/s41408-018-0162-8 · Cited by 45 · see citing works
Deconstructing and targeting the genomic architecture of human neurodegeneration
Jager, P. L. D., Yang, H., & Bennett, D. A. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0240-z · Cited by 46 · see citing works
Aracena, K. A., Lin, Y., Luo, K., Pacis, A., Gona, S., Mu, Z., Yotova, V., Sindeaux, R., Pramatarova, A., Simon, M.-M., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01668-z · Cited by 42 · see citing works
McErlean, P. K., Bell, C. G., Hewitt, R. J., Busharat, Z., Ogger, P. P., Ghai, P., Albers, G. J., Calamita, E., Kingston, S., Molyneaux, P. L., et al. (2021) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202101-0004oc · Cited by 42 · see citing works
Clinical implications of genome-wide DNA methylation studies in acute myeloid leukemia
Li, Y., Xu, Q., Lv, N., Wang, L., Zhao, H., Wang, X., Guo, J., Chen, C., Li, Y., & Yu, L. (2017) · Journal of Hematology & Oncology · doi:10.1186/s13045-017-0409-z · Cited by 41 · see citing works
The road towards data integration in human genomics: players, steps and interactions
Bernasconi, A., Canakoglu, A., Masseroli, M., & Ceri, S. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa080 · Cited by 39 · see citing works
Epigenetic Regulation of F2RL3 Associates With Myocardial Infarction and Platelet Function
Corbin, L. J., White, S. J., Taylor, A. E., Williams, C. M., Taylor, K., van den Bosch, M. T. J., Teasdale, J. E., Jones, M., Bond, M., Harper, M. T., et al. (2022) · Circulation Research · doi:10.1161/circresaha.121.318836 · Cited by 39 · see citing works
Identifying common transcriptome signatures of cancer by interpreting deep learning models
Jha, A., Quesnel-Vallières, M., Wang, D., Thomas-Tikhonenko, A., Lynch, K. W., & Barash, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02681-3 · Cited by 39 · see citing works
Morante-Palacios, O., Ciudad, L., Micheroli, R., de la Calle‐Fabregat, C., Li, T., Barbisan, G., Houtman, M., Edalat, S. G., Frank‐Bertoncelj, M., Ospelt, C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1182 · Cited by 37 · see citing works
Wegner, M., Diehl, V., Bittl, V., de Bruyn, R., Wiechmann, S., Matthess, Y., Hebel, M., Hayes, M. G. B., Schaubeck, S., Benner, C. W., et al. (2019) · eLife · doi:10.7554/elife.42549 · Cited by 38 · see citing works
Stikker, B., Stik, G., van Ouwerkerk, A. F., Trap, L., Spicuglia, S., Hendriks, R. W., & Stadhouders, R. (2022) · Genome biology · doi:10.1186/s13059-022-02669-z · Cited by 36 · see citing works
Harrison, P. W., Fan, J., Richardson, D., Clarke, L., Zerbino, D. R., Cochrane, G., Archibald, A. L., Schmidt, C. J., & Flicek, P. (2018) · Animal Genetics · doi:10.1111/age.12736 · Cited by 32 · see citing works
Self-citationSun, Y., Sedgwick, A. J., Khan, M. A., Palarasah, Y., Mangiola, S., & Barrow, A. D. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.724107 · Cited by 32 · see citing works
The tidyomics ecosystem: enhancing omic data analyses
Hutchison, W. J., Keyes, T., Crowell, H. L., Serizay, J., Soneson, C., Davis, E. S., Sato, N., Moses, L., Tarlinton, B., Nahid, A. A., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02299-2 · Cited by 29 · see citing works
Epigenomics-Guided Drug Development: Recent Advances in Solving the Cancer Treatment “jigsaw puzzle”
Dzobo, K. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2018.0206 · Cited by 29 · see citing works
Wang, R., Peng, L., Xiao, Y., Zhou, Q., Wang, Z., Tang, L., Xiao, H., Yang, K., Liu, H., & Li, L. (2023) · Cancer Gene Therapy · doi:10.1038/s41417-022-00582-y · Cited by 29 · see citing works
An intronic LINE-1 regulates IFNAR1 expression in human immune cells
Buttler, C. A., Ramirez, D., Dowell, R. D., & Chuong, E. B. (2023) · Mobile DNA · doi:10.1186/s13100-023-00308-3 · Cited by 28 · see citing works
Janke, F., Angeles, A. K., Riediger, A. L., Bauer, S., Reck, M., Stenzinger, A., Schneider, M. A., Muley, T., Thomas, M., Christopoulos, P., et al. (2022) · Clinical Epigenetics · doi:10.1186/s13148-022-01387-4 · Cited by 26 · see citing works
Hecker, M., Boxberger, N., Illner, N., Fitzner, B., Schröder, I., Winkelmann, A., Dudešek, A., Meister, S., Koczan, D., Lorenz, P., et al. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1007961 · Cited by 25 · see citing works
A practical guide for DNase-seq data analysis: from data management to common applications
Liu, Y., Fu, L.-Y., Kaufmann, K., Chen, D., & Chen, M. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby057 · Cited by 24 · see citing works
Mundy-Bosse, B. L., Weigel, C., Wu, Y.-Z., Abdelbaky, S., Youssef, Y., Casas, S. B., Polley, N., Ernst, G., Young, K. A., McConnell, K. K., et al. (2022) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-21-0098 · Cited by 24 · see citing works
Orliaguet, L., Ejlalmanesh, T., Humbert, A., Ballaire, R., Diedisheim, M., Julla, J., Chokr, D., Cuenco, J., Michieletto, J., Charbit, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32813-z · Cited by 24 · see citing works
Mikulášová, A., Kent, D., Trevisán-Herraz, M., Karataraki, N., Fung, K. T. M., Ashby, C., Cieślak, A., Yaccoby, S., van Rhee, F., Zangari, M., et al. (2021) · Genome Research · doi:10.1101/gr.276042.121 · Cited by 22 · see citing works
Self-citationAutomatic identification of informative regions with epigenomic changes associated to hematopoiesis
Pau, E. C. D. S., Juan, D., Pancaldi, V., Were, F., Martín-Subero, J. I., Rico, D., & Valencia, A. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx618 · Cited by 23 · see citing works
Self-citationDerrien, J., Guérin-Charbonnel, C., Gaborit, V., Campion, L., Devic, M., Douillard, E., Roi, N., Avet-Loiseau, H., Decaux, O., Facon, T., et al. (2020) · Genome Medicine · doi:10.1186/s13073-021-00938-3 · Cited by 23 · see citing works
Lee, T., Sung, M. K., Lee, S., Yang, W., Oh, J., Kim, J. Y., Hwang, S., Ban, H., & Choi, J. K. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz868 · Cited by 21 · see citing works
McNamara, M. E., Loyfer, N., Kiliti, A. J., Schmidt, M. O., Shabi-Porat, S., Jain, S. S., Martinez, S. A., McDeed, A. P., Shahrour, N., Ballew, E., et al. (2023) · JCI Insight · doi:10.1172/jci.insight.156529 · Cited by 20 · see citing works
The Ensembl Genome Browser: Strategies for Accessing Eukaryotic Genome Data.
Newman, V., Moore, B., Sparrow, H., & Perry, E. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7737-6_6 · Cited by 17 · see citing works
Cell type deconvolution of methylated cell-free DNA at the resolution of individual reads
Keukeleire, P., Makrodimitris, S., & Reinders, M. J. T. (2022) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad048 · Cited by 18 · see citing works
3D chromatin hubs as regulatory units of identity and survival in human acute leukemia
Gambi, G., Boccalatte, F. E., Rodriguez-Hernaez, J., Lin, Z., Nadorp, B., Polyzos, A., Tan, J., Avrampou, K., Inghirami, G., Kentsis, A., et al. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.11.040 · Cited by 18 · see citing works
Su, C., Pahl, M. C., Grant, S. F. A., & Wells, A. D. (2021) · Human Genetics · doi:10.1007/s00439-021-02326-8 · Cited by 24 · see citing works
Im, N. G., Guillaumet-Adkins, A., Wal, M., Rogers, A. J., Frede, J., Havig, C. C., Yang, J., Anand, P., Stegmann, S. K., Waldschmidt, J. M., et al. (2022) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-21-0626 · Cited by 17 · see citing works
Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits
Chun, S., Akle, S., Teodosiadis, A., Cade, B. E., Wang, H., Sofer, T., Evans, D. S., Stone, K. L., Gharib, S. A., Mukherjee, S., et al. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010557 · Cited by 16 · see citing works
Madan, N., Ghazi, A. R., Kong, X., Chen, E. S., Shaw, C. A., & Edelstein, L. C. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008287 · Cited by 16 · see citing works
Xiong, S., Zhou, J., Tan, T. K., Chung, T., Tan, T. Z., Toh, S. H. M., Tang, N. X. N., Jia, Y., See, Y. X., Fullwood, M. J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50910-z · Cited by 16 · see citing works
LIPA, a risk locus for coronary artery disease: decoding the variant-to-function relationship
Li, F., Flynn, E. D., Ha, P., Zebak, M., Cheng, H.-X., Xue, C.-Y., Shi, J., Wu, X., Wang, Z., Meng, Y., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf581 · Cited by 9 · see citing works
MetDecode: methylation-based deconvolution of cell-free DNA for noninvasive multi-cancer typing
Passemiers, A., Tuveri, S., Sudhakaran, D., Jatsenko, T., Laga, T., Punie, K., Hatse, S., Tejpar, S., Coosemans, A., Nieuwenhuysen, E. V., et al. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae522 · Cited by 15 · see citing works
Maughan, R. T., Macdonald-Dunlop, E., Rashid, L. H., Sørensen, L., Chaddock, N., Masters, S., Porter, A., Peverelli, M., Pericleous, C., Hutchings, A., et al. (2025) · Arthritis & Rheumatology · doi:10.1002/art.43110 · Cited by 14 · see citing works
Mills, L. J., Scott, M. C., Shah, P., Cunanan, A. R., Deshpande, A., Auch, B., Curtin, B., Beckman, K. B., Spector, L. G., Sarver, A. L., et al. (2020) · Bone · doi:10.1016/j.bone.2020.115716 · Cited by 14 · see citing works
MiR-146b-5p regulates IL-23 receptor complex expression in chronic lymphocytic leukemia cells
Matis, S., Recchia, A. G., Colombo, M., Cardillo, M., Fabbi, M., Todoerti, K., Bossio, S., Fabris, S., Cancila, V., Massara, R., et al. (2022) · Blood Advances · doi:10.1182/bloodadvances.2021005726 · Cited by 14 · see citing works
sciMET-cap: high-throughput single-cell methylation analysis with a reduced sequencing burden
Acharya, S. N., Nichols, R. V., Rylaarsdam, L. E., O’Connell, B. L., Braun, T. P., & Adey, A. (2024) · Genome biology · doi:10.1186/s13059-024-03306-7 · Cited by 13 · see citing works
A personalized network framework reveals predictive axis of anti-TNF response across diseases
Gerassy-Vainberg, S., Starosvetsky, E., Gaujoux, R., Blatt, A., Maimon, N., Gorelik, Y., Pressman, S., Alpert, A., Bar‐Yoseph, H., Dubovik, T., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101300 · Cited by 12 · see citing works
Kong, X., Ma, L., Chen, E., Shaw, C. A., & Edelstein, L. C. (2019) · Thrombosis and Haemostasis · doi:10.1055/s-0039-1678694 · Cited by 12 · see citing works
Rare t(X;14)(q28;q32) translocation reveals link between MTCP1 and chronic lymphocytic leukemia
Walker, J. S., Hing, Z. A., Sher, S., Cronin, J., Williams, K., Harrington, B. K., Skinner, J. N., Cempre, C. B., Gregory, C. T., Pan, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26400-x · Cited by 11 · see citing works
Recommendations for the FAIRification of genomic track metadata
Gundersen, S., Boddu, S., Capella-Gutiérrez, S., Drabløs, F., Fernández, J. M., Kompova, R., Taylor, K., Titov, D., Zerbino, D. R., & Hovig, E. J. (2021) · F1000Research · doi:10.12688/f1000research.28449.1 · Cited by 11 · see citing works
Self-citationApproaching Shared Pathophysiology in Immune-Mediated Diseases through Functional Genomics
González‐Serna, D., Villanueva-Martín, G., Acosta‐Herrera, M., Márquez, A., & Martin, J. J. (2020) · Genes · doi:10.3390/genes11121482 · Cited by 11 · see citing works
Copy number-aware deconvolution of tumor-normal DNA methylation profilesPreprint
Cadieux, E. L., Mensah, N., Castignani, C., Tanić, M., Wilson, G. A., Dietzen, M., Dhami, P., Vaikkinen, H., Verfaillie, A., Cotobal, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.03.366252 · Cited by 9 · see citing works
Mullan, K. A., Anderson, A., Shi, Y., Ding, J., Ng, C. C., Chen, Z. Z., Baum, L., Cherny, S. S., Petrovski, S., Sham, P. C., et al. (2022) · Epilepsia · doi:10.1111/epi.17182 · Cited by 8 · see citing works
Cheng, J. C., Swarup, N., Morselli, M., Huang, W.-L., Aziz, M. A., Caggiano, C., Kordi, M., Patel, A. A., Chia, D., Kim, Y., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae276 · Cited by 8 · see citing works
In Silico Cell-Type Deconvolution Methods in Cancer Immunotherapy
Sturm, G., Finotello, F., & List, M. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0327-7_15 · Cited by 8 · see citing works
Dirks, R. A. M., Thomas, P. C., Wu, H., Jones, R. C., Stunnenberg, H. G., & Marks, H. (2021) · Genome Research · doi:10.1101/gr.260745.120 · Cited by 8 · see citing works
DeepBlueR: large-scale epigenomic analysis in R
Albrecht, F., List, M., Bock, C., & Lengauer, T. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx099 · Cited by 7 · see citing works
Estimating the rate of cell type degeneration from epigenetic sequencing of cell-free DNAPreprint
Caggiano, C., Celona, B., Garton, F. C., Mefford, J., Black, B. L., Lomen‐Hoerth, C., Dahl, A., & Zaitlen, N. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.15.907022 · Cited by 7 · see citing works
Tissue informative cell-free DNA methylation sites in amyotrophic lateral sclerosisPreprint
Caggiano, C., Morselli, M., Qian, X., Celona, B., Thompson, M. J., Wani, S., Tosevska, A., Taraszka, K. S., Heuer, G., Ngo, S. T., et al. (2024) · medRxiv · doi:10.1101/2024.04.08.24305503 · Cited by 7 · see citing works
Párraga, A. A., Nylund, P., Diamanti, K., Garrido-Zabala, B., Tziola, S. I., Vasquez, L. J., Pyl, P. T., Raykova, D., Skaftason, A., Ma, A., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-17093-z · Cited by 7 · see citing works
Cardinale, C. J., Chang, X., Wei, Z., Qu, H., Bradfield, J. P., Polychronakos, C., & Hakonarson, H. H. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1101488 · Cited by 6 · see citing works
Carter, M. J., Bogdanov, Y. D., Smith, R. C. G., Cox, K. L., Frampton, S., Ferson, L., Foxall, R. B., Hussain, K., Strefford, J. C., Beers, S. A., et al. (2025) · The Journal of Immunology · doi:10.1093/jimmun/vkaf109 · Cited by 6 · see citing works
Xie, T., Gorenjak, V., Stathopoulou, M. G., Dadé, S., Marouli, E., Masson, C., Murray, H. A., Lamont, J. V., FitzGerald, P. S., Deloukas, P., et al. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-01077-9 · Cited by 6 · see citing works
Exploring the Role of Enhancer-Mediated Transcriptional Regulation in Precision Biology
Wang, X., Liu, D., Luo, J., Kong, D., & Zhang, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241310843 · Cited by 6 · see citing works
Aracena, K. A., Lin, Y., Luo, K., Pacis, A., Gona, S., Mu, Z., Yotova, V., Sindeaux, R., Pramatarova, A., Simon, M.-M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.10.491413 · Cited by 6 · see citing works
EPIraction - an atlas of candidate enhancer-gene interactions in human tissues and cell linesPreprint
Nurtdinov, R., & Guigó, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.18.638885 · Cited by 6 · see citing works
Exosomal non-coding RNAs: key regulators of inflammation-related cardiovascular disorders
Saadh, M. J., Muhammad, F. A., Albadr, R. J., Sanghvi, G. V., Ballal, S., Pathak, P. K., Bareja, L., Aminov, Z., Taher, W. M., Alwan, M., et al. (2025) · European journal of medical research · doi:10.1186/s40001-025-02649-6 · Cited by 5 · see citing works
Sisu, C. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1503-4_5 · Cited by 5 · see citing works
Trametinib Sensitivity is Defined by a Myeloid Differentiation Profile in Acute Myeloid Leukemia
Quesnel-Vallières, M., Schultz, D., Orlenko, A., Lo, Y., Moore, J. H., Ritchie, M. D., Roth, D. B., Carroll, M. P., Barash, Y., Lynch, K. W., et al. (2024) · Drugs in R&D · doi:10.1007/s40268-024-00491-5 · Cited by 5 · see citing works
Differentially expressed genes prediction by multiple self-attention on epigenetics data
Huang, Z., Wang, J., Yan, Z., & Guo, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac117 · Cited by 5 · see citing works
Computational methods to explore chromatin state dynamics
Orouji, E., & Raman, A. T. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac439 · Cited by 5 · see citing works
Cardinale, C. J., Liu, Y., Kevadia, A., Strong, A., Watts, V. J., & Hakonarson, H. H. (2025) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjaf030 · Cited by 4 · see citing works
- Authors
- Fernández, J. M., de la Torre, V., Richardson, D., Royo, R., Puiggròs, M., Moncunill, V., Fragkogianni, S., Clarke, L., Flicek, P., Rico, D., Torrents, D., Carrillo de Santa Pau, E., & Valencia, A.
- Affiliations
- Instituto de Salud Carlos III (ES)
- European Bioinformatics Institute (GB)
- Barcelona Supercomputing Center (ES)
- Spanish National Cancer Research Centre (ES)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Corresponding author
- Valencia, Alfonso
- ORCID
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 3
- Issue
- 5
- Pages
- 491-495.e5
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC5919098
- OpenAlex
- W2549343567
- Cited by
- 151
- References
- 22
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 16 · 2023: 21 · 2022: 23 · 2021: 20 · 2020: 19 · 2019: 14 · 2018: 14 · 2017: 7 · 2016: 1
- FWCI
- 6.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- ISCIII (PT13/0001)
- FEDER
- Spanish Ministry of Health, ISCIII (PRB2: PT13/0001/0028)
- European Union’s Seventh Framework Programme (FP7/2007-2013)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- epigenomics, hematopoietic differentiation, epigenetic features
- MeSH terms
- Cell Differentiation, Software, User-Computer Interface, Genome, Internet, Epigenesis, Genetic, Epigenomics
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