DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants.
Functional genetics
Functional genomics
Genome-wide association study
Non-coding genome
Non-coding variants
Single nucleotide polymorphism
Single nucleotide variants
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
06 Aug 2024
06 Aug 2024
Historique:
received:
06
03
2024
accepted:
25
07
2024
medline:
7
8
2024
pubmed:
7
8
2024
entrez:
6
8
2024
Statut:
epublish
Résumé
Genome-wide association studies (GWAS) have revealed a multitude of candidate genetic variants affecting the risk of developing complex traits and diseases. However, the highlighted regions are typically in the non-coding genome, and uncovering the functional causative single nucleotide variants (SNVs) is challenging. Prioritization of variants is commonly based on genomic annotation with markers of active regulatory elements, but current approaches still poorly predict functional variants. To address this, we systematically analyze six markers of active regulatory elements for their ability to identify functional variants. We benchmark against molecular quantitative trait loci (molQTL) from assays of regulatory element activity that identify allelic effects on DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility. We identify the combination of DNase footprints and divergent enhancer RNA (eRNA) as markers for functional variants. This signature provides high precision, but with a trade-off of low recall, thus substantially reducing candidate variant sets to prioritize variants for functional validation. We present this as a framework called FINDER-Functional SNV IdeNtification using DNase footprints and eRNA. We demonstrate the utility to prioritize variants using leukocyte count trait and analyze variants in linkage disequilibrium with a lead variant to predict a functional variant in asthma. Our findings have implications for prioritizing variants from GWAS, in development of predictive scoring algorithms, and for functionally informed fine mapping approaches.
Sections du résumé
BACKGROUND
BACKGROUND
Genome-wide association studies (GWAS) have revealed a multitude of candidate genetic variants affecting the risk of developing complex traits and diseases. However, the highlighted regions are typically in the non-coding genome, and uncovering the functional causative single nucleotide variants (SNVs) is challenging. Prioritization of variants is commonly based on genomic annotation with markers of active regulatory elements, but current approaches still poorly predict functional variants. To address this, we systematically analyze six markers of active regulatory elements for their ability to identify functional variants.
RESULTS
RESULTS
We benchmark against molecular quantitative trait loci (molQTL) from assays of regulatory element activity that identify allelic effects on DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility. We identify the combination of DNase footprints and divergent enhancer RNA (eRNA) as markers for functional variants. This signature provides high precision, but with a trade-off of low recall, thus substantially reducing candidate variant sets to prioritize variants for functional validation. We present this as a framework called FINDER-Functional SNV IdeNtification using DNase footprints and eRNA.
CONCLUSIONS
CONCLUSIONS
We demonstrate the utility to prioritize variants using leukocyte count trait and analyze variants in linkage disequilibrium with a lead variant to predict a functional variant in asthma. Our findings have implications for prioritizing variants from GWAS, in development of predictive scoring algorithms, and for functionally informed fine mapping approaches.
Identifiants
pubmed: 39107801
doi: 10.1186/s13059-024-03352-1
pii: 10.1186/s13059-024-03352-1
doi:
Substances chimiques
DNA-Binding Proteins
0
Enhancer RNAs
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
208Subventions
Organisme : Medical Research Council
ID : MC_UU_00007/2
Pays : United Kingdom
Organisme : Chief Scientist Office, Scottish Government Health and Social Care Directorate
ID : PCL/20/02
Organisme : Swiss National Science Foundation
ID : P500PB_206805
Pays : Switzerland
Informations de copyright
© 2024. The Author(s).
Références
Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, Reynolds AP, Sandstrom R, Qu H, Brody J, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190–5. https://doi.org/10.1126/science.1222794 .
doi: 10.1126/science.1222794
pubmed: 22955828
pmcid: 3771521
Qi T, Wu Y, Fang H, Zhang F, Liu S, Zeng J, Yang J. Genetic control of RNA splicing and its distinct role in complex trait variation. Nat Genet. 2022;54:1355–63. https://doi.org/10.1038/s41588-022-01154-4 .
doi: 10.1038/s41588-022-01154-4
pubmed: 35982161
pmcid: 9470536
Johnston AD, Simões-Pires CA, Thompson TV, Suzuki M, Greally JM. Functional genetic variants can mediate their regulatory effects through alteration of transcription factor binding. Nat Commun. 2019;10:3472. https://doi.org/10.1038/s41467-019-11412-5 .
doi: 10.1038/s41467-019-11412-5
pubmed: 31375681
pmcid: 6677801
Maurano MT, Haugen E, Sandstrom R, Vierstra J, Shafer A, Kaul R, Stamatoyannopoulos JA. Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo. Nat Genet. 2015;47:1393–401. https://doi.org/10.1038/ng.3432 .
doi: 10.1038/ng.3432
pubmed: 26502339
pmcid: 4666772
Jeong Y, Leskow FC, El-Jaick K, Roessler E, Muenke M, Yocum A, Dubourg C, Li X, Geng X, Oliver G, et al. Regulation of a remote Shh forebrain enhancer by the Six3 homeoprotein. Nat Genet. 2008;40:1348–53. https://doi.org/10.1038/ng.230 .
doi: 10.1038/ng.230
pubmed: 18836447
pmcid: 2648611
Lettice LA, Williamson I, Wiltshire JH, Peluso S, Devenney PS, Hill AE, Essafi A, Hagman J, Mort R, Grimes G, DeAngelis CL, Hill RE. Opposing functions of the ETS factor family define Shh spatial expression in limb buds and underlie polydactyly. Dev Cell. 2012;22:459–67. https://doi.org/10.1016/j.devcel.2011.12.010 .
doi: 10.1016/j.devcel.2011.12.010
pubmed: 22340503
pmcid: 3314984
Carrasco Pro S, Bulekova K, Gregor B, Labadorf A, Fuxman Bass JI. Prediction of genome-wide effects of single nucleotide variants on transcription factor binding. Sci Rep. 2020;10:17632. https://doi.org/10.1038/s41598-020-74793-4 .
doi: 10.1038/s41598-020-74793-4
pubmed: 33077858
pmcid: 7572467
Kasowski M, Grubert F, Heffelfinger C, Hariharan M, Asabere A, Waszak SM, Habegger L, Rozowsky J, Shi M, Urban AE, et al. Variation in transcription factor binding among humans. Science. 2010;328:232–5.
doi: 10.1126/science.1183621
pubmed: 20299548
pmcid: 2938768
Vernimmen D, Bickmore WA. The hierarchy of transcriptional activation: from enhancer to promoter. Trends Genet. 2015;31:696–708. https://doi.org/10.1016/j.tig.2015.10.004 .
doi: 10.1016/j.tig.2015.10.004
pubmed: 26599498
Grishin D, Gusev A. Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms. Nat Genet. 2022;54:837–49. https://doi.org/10.1038/s41588-022-01075-2 .
doi: 10.1038/s41588-022-01075-2
pubmed: 35697866
pmcid: 9886437
Su C, Gao L, May CL, Pippin JA, Boehm K, Lee M, Liu C, Pahl MC, Golson ML, Naji A;, et al. 3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D risk. Cell Metab. 2022;34:1394-1409.e4. https://doi.org/10.1016/j.cmet.2022.08.014 .
doi: 10.1016/j.cmet.2022.08.014
pubmed: 36070683
pmcid: 9664375
Moyerbrailean GA, Kalita CA, Harvey CT, Wen X, Luca F, Pique-Regi R. Which genetics variants in DNase-seq footprints are more likely to alter binding? PLoS Genet. 2016;12: e1005875. https://doi.org/10.1371/journal.pgen.1005875 .
doi: 10.1371/journal.pgen.1005875
pubmed: 26901046
pmcid: 4764260
Vierstra J, Lazar J, Sandstrom R, Halow J, Lee K, Bates D, Diegel M, Dunn D, Neri F, Haugen E, et al. Global reference mapping of human transcription factor footprints. Nature. 2020;583:729–36. https://doi.org/10.1038/s41586-020-2528-x .
doi: 10.1038/s41586-020-2528-x
pubmed: 32728250
pmcid: 7410829
Gazal S, Weissbrod O, Hormozdiari F, Dey KK, Nasser J, Jagadeesh KA, Weiner DJ, Shi H, Fulco CP, O’Connor LJ, et al. Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity. Nat Genet. 2022;54:827–36. https://doi.org/10.1038/s41588-022-01087-y .
doi: 10.1038/s41588-022-01087-y
pubmed: 35668300
pmcid: 9894581
Nasser J, Bergman DT, Fulco CP, Guckelberger P, Doughty BR, Patwardhan TA, Jones TR, Nguyen TH, Ulirsch JC, Lekschas F, et al. Genome-wide enhancer maps link risk variants to disease genes. Nature. 2021;593:238–43. https://doi.org/10.1038/s41586-021-03446-x .
doi: 10.1038/s41586-021-03446-x
pubmed: 33828297
pmcid: 9153265
Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M. Linking disease associations with regulatory information in the human genome. Genome Res. 2012;22:1748–59. https://doi.org/10.1101/gr.136127.111 .
doi: 10.1101/gr.136127.111
pubmed: 22955986
pmcid: 3431491
Chen HV, Lorenzini MH, Lavalle SN, Sajeev K, Fonesca A, Fiaux PC, Sen A, Luthra I, Ho AJ, Chen AR, et al. Deletion mapping of regulatory elements for GATA3 in T cells reveals a distal enhancer involved in allergic diseases. J Hum Genet. 2023;110(4):703–14.
doi: 10.1016/j.ajhg.2023.03.008
Gasperini M, Findlay GM, McKenna A, Milbank JH, Lee C, Zhang MD, Cusanovich DA, Shendure J. CRISPR/Cas9-mediated scanning for regulatory elements required for HPRT1 expression via thousands of large, programmed genomic deletions. Am J Hum Genet. 2017;101(2):192–205.
doi: 10.1016/j.ajhg.2017.06.010
pubmed: 28712454
pmcid: 5544381
Wang Z, Zhao G, Li B, Fang Z, Chen Q, Wang X, Luo T, Wang Y, Zhou Q, Li K, et al. (2022) Performance comparison of computational methods for the prediction of the function and pathogenicity of non-coding variants. Genom Proteomics Bioinform. 7:S1672–0229(22)00016-X. https://doi.org/10.1016/j.gpb.2022.02.002 .
Tabarini N, Biagi E, Uva P, Iovino E, Pippucci T, Seri M, Cavalli A, Ceccherini I, Rusmini M, Viti F. Exploration of tools for the interpretation of human non-coding variants. Int J Mol Sci. 2022;23:12977. https://doi.org/10.3390/ijms232112977 .
doi: 10.3390/ijms232112977
pubmed: 36361767
pmcid: 9654743
Hou TY, Kraus WL. Spirits in the material world: enhancer RNAs in transcriptional regulation. Trends Biochem Sci. 2021;46:138–53. https://doi.org/10.1016/j.tibs.2020.08.007 .
doi: 10.1016/j.tibs.2020.08.007
pubmed: 32888773
Yao L, Liang J, Ozer A, Leung AKY, Lis JT, Yu H. A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers. Nat biotechnol. 2022;40(7):1056–65.
doi: 10.1038/s41587-022-01211-7
pubmed: 35177836
pmcid: 9288987
Abramov S, Boystov A, Bykova D, Penzar DD, Yevshin I, Kolmykov SK, Fridman MV, Favorov AV, Vorontsov IE, Baulin E, Kolpakov FF, Makeev V, Kulakovskiy IV. Landscape of allele-specific transcription factor binding in the human genome. Nat Commun. 2021;12(1):2751. https://doi.org/10.1038/s41467-021-23007-0 .
doi: 10.1038/s41467-021-23007-0
pubmed: 33980847
pmcid: 8115691
Van Arensbergen J, Pagie L, FitzPatrick VD, de Haas M, Baltissen MP, Comoglio F, van der Weide R, Teunissen H, Vosa U, Franke L, de Wit E, Vermeulen M, Bussemaer HHJ, van Steensel B. High-throughput identification of human SNPs affecting regulatory element activity. Nat Genet. 2019;51(7):1160–9.
doi: 10.1038/s41588-019-0455-2
pubmed: 31253979
pmcid: 6609452
Zheng Z, Huang D, Wang J, Zhao K, Zhou Y, Guo Z, Zhai S, Xu H, Cui H, Yao H, Wang Z, Yi X, Zhang S, Sham PC, Li MJ. QTLbase: an integrative resource for quantitative trait loci across multiple human molecular phenotypes. NAR. 2020;48(D1):D983-991.
doi: 10.1093/nar/gkz888
pubmed: 31598699
Zheng Z, Huang D, Wang J, Zhao K, Zhou Y, Guo Z, Zhai S, Xu H, Cui H, Yao H, Wang Z, Yi X, Zhang S, Sham PC, Li MJ. QTLbase [Internet]; 2016–2022. Available from: http://www.mulinlab.org/qtlbase .
Vierstra J, Lazar J, Sandstrom R, Halow J, Lee K, Bates D, Diegel M, Dunn D, Neri F, Haugen E, et al., Digital genomic footprinting [Internet]; 2020. Available from: https://www.vierstra.org/resources/dgf .
Sollis E, Mosaku A, Abid A, Buniello A, Cerezo M, Gil L, Groza T, Gunes O, Hall P, Hayhurst, et al. The NHGRI-EBI GWAS catalog: knowledgebase and deposition resource. NAR. 2023;51(D1):D977-985.
doi: 10.1093/nar/gkac1010
pubmed: 36350656
Sollis E, Mosaku A, Abid A, Buniello A, Cerezo M, Gil L, Groza T, Güneş O, Hall P, Hayhurst J, et al.., NHGRI-EBI GWAS catalog [Internet]; 2022. Available from: https://www.ebi.ac.uk/gwas/ .
Wang F, Bai X, Wang Y, Jiang Y, Ai B, Zhang Y, Liu Y, Xu M, Wang Q, Han X, et al. ATACdb: a comprehensive human chromatin accessibility database. Nucleic Acids Res. 2021;49(D1):D55–64.
doi: 10.1093/nar/gkaa943
pubmed: 33125076
Wang F, Bai X, Wang Y, Jiang Y, Ai B, Zhang Y, Liu Y, Xu M, Wang Q, Han X, et al., ATACdb [Internet]; 2020. Available from: https://bio.liclab.net/ATACdb/ .
Kolmykov S, Yevshin I, Kuulyashov M, Sharipov R, Kondrakhin Y, Makeev VJ, Kulakovskiy IV, Kel A, Kolpakov F. GTRD: an integrated view of transcription regulation. NAR. 2021;49(D1):D104–11.
doi: 10.1093/nar/gkaa1057
pubmed: 33231677
Kolmykov S, Yevshin I, Kuulyashov M, Sharipov R, Kondrakhin Y, Makeev VJ, Kulakovskiy IV, Kel A, Kolpakov F. GTRD [Internet]; 2021. Available from: https://gtrd.biouml.org .
Biddie SC, John S, Sabo PJ, Thurman RE, Johnson TA, Schiltz RL, Miranda TB, Sung MH, Trump S, Lightman SL, et al. Transcription factor AP1 potentiates chromatin accessibility and glucocorticoid receptor binding. Mol Cell. 2011;43(1):145–55. https://doi.org/10.1016/j.molcel.2011.06.016 . (PMID: 21726817).
doi: 10.1016/j.molcel.2011.06.016
pubmed: 21726817
pmcid: 3138120
Meuleman W, Muratov A, Rynes E, Halow J, Lee K, Bates D, Diegel M, Dunn D, Neri F, Teodosiadis A, et al. Index and biological spectrum of human DNase I hypersensitive sites. Nature. 2020;584:244–51. https://doi.org/10.1038/s41586-020-2559-3 .
doi: 10.1038/s41586-020-2559-3
pubmed: 32728217
pmcid: 7422677
Meuleman W, Muratov A, Rynes E, Halow J, Lee K, Bates D, Diegel M, Dunn D, Neri F, Teodosiadis A, et al Index and biological spectrum of human DNase I hypersensitive sites [Internet]; 2020. Available from: https://www.meuleman.org/research/dhsindex/
Smedley D, Schubach M, Jacobsen JOB, Köhler S, Zemojtel T, Spielmann M, Jäger M, Hochheiser H, Washington NL, McMurry JA, et al. A whole-genome analysis framework for effective identification of pathogenic regulatory variants in Mendelian disease. Am J Hum Genet. 2016;99:595–606. https://doi.org/10.1016/j.ajhg.2016.07.005 .
doi: 10.1016/j.ajhg.2016.07.005
pubmed: 27569544
pmcid: 5011059
Tanigawa Y, Dyer ES, Bejerano G. WhichTF is functionally important in your open chromatin data? PLoS Comput Biol. 2022;18(8): e1010378. https://doi.org/10.1371/journal.pcbi.1010378 .
doi: 10.1371/journal.pcbi.1010378
pubmed: 36040971
pmcid: 9426921
Friman, ET. PeakPredict. Zenodo. 2024. https://zenodo.org/doi/10.5281/zenodo.12706471 .
Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS, Hitz BC, Gabdank I, Narayanan AK, Ho M, Lee BT et al., ENCODE portal [Internet]; 2016. Available from: https://www.encodeproject.org/ .
Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. NAR. 2001;21(1):308–11.
doi: 10.1093/nar/29.1.308
Quinlan AR and Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 26(6):841–2
Huang M, Wangg Y, Yangg M, Yan J, Yangg H, Zhuangg W, Xu Y, Koeffler HP, Lin DC, Chen X. dbInDel [Internet]; 202. Available from: http://enhancer-indel.cam-su.org .
Yao L, Liang J, Ozer A, Leung AKY, Lis JT, Yu H. PINTS web portal [Internet]; 2022. Available from: https://pints.yulab.org .
Friman, ET. PeakPredict. Github .2024. https://github.com/efriman/PeakPredict .
Rai V, Quang DX, Erdos MR, Cusanovich DA, Daza RM, Narisu N, Zou LS, Didion JP, Guan Y, Shendure J, et al. Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Mol Metab. 2020;32:109–21. https://doi.org/10.1016/j.molmet.2019.12.006 .
doi: 10.1016/j.molmet.2019.12.006
pubmed: 32029221
Torres JM, Abdalla M, Payne A, Fernandez-Tajes J, Thurner M, Nylander V, Gloyn AL, Mahajan A, McCarthy MI. A multi-omic integrative scheme characterizes tissues of action at loci associated with type 2 diabetes. Am J Hum Genet. 2020;107:1011–28. https://doi.org/10.1016/j.ajhg.2020.10.009 .
doi: 10.1016/j.ajhg.2020.10.009
pubmed: 33186544
pmcid: 7820628
Breeze CE, Haugen E, Reynolds A, Teschendorff A, van Dongen J, Lan Q, Rothman N, Bourque G, Dunham I, Beck S, et al. Integrative analysis of 3604 GWAS reveals multiple novel cell type-specific regulatory associations. Genome Biol. 2022;23:13. https://doi.org/10.1186/s13059-021-02560-3 .
doi: 10.1186/s13059-021-02560-3
pubmed: 34996498
pmcid: 8742386
Garrett-Sinha LA. Review of Ets1 structure, function, and roles in immunity. Cell Mol Life Sci. 2013;70:3375–90. https://doi.org/10.1007/s00018-012-1243-7 .
doi: 10.1007/s00018-012-1243-7
pubmed: 23288305
pmcid: 3636162
Schwartz AM, Demin DE, Vorontsov IE, Kasyanov AS, Putlyaeva LV, Tatosyan KA, Kulakovskiy IV, Kuprash DV. Multiple single nucleotide polymorphisms in the first intron of the IL2RA gene affect transcription factor binding and enhancer activity. Gene. 2017;603:50–6.
doi: 10.1016/j.gene.2016.11.032
Johansson Å, Rask-Andersen M, Karlsson T, Ek WE. (2019) Genome-wide association analysis of 350 000 Caucasians from the UK Biobank identifies novel loci for asthma, hay fever and eczema Hum Mol Genet. 28:4022–4041. https://doi.org/10.1093/hmg/ddz175 .
Portelli MA, Dijk FN, Ketelaar ME, Shrine N, Hankinson J, Bhaker S, Grotenboer NS, Obeidat M, Henry AP, Billington CK, et al. Phenotypic and functional translation of IL1RL1 locus polymorphisms in lung tissue and asthmatic airway epithelium. JCI Insight. 2020;5: e132446. https://doi.org/10.1172/jci.insight.132446 .
doi: 10.1172/jci.insight.132446
pubmed: 32324168
pmcid: 7205441
Daya M, Rafaels N, Burnetti TM, Chhavan S, Levin AM, Shetty A, Gignoux CR, Boorgula MP, Wojcik G, et al. Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations. Nat Commun. 2019;10(1):880. https://doi.org/10.1038/s41467-019-08469-7 .
doi: 10.1038/s41467-019-08469-7
pubmed: 30787307
pmcid: 6382865
Trajanoska K, Bherer C, Taliun D, Zhouu S, Richards JB, Mooser V. From target discovery to clinical drug development with human genetics. Nature. 2023;620(7975):737–45.
doi: 10.1038/s41586-023-06388-8
pubmed: 37612393
Dong S, Zhao N, Spragins E, Kagda MS, Li M, Assis P, Jolanki O, Luo Y, Cherry JK, Boyle AP et al. Annotating and prioritizing human non-coding variants with RegulomeDB v.2 Nat Genet. 20223;55(5):724–726
Downes DJ, Cross AR, Hua P, Roberts N, Schwessinger R, Cutler AJ, Munis AM, Brown J, Mielczarek O, de Andrea CE, et al. Identification of LZTFL1 as a candidate effector gene at a COVID-19 risk locus. Nat Genet. 2021;53(11):1606–15.
doi: 10.1038/s41588-021-00955-3
pubmed: 34737427
pmcid: 7611960
Wu H, Nord AS, Akiyama JA, Shoukry M, Afzal V, Rubin EM, et al. Tissue-specific RNA expression marks distant-acting developmental enhancers. PLoS Genet. 2014;10(9):e1004610. https://doi.org/10.1371/journal.pgen.1004610 .
doi: 10.1371/journal.pgen.1004610
pubmed: 25188404
pmcid: 4154669
Lee SA, Kristjánsdóttir K, Kwak H. eRNA co-expression network uncovers TF dependency and convergent cooperativity. Sci Rep. 2023;13(1):19085.
doi: 10.1038/s41598-023-46415-2
pubmed: 37925545
pmcid: 10625640
Stefan K, Barksi A. Cis-regulatory atlas of primary human CD4+ T cells. BMC Genomics. 2023;24(1):253. https://doi.org/10.1186/s12864-023-09288-3 .
doi: 10.1186/s12864-023-09288-3
pubmed: 37170195
pmcid: 10173520
Vockley CM, D’Ippolito AM, McDowell IC, Majoros WH, Safi A, Song L, Crawford GE, Reddy TE. Direct GR binding sites potentiate clusters of TF binding across the human genome. Cell. 2016;166(5):1269–81.
doi: 10.1016/j.cell.2016.07.049
pubmed: 27565349
pmcid: 5046229
Mercer TR, Edwards SL, Clark MB, Nephh SJ, Wang H, Stergachis AB, John S, Sandstrom R, Li G, Sandhu KS, et al. DNase I-hypersensitive exons colocalize with promoters and distal regulatory elements. Nat Genet. 2013;45(8):852–9.
doi: 10.1038/ng.2677
pubmed: 23793028
pmcid: 4405174
Partridge EC, Chhetri SB, Prokop JW, Ramaker RC, Jansen CS, Goh ST, Machiewicz M, Newberry KM, Brandsmeier LA, Meadows SK, et al. Occupancy maps of 208 chromatin-associated proteins in one human cell type. Nature. 2020;583(7818):720–8.
doi: 10.1038/s41586-020-2023-4
pubmed: 32728244
pmcid: 7398277
Sung MH, Guertin MJ, Baek S, Hager GL. DNase footprint signatures are dictated by factor dynamics and DNA sequence. Mol Cell. 2014;56(2):275–85.
doi: 10.1016/j.molcel.2014.08.016
pubmed: 25242143
pmcid: 4272573
Oh KS, Ha J, Baek S, Sung MH. XL-DNase-seq: improved footprinting of dynamic transcription factors. Epigenetics Chromatin. 2019;12(1):30. https://doi.org/10.1186/s13072-019-0277-6 .
doi: 10.1186/s13072-019-0277-6
pubmed: 31164146
pmcid: 6547507
Calviello AK, Hirsekorn A, Wurmus R, Yusuf D, Ohler U. Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets using protocol-specific bias modelling. Genome Biol. 2019;20(1):42.
doi: 10.1186/s13059-019-1654-y
Kumasaka N, Knights AJ, Gaffney DJ. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat Genet. 2016;48(2):206–13.
doi: 10.1038/ng.3467
pubmed: 26656845
Huang M, Wangg Y, Yangg M, Yan J, Yangg H, Zhuangg W, Xu Y, Koeffler HP, Lin DC, Chen X. dbInDel: a database of enhancer-associated insertion and deletion variants by analysis of H3K27ac ChIP-Seq. Bioinformatics. 2020;36(5):1649–51.
doi: 10.1093/bioinformatics/btz770
pubmed: 31603498
Abramov S, Boystov A, Bykova D, Penzar DD, Yevshin I, Kolmykov SK, Fridman MV, Favorov AV, Vorontsov IE, Baulin E, Kolpakov FF, Makeev V, Kulakovskiy IV. ADASTRA [Internet]; 2021. Available from: https://adastra.autosome.org/bill-cipher/downloads?releaseName=Zanthar .
Hinrichs AS, Karolchik D, Baertsch R, Barber GP, Bejerano G, Clawson H, Diekhans M, Furey TS, Harte RA, Hsu F et al. The UCSC Genome Browser Database: update 2006. NAR 2006;34(D):D590–8
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J. Scikit-learn: machine learning in Python. J Machine Learn Res. 2011;12:2825–30.
Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inform Process Syst. 2017;30. https://doi.org/10.48550/arXiv.1705.07874 .
Steinhaus R, Robinson PN, Seelow D. FABIAN-variant: predicting the effects of DNA variants on transcription factor binding. NAR. 2022;50(W1):W322-329.
doi: 10.1093/nar/gkac393
pubmed: 35639768
pmcid: 9252790
Huang L, Rosen JD, Sun Q, Chen J, Wheeler MM, Zhou Y, Min Y, Kooperberg C, Conomos MP, Stilp AM, et al. TOP-LD: a tool to explore linkage disequilibrium with TOPMed whole genome sequence data. Am J Human Genet. 2022;109(6):1175–81. https://doi.org/10.1016/j.ajhg.2022.04.006 .
doi: 10.1016/j.ajhg.2022.04.006
Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38(4):576–89.
doi: 10.1016/j.molcel.2010.05.004
pubmed: 20513432
pmcid: 2898526
Review Commons report 1. Early evidence base. 2024. https://doi.org/10.15252/rc.2024235645 .
Review Commons report 2. Early evidence base. 2024. https://doi.org/10.15252/rc.2024765375 .
Biddie SC. FINDER, GitHub. 2024. https://github.com/sbiddie/FINDER/tree/v1.0 .
Biddie SC. FINDER, Zenodo.2024. https://zenodo.org/doi/10.5281/zenodo.12795448 .