Kashtanova DA, Mamchur AA, Dzhumaniyazova IH, Ivanov MV, Erema VV, Zelenova EA, Yakovchik AY, Gusakova MS, Rumyantseva AM, Terekhov MV, Matkava LR, Akopyan AA, Strazhesko ID, Yudin VS, Makarov VV, Kraevoy SA, Tkacheva ON, Yudin SM (2023) Cognitive impairment in long-living adults: a genome-wide association study, polygenic risk score model and molecular modeling of the APOE protein. Front Aging Neurosci 15:1273825

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gusakova MS, Ivanov MV, Kashtanova DA, Taraskina AN, Erema VV, Mikova VM, Loshkarev RI, Ignatyeva OA, Akinshina AI, Mitrofanov SI, Snigir EA, Yudin VS, Makarov VV, Keskinov AA, Yudin SM (2023) GWAS reveals genetic basis of a predisposition to severe COVID-19 through in silico modeling of the FYCO1 protein. Front Med 10:1178939

Article  Google Scholar 

Keaton, J.M., Kamali, Z., Xie, T., Vaez, A., Williams, A., Goleva, S.B., Ani, A., Evangelou, E., Hellwege, J.N., Yengo, L., Young, W.J., Traylor, M., Giri, A., Zheng, Z., Zeng, J., Chasman, D.I., Morris, A.P., Caulfield, M.J., Hwang, S.-J., Kooner, J.S., Conen, D., Attia, J.R., Morrison, A.C., Loos, R.J.F., Kristiansson, K., Schmidt, R., Hicks, A.A., Pramstaller, P.P., Nelson, C.P., Samani, N.J., Risch, L., Gyllensten, U., Melander, O., Riese, H., Wilson, J.F., Campbell, H., Rich, S.S., Psaty, B.M., Lu, Y., Rotter, J.I., Guo, X., Rice, K.M., Vollenweider, P., Sundström, J., Langenberg, C., Tobin, M.D., Giedraitis, V., Luan, J., Tuomilehto, J., Kutalik, Z., Ripatti, S., Salomaa, V., Girotto, G., Trompet, S., Jukema, J.W., Harst, P., Ridker, P.M., Giulianini, F., Vitart, V., Goel, A., Watkins, H., Harris, S.E., Deary, I.J., Most, P.J., Oldehinkel, A.J., Keavney, B.D., Hayward, C., Campbell, A., Boehnke, M., Scott, L.J., Boutin, T., Mamasoula, C., Järvelin, M.-R., Peters, A., Gieger, C., Lakatta, E.G., Cucca, F., Hui, J., Knekt, P., Enroth, S., De Borst, M.H., Polašek, O., Concas, M.P., Catamo, E., Cocca, M., Li-Gao, R., Hofer, E., Schmidt, H., Spedicati, B., Waldenberger, M., Strachan, D.P., Laan, M., Teumer, A., Dörr, M., Gudnason, V., Cook, J.P., Ruggiero, D., Kolcic, I., Boerwinkle, E., Traglia, M., Lehtimäki, T., Raitakari, O.T., Johnson, A.D., Newton-Cheh, C., Brown, M.J., Dominiczak, A.F., Sever, P.J., Poulter, N., Chambers, J.C., Elosua, R., Siscovick, D., Esko, T., Metspalu, A., Strawbridge, R.J., Laakso, M., Hamsten, A., Hottenga, J.-J., Geus, E., Morris, A.D., Palmer, C.N.A., Nolte, I.M., Milaneschi, Y., Marten, J., Wright, A., Zeggini, E., Howson, J.M.M., O’Donnell, C.J., Spector, T., Nalls, M.A., Simonsick, E.M., Liu, Y., Duijn, C.M., Butterworth, A.S., Danesh, J.N., Menni, C., Wareham, N.J., Khaw, K.-T., Sun, Y.V., Wilson, P.W.F., Cho, K., Visscher, P.M., Denny, J.C., Million Veteran Program, Lifelines Cohort Study, CHARGE consortium, ICBP Consortium, Levy, D., Edwards, T.L., Munroe, P.B., Snieder, H., Warren, H.R.: Genome-wide analysis in over 1 million individuals of european ancestry yields improved polygenic risk scores for blood pressure traits. Nat. Genet. 56(5), 778–791 (2024)

Luo J, Wu X, Cheng Y, Chen G, Wang J, Song X (2023) Expression quantitative trait locus studies in the era of single-cell omics. Front Genet 14:1182579

Article  CAS  PubMed  PubMed Central  Google Scholar 

Adil A, Kumar V, Jan AT, Asger M (2021) Single-Cell transcriptomics: Current methods and challenges in data acquisition and analysis. Front Neurosci 15:591122

Article  PubMed  PubMed Central  Google Scholar 

Zhernakova DV, Deelen P, Vermaat M, Iterson M, Galen M, Arindrarto W, Hof P, Mei H, Dijk F, Westra H-J, Bonder MJ, Rooij J, Verkerk M, Jhamai PM, Moed M, Kielbasa SM, Bot J, Nooren I, Pool R, Dongen J, Hottenga JJ, Stehouwer CDA, Kallen CJH, Schalkwijk CG, Zhernakova A, Li Y, Tigchelaar EF, Klein N, Beekman M, Deelen J, Heemst D, Berg LH, Hofman A, Uitterlinden AG, Greevenbroek MMJ, Veldink JH, Boomsma DI, Duijn CM, Wijmenga C, Slagboom PE, Swertz MA, Isaacs A, Meurs JBJ, Jansen R, Heijmans BT, Hoen PAC, Franke L (2017) Identification of context-dependent expression quantitative trait loci in whole blood. Nat Genet 49(1):139–145

Magnusson, R., Tegnér, J.N., Gustafsson, M.: Deep neural network prediction of genome-wide transcriptome signatures - beyond the black-box. npj Systems Biology and Applications 8(1), 1–8 (2022)

Lee D, Gorkin DU, Baker M, Strober BJ, Asoni AL, McCallion AS, Beer MA (2015) A method to predict the impact of regulatory variants from DNA sequence. Nat Genet 47(8):955–961

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kelley DR, Snoek J, Rinn JL (2016) Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res 26(7):990–999

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12(10):931–934

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG (2018) Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet 50(8):1171–1179

Article  CAS  PubMed  PubMed Central  Google Scholar 

Svetlichnyy D, Imrichova H, Fiers M, Kalender Atak Z, Aerts S (2015) Identification of High-Impact cis-regulatory mutations using transcription factor specific random forest models. PLoS Comput Biol 11(11):1004590

Article  Google Scholar 

Chen KM, Wong AK, Troyanskaya OG, Zhou J (2022) A sequence-based global map of regulatory activity for deciphering human genetics. Nat Genet 54(7):940–949

Article  CAS  PubMed  PubMed Central  Google Scholar 

Quang D, Xie X (2016) DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res 44(11):107

Article  Google Scholar 

Shaymardanov AM, Antonova OA, Sokol AD, Deinichenko KA, Kazakova PG, Milovanov MM, Zakubansky AV, Akinshina AI, Tsypkina AV, Romanova SV, Muhin VE, Mitrofanov SI, Yudin VS, Yudin SM, Makhotenko AV, Keskinov AA, Kraevoy SA, Snigir EA, Svetlichnyy DV, Skvortsova VI (2022) Single-Cell gene expression analysis revealed immune cell signatures of delta COVID-19. Cells 11(19):2950

Article  CAS  PubMed  PubMed Central  Google Scholar 

Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.R., Raychaudhuri, S.: Fast, sensitive and accurate integration of single-cell data with harmony. Nat. Methods 16(12) (2019)

Krusche P, Trigg L, Boutros PC, Mason CE, De La Vega FM, Moore BL, Gonzalez-Porta M, Eberle MA, Tezak Z, Lababidi S, Truty R, Asimenos G, Funke B, Fleharty M, Chapman BA, Salit M, Zook JM (2019) Global Alliance for Genomics and Health Benchmarking Team: Best practices for benchmarking germline small-variant calls in human genomes. Nat Biotechnol 37(5):555–560

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pedersen BS, Quinlan AR (2018) Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34(5):867–868

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kim S, Scheffler K, Halpern AL, Bekritsky MA, Noh E, Källberg M, Chen X, Kim Y, Beyter D, Krusche P, Saunders CT (2018) Strelka2: fast and accurate calling of germline and somatic variants. Nat Methods 15(8):591–594

Article  CAS  PubMed  Google Scholar 

Yu, G., Wang, L.G., He, Q.Y.: ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31(14) (2015)

Shabalin AA (2012) Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28(10):1353–1358

Article  CAS  PubMed  PubMed Central  Google Scholar 

Durinck, S., Spellman, P.T., Birney, E., Huber, W.: Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomart. Nat. Protoc. 4(8) (2009)

Heger A, Webber C, Goodson M, Ponting CP, Lunter G (2013) GAT: a simulation framework for testing the association of genomic intervals. Bioinformatics 29(16):2046–2048

Article  CAS  PubMed  PubMed Central  Google Scholar 

Vu H, Ernst J (2022) Universal annotation of the human genome through integration of over a thousand epigenomic datasets. Genome Biol 23(1):1–37

Article  Google Scholar 

Zhang Y, Hardison RC (2017) Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation. Nucleic Acids Res 45(17):9823

Article  CAS  PubMed  PubMed Central  Google Scholar 

Burdett, T., Hastings, E., Welter, D., SPOT, EMBL-EBI, NHGRI: GWAS Catalog. https://www.ebi.ac.uk/gwas/efotraits/MONDO_0100096. Accessed: 2024-9-23

Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Gable, A.L., Fang, T., Doncheva, N.T., Pyysalo, S., Bork, P., Jensen, L.J., Mering, C.: The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51(D1) (2023)

Butler A, Hoffman P, Smibert P, Papalexi E, Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36(5):411–420

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yu G, Wang L-G, Han Y, He Q-Y (2012) clusterprofiler: an R package for comparing biological themes among gene clusters. OMICS 16(5):284

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chiu T-P, Comoglio F, Zhou T, Yang L, Paro R, Rohs R (2016) DNAshapeR: an R/Bioconductor package for DNA shape prediction and feature encoding. Bioinformatics 32(8):1211

Article  CAS  PubMed  Google Scholar 

Chen KM, Cofer EM, Zhou J, Troyanskaya OG (2019) Selene: a PyTorch-based deep learning library for sequence data. Nat Methods 16(4):315–318

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wang X, Shen X, Chen S, Liu H, Hong N, Zhong H, Chen X, Jin W (2022) Reinvestigation of classic T cell subsets and identification of novel cell subpopulations by Single-Cell RNA sequencing. J Immunol 208(2):396–406

Article  CAS  PubMed  Google Scholar 

Li H, Leun AM, Yofe I, Lubling Y, Gelbard-Solodkin D, Akkooi ACJ, Braber M, Rozeman EA, Haanen JBAG, Blank CU, Horlings HM, David E, Baran Y, Bercovich A, Lifshitz A, Schumacher TN, Tanay A, Amit I (2019) Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176(4):775–78918

Article  CAS  PubMed  Google Scholar 

Ramesh, A., Schubert, R.D., Greenfield, A.L., Dandekar, R., Loudermilk, R., Sabatino, J.J. Jr, Koelzer, M.T., Tran, E.B., Koshal, K., Kim, K., Pröbstel, A.-K., Banerji, D., University of California, San Francisco MS-EPIC Team, Guo, C.-Y., Green, A.J., Bove, R.M., DeRisi, J.L., Gelfand, J.M., Cree, B.A.C., Zamvil, S.S., Baranzini, S.E., Hauser, S.L., Wilson, M.R.: A pathogenic and clonally expanded B cell transcriptome in active multiple sclerosis. Proc. Natl. Acad. Sci. U. S. A. 117(37), 22932–22943 (2020)

Bryois J, Calini D, Macnair W, Foo L, Urich E, Ortmann W, Iglesias VA, Selvaraj S, Nutma E, Marzin M, Amor S, Williams A, Castelo-Branco G, Menon V, De Jager P, Malhotra D (2022) Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nat Neurosci 25(8):1104–1112

Article  CAS  PubMed