GRaNIE and GRaNPA: Inference and evaluation of enhancer-mediated gene regulatory networks applied to study macrophages
Open accessAbstract
Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R packages: GRaNIE (Gene Regulatory Network Inference including Enhancers) for building enhancer-based gene regulatory networks (eGRNs) and GRaNPA (Gene Regulatory Network Performance Analysis) for evaluating GRNs. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection and cancer, their involvement in common genetic diseases including autoimmune diseases, and identify the TF PURA as putative regulator of pro-inflammatory macrophage polarisation. Availability: - GRaNIE: https://bioconductor.org/packages/release/bioc/html/GRaNIE.html - GRaNPA: https://git.embl.de/grp-zaugg/GRaNPA Graphical abstract:
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Methods for inferring enhancer-driven GRNs that integrate enhancer sequence, chromatin accessibility and gene expression found that accuracy to predict active enhancers increases by approximately 10% compared to using accessibility alone.
“methods for inferring enhancer-driven GRNs (eGRNs) that integrate enhancer sequence, chromatin accessibility and gene expression22–24 found that the accuracy to predict active enhancers increases by ~10% compared to using accessibility alone25.”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [22]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012776 · Cited by 2,131 · see citing works
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works
ChEA3: transcription factor enrichment analysis by orthogonal omics integration
Keenan, A. B., Torre, D., Lachmann, A., Leong, A. K., Wojciechowicz, M. L., Utti, V., Jagodnik, K. M., Kropiwnicki, E., Wang, Z., & Ma’ayan, A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz446 · Cited by 1,136 · see citing works
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020) · Nature Methods · doi:10.1038/s41592-019-0690-6 · Cited by 792 · see citing works
GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks
Moerman, T., Aibar Santos, S., Bravo González-Blas, C., Simm, J., Moreau, Y., Aerts, J., & Aerts, S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty916 · Cited by 621 · see citing works
Competition between DNA methylation and transcription factors determines binding of NRF1
Domcke, S., Bardet, A. F., Adrian Ginno, P., Hartl, D., Burger, L., & Schübeler, D. (2015) · Nature · doi:10.1038/nature16462 · Cited by 508 · see citing works
Macrophages: An Inflammatory Link Between Angiogenesis and Lymphangiogenesis
Corliss, B. A., Azimi, M. S., Munson, J. M., Peirce, S. M., & Murfee, W. L. (2016) · Microcirculation · doi:10.1111/micc.12259 · Cited by 356 · see citing works
HIPSCI Consortium, Alasoo, K., Rodrigues, J., Mukhopadhyay, S., Knights, A. J., Mann, A. L., Kundu, K., Hale, C., Dougan, G., & Gaffney, D. J. (2018) · Nature Genetics · doi:10.1038/s41588-018-0046-7 · Cited by 338 · see citing works
Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases
Marbach, D., Lamparter, D., Quon, G., Kellis, M., Kutalik, Z., & Bergmann, S. (2016) · Nature Methods · doi:10.1038/nmeth.3799 · Cited by 326 · see citing works
Holland, C. H., Tanevski, J., Perales-Patón, J., Gleixner, J., Kumar, M. P., Mereu, E., Joughin, B. A., Stegle, O., Lauffenburger, D. A., Heyn, H., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1949-z · Cited by 323 · see citing works
Chistiakov, D. A., Myasoedova, V. A., Revin, V. V., Orekhov, A. N., & Bobryshev, Y. V. (2018) · Immunobiology · doi:10.1016/j.imbio.2017.10.005 · Cited by 311 · cited 1× · see citing works
“IRF8 is a known pro-inflammatory interferon response factor, associated with the polarization of macrophages into the M1, pro-inflammatory type [43], which we confirmed in our data using gene-set enrichment analysis of the IRF8 regulon (Fig.”
ResultChen, S., & Mar, J. C. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2217-z · Cited by 235 · see citing works
MBD2 serves as a viable target against pulmonary fibrosis by inhibiting macrophage M2 program
Wang, Y., Zhang, L., Wu, G.-R., Zhou, Q., Yue, H., Rao, L.-Z., Yuan, T., Mo, B., Wang, F.-X., Chen, L.-M., et al. (2021) · Science Advances · doi:10.1126/sciadv.abb6075 · Cited by 227 · see citing works
Population Variation and Genetic Control of Modular Chromatin Architecture in Humans
Waszak, S. M., Delaneau, O., Gschwind, A. R., Kilpinen, H., Raghav, S. K., Witwicki, R. M., Orioli, A., Wiederkehr, M., Panousis, N. I., Yurovsky, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.08.001 · Cited by 214 · cited 1× · see citing works
“In contrast to other approaches for interpreting trait-associated variants that are solely based on epigenetics, such as the activity by contact model [61], or purely based on genetics, such as eQTLs [4] or variable chromatin domains [62], eGRNs by GRaNIE capture TF-enhancer-gene links based on variation due to geneti…”
Novikova, G., Kapoor, M., TCW, J., Abud, E. M., Efthymiou, A. G., Chen, S. X., Cheng, H., Fullard, J. F., Bendl, J., Liu, Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21823-y · Cited by 214 · see citing works
Decoding gene regulation in the fly brain
Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Spanier, K. I., Bravo González-Blas, C., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., et al. (2021) · doi:10.1101/2021.08.11.454937 · Cited by 170 · see citing works
Decoding gene regulation in the fly brain
Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Gomez, A. E., Aughey, G., Spanier, K. I., De Rop, F. V., González-Blas, C. B., Dionne, M., et al. (2022) · Nature · doi:10.1038/s41586-021-04262-z · Cited by 170 · see citing works
Berest, I., Arnold, C., Reyes-Palomares, A., Palla, G., Rasmussen, K. D., Giles, H., Bruch, P.-M., Huber, W., Dietrich, S., Helin, K., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.106 · Cited by 119 · see citing works
Self-citationCellOracle: Dissecting cell identity via network inference and in silico gene perturbation
Kamimoto, K., Hoffmann, C. M., & Morris, S. A. (2020) · doi:10.1101/2020.02.17.947416 · Cited by 81 · see citing works
Flagged first, then by citations
Research works printed but not linked: 61
- Claringbould A, Zaugg JB. Enhancers in disease: molecular basis and emerging treatment strategies. Trends Mol Med. 2021;27:1060-73.
- Karnuta JM, Scacheri PC. Enhancers: bridging the gap between gene control and human disease. Hum Mol Genet. 2018;27:R219-27.
- Freimer JW, Shaked O, Naqvi S, Sinnott-Armstrong N, Kathiria A, Chen AF, et al. Systematic discovery and perturbation of regulatory genes in human T cells reveals the architecture of immune networks. BioRxiv. 2021.
- Võsa U, Claringbould A, Westra H-J, Bonder MJ, Deelen P, Zeng B, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53:1300-10.
- Reyes-Palomares A, Gu M, Grubert F, Berest I, Sa S, Kasowski M, et al. Remodeling of active endothelial enhancers is associated with aberrant gene-regulatory networks in pulmonary arterial hypertension. Nat Commun. 2020;11:1673.
- Haynes BC, Maier EJ, Kramer MH, Wang PI, Brown H, Brent MR. Mapping functional transcription factor networks from gene expression data. Genome Res. 2013;23:1319-28.
- Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14:1083-6.
- Chan TE, Stumpf MPH, Babtie AC. Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures. Cell Syst. 2017;5:251-267.e3.
- Huynh-Thu VA, Geurts P. dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data. Sci Rep. 2018;8:3384.
- Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res. 2019;29:1363-75.
- Han H, Cho J-W, Lee S, Yun A, Kim H, Bae D, et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2018;46:D380-6.
- Liu Z-P, Wu C, Miao H, Wu H. RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database (Oxford). 2015;2015.
- Fulco CP, Nasser J, Jones TR, Munson G, Bergman DT, Subramanian V, et al. Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations. Nat Genet. 2019;51:1664-9.
- Schraivogel D, Gschwind AR, Milbank JH, Leonce DR, Jakob P, Mathur L, et al. Targeted Perturb-seq enables genome-scale genetic screens in single cells. Nat Methods. 2020;17:629-35.
- Pliner HA, Packer JS, McFaline-Figueroa JL, Cusanovich DA, Daza RM, Aghamirzaie D, et al. Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data. Mol Cell. 2018;71:858-871.e8.
- Stone M, Li J, Grace McCalla S, Fotuhi Siahpirani A, Periyasamy V, Shin J, Roy S. Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data. bioRxiv 2021.06.01.446671;
- Bunina D, Germain P-L, Lopez Tobon A, Fernandez-Novel Marx N, Arnold C, Ó hEachteirn A, et al. Pathological LSD1 mutations cause HDAC-mediated aberrant gene repression during early cell differentiation. BioRxiv. 2021.
- Carty M, Zamparo L, Sahin M, González A, Pelossof R, Elemento O, et al. An integrated model for detecting significant chromatin interactions from high-resolution Hi-C data. Nat Commun. 2017;8:15454.
- Ouma WZ, Pogacar K, Grotewold E. Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. PLoS Comput Biol. 2018;14:e1006098.
- Langlais D, Barreiro LB, Gros P. The macrophage IRF8/IRF1 regulome is required for protection against infections and is associated with chronic inflammation. J Exp Med. 2016;213:585-603.
- Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech. 2008;2008:P10008.
- Bujold D, Morais DA de L, Gauthier C, Côté C, Caron M, Kwan T, et al. The international human epigenome consortium data portal. Cell Syst. 2016;3:496-499.e2.
- Garg S, Reyes-Palomares A, He L, Bergeron A, Lavallée V-P, Lemieux S, et al. Hepatic leukemia factor is a novel leukemic stem cell regulator in DNMT3A, NPM1, and FLT3-ITD triple-mutated AML. Blood. 2019;134:263-76.
- Calderon D, Nguyen MLT, Mezger A, Kathiria A, Müller F, Nguyen V, et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat Genet. 2019;51:1494-505.
- Holland CH, Szalai B, Saez-Rodriguez J. Transfer of regulatory knowledge from human to mouse for functional genomics analysis. Biochim Biophys Acta Gene Regul Mech. 2020;1863:194431.
- Pai AA, Baharian G, Pagé Sabourin A, Brinkworth JF, Nédélec Y, Foley JW, et al. Widespread shortening of 3' untranslated regions and increased exon inclusion are evolutionarily conserved features of innate immune responses to infection. PLoS Genet. 2016;12:e1006338.
- Medzhitov R, Horng T. Transcriptional control of the inflammatory response. Nat Rev Immunol. 2009;9:692-703.
- Leseigneur C, Lê-Bury P, Pizarro-Cerdá J, Dussurget O. Emerging evasion mechanisms of macrophage defenses by pathogenic bacteria. Front Cell Infect Microbiol. 2020;10:577559.
- Jones G-R, Brown SL, Phythian-Adams AT, Ivens AC, Cook PC, MacDonald AS. The Methyl-CpG-Binding Protein Mbd2 Regulates Susceptibility to Experimental Colitis via Control of CD11c+ Cells and Colonic Epithelium. Front Immunol. 2020;11:183.
- Festa RA, Thiele DJ. Copper at the front line of the host-pathogen battle. PLoS Pathog. 2012;8:e1002887.
- Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh P-R, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet. 2015;47:1228-35.
- Udalova IA, Mantovani A, Feldmann M. Macrophage heterogeneity in the context of rheumatoid arthritis. Nat Rev Rheumatol. 2016;12:472-85.
- Ma C, Xia Y, Yang Q, Zhao Y. The contribution of macrophages to systemic lupus erythematosus. Clin Immunol. 2019;207:1-9.
- Harigai M, Kawamoto M, Hara M, Kubota T, Kamatani N, Miyasaka N. Excessive production of IFN-gamma in patients with systemic lupus erythematosus and its contribution to induction of B lymphocyte stimulator/B cell-activating factor/TNF ligand superfamily-13B. J Immunol. 2008;181:2211-9.
- Kato M. New insights into IFN-γ in rheumatoid arthritis: role in the era of JAK inhibitors. Immunol Med. 2020;43:72-8.
- Rönnblom L, Leonard D. Interferon pathway in SLE: one key to unlocking the mystery of the disease. Lupus Sci Med. 2019;6:e000270.
- Andreou N-P, Legaki E, Gazouli M. Inflammatory bowel disease pathobiology: the role of the interferon signature. Ann Gastroenterol. 2020;33:125-33.
- Gatchalian J, Liao J, Maxwell MB, Hargreaves DC. Control of Stimulus-Dependent Responses in Macrophages by SWI/SNF Chromatin Remodeling Complexes. Trends Immunol. 2020;41:126-40.
- Buhling F, Kellner U, Guenther D, Kahl S, Brömme D, Weber E, et al. Characterization of novel anti-cathepsin W antibodies and cellular distribution of cathepsin W in the gastrointestinal tract. Biol Chem. 2002;383:1285-9.
- Pires D, Marques J, Pombo JP, Carmo N, Bettencourt P, Neyrolles O, et al. Role of Cathepsins in Mycobacterium tuberculosis Survival in Human Macrophages. Sci Rep. 2016;6:32247.
- Sigalova OM, Shaeiri A, Forneris M, Furlong EE, Zaugg JB. Predictive features of gene expression variation reveal mechanistic link with differential expression. Mol Syst Biol. 2020;16:e9539.
- Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317-30.
- Zeitlinger J. Seven myths of how transcription factors read the cis-regulatory code. Current Opinion in Systems Biology. 2020;23:22-31.
- Nasser J, Bergman DT, Fulco CP, Guckelberger P, Doughty BR, Patwardhan TA, et al. Genome-wide enhancer maps link risk variants to disease genes. Nature. 2021;593:238-43.
- Ibarra IL, Hollmann NM, Klaus B, Augsten S, Velten B, Hennig J, et al. Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions. Nat Commun. 2020;11:124.
- Bunina D, Abazova N, Diaz N, Noh K-M, Krijgsveld J, Zaugg JB. Genomic Rewiring of SOX2 Chromatin Interaction Network during Differentiation of ESCs to Postmitotic Neurons. Cell Syst. 2020;10:480-494.e8.
- Lewis JD, Meehan RR, Henzel WJ, Maurer-Fogy I, Jeppesen P, Klein F, et al. Purification, sequence, and cellular localization of a novel chromosomal protein that binds to methylated DNA. Cell. 1992;69:905-14.
- Qin W, Scicluna BP, van der Poll T. The role of host cell DNA methylation in the immune response to bacterial infection. Front Immunol. 2021;12:696280.
- Fadini GP, Cappellari R, Mazzucato M, Agostini C, Vigili de Kreutzenberg S, Avogaro A. Monocyte-macrophage polarization balance in pre-diabetic individuals. Acta Diabetol. 2013;50:977-82.
- Samaan MC, Missiuna P, Peterson D, Thabane L. Understanding the role of the immune system in adolescent idiopathic scoliosis: Immunometabolic CONnections to Scoliosis (ICONS) study protocol. BMJ Open. 2016;6:e011812.
- Keller M, Mazuch J, Abraham U, Eom GD, Herzog ED, Volk H-D, et al. A circadian clock in macrophages controls inflammatory immune responses. Proc Natl Acad Sci USA. 2009;106:21407-12.
- Rana A, Musto AE. The role of inflammation in the development of epilepsy. J Neuroinflammation. 2018;15:144.
- Liao X, Shen Y, Zhang R, Sugi K, Vasudevan NT, Alaiti MA, et al. Distinct roles of resident and nonresident macrophages in nonischemic cardiomyopathy. Proc Natl Acad Sci USA. 2018;115:E4661-9.
- Chiba T, Umegaki K. Pivotal roles of monocytes/macrophages in stroke. Mediators Inflamm. 2013;2013:759103.
- Boyle AK, Rinaldi SF, Norman JE, Stock SJ. Preterm birth: Inflammation, fetal injury and treatment strategies. J Reprod Immunol. 2017;119:62-6.
- Garg S, Reyes-Palomares A, He L, Bergeron A, Lavallée V-P, Lemieux S, et al. Hepatic leukemia factor is a novel leukemic stem cell regulator in DNMT3A, NPM1, and FLT3-ITD triple-mutated AML. Blood, The Journal of the American Society of Hematology. 2019;134:263-76.
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
- Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. BioRxiv. 2016.
- Orecchioni M, Ghosheh Y, Pramod AB, Ley K. Macrophage Polarization: Different Gene Signatures in M1(LPS+) vs. Classically and M2(LPS-) vs. Alternatively Activated Macrophages. Front Immunol. 2019;10:1084.
- Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47:D1005-12.
- Alexa A, Rahnenführer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006;22:1600-7.
Cited by
Sorted by citations
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 · see citing works
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 · see citing works
Enhancer grammar of liver cell types and hepatocyte zonation states
González-Blas, C. B., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2022) · doi:10.1101/2022.12.08.519575 · Cited by 5 · see citing works
- Authors
- Kamal, A., Arnold, C., Claringbould, A., Moussa, R., Servaas, N. H., Kholmatov, M., Daga, N., Nogina, D., Mueller-Dott, S., Reyes-Palomares, A., Palla, G., Sigalova, O., Bunina, D., Pabst, C., & Zaugg, J. B.
- Affiliations
- Heidelberg University (DE)
- European Molecular Biology Laboratory (DE)
- Helmholtz Munich (DE)
- Corresponding author
- Zaugg, Judith B.
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2021-12-21
- OpenAlex
- W4200401870
- Cited by
- 12
- References
- 19
- Citations by year
- 2024: 4 · 2023: 5 · 2022: 3
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Funders
- European Commission (847543, 860002, HORIZON2020, H2020, Horizon 2020 MSCA)
- Horizon 2020 Framework Programme (860002, 847543)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- transcription factor-gene interactions, enhancer-gene interactions, open chromatin, ATAC-seq, H3K27ac, Hi-C, macrophages, macrophage polarization, PURA, non-coding variants, autoimmune disease, infection response, cancer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
