AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model
Open accessPreprint — published as 10.1038/s41586-025-10014-0 · Paper page
Abstract
Deep learning models that predict functional genomic measurements from DNA sequence are powerful tools for deciphering the genetic regulatory code. Existing methods trade off between input sequence length and prediction resolution, thereby limiting their modality scope and performance. We present AlphaGenome, which takes as input 1 megabase of DNA sequence and predicts thousands of functional genomic tracks up to single base pair resolution across diverse modalities – including gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chro- matin contact maps, splice site usage, and splice junction coordinates and strength. Trained on human and mouse genomes, AlphaGenome matches or exceeds the strongest respective available external models on 24 out of 26 evaluations on variant effect prediction. AlphaGenome’s ability to simultaneously score variant effects across all modalities accurately recapitulates the mechanisms of clinically-relevant variants near the TAL1 oncogene. To facilitate broader use, we provide tools for making genome track and variant effect predictions from sequence.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Self-citationAn integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072 · see citing works
Twelve years of SAMtools and BCFtools
Danecek, P., Bonfield, J., Liddle, J., Marshall, J., Ohan, V., Pollard, M., Whitwham, A., Keane, T., McCarthy, S., Davies, R. M., et al. (2021) · GigaScience · doi:10.1093/gigascience/giab008 · Cited by 15,314 · see citing works
The Ensembl Variant Effect Predictor
McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016) · Genome Biology · doi:10.1186/s13059-016-0974-4 · Cited by 8,099 · see citing works
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · see citing works
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works
Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261 · see citing works
ClinVar: improving access to variant interpretations and supporting evidence
Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1153 · Cited by 4,196 · see citing works
Systematic Localization of Common Disease-Associated Variation in Regulatory DNA
Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., et al. (2012) · Science · doi:10.1126/science.1222794 · Cited by 3,716 · see citing works
Transformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169 · see citing works
Predicting splicing from primary sequence with deep learning
Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.015 · Cited by 2,853 · see citing works
Detection of nonneutral substitution rates on mammalian phylogenies
Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R., & Siepel, A. (2010) · Genome Research · doi:10.1101/gr.097857.109 · Cited by 2,353 · see citing works
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338 · see citing works
Accurate proteome-wide missense variant effect prediction with AlphaMissense
Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science · doi:10.1126/science.adg7492 · Cited by 2,163 · see citing works
Self-citationRoFormer: Enhanced transformer with rotary position embedding
Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., & Liu, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2023.127063 · Cited by 1,957 · see citing works
<i>TERT</i> Promoter Mutations in Familial and Sporadic Melanoma
Horn, S., Figl, A., Rachakonda, P. S., Fischer, C., Sucker, A., Gast, A., Kadel, S., Moll, I., Nagore, E., Hemminki, K., et al. (2013) · Science · doi:10.1126/science.1230062 · Cited by 1,799 · see citing works
Plasma proteomic associations with genetics and health in the UK Biobank
Sun, B. B., Chiou, J., Traylor, M., Benner, C., Hsu, Y.-H., Richardson, T. G., Surendran, P., Mahajan, A., Robins, C., Vasquez-Grinnell, S. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06592-6 · Cited by 1,740 · see citing works
De novo assembly of bacterial transcriptomes from RNA-seq data
Tjaden, B. C. (2015) · Genome biology · doi:10.1186/s13059-014-0572-2 · Cited by 1,695 · see citing works
Computationally efficient whole-genome regression for quantitative and binary traits
Mbatchou, J., Barnard, L., Backman, J., Marcketta, A., Kosmicki, J. A., Ziyatdinov, A., Benner, C., O’Dushlaine, C., Barber, M., Boutkov, B., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00870-7 · Cited by 1,502 · see citing works
Effective gene expression prediction from sequence by integrating long-range interactions
Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021) · Nature Methods · doi:10.1038/s41592-021-01252-x · Cited by 1,326 · see citing works
Self-citationAlternative polyadenylation of mRNA precursors
Tian, B., & Manley, J. L. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2016.116 · Cited by 1,179 · see citing works
A simple new approach to variable selection in regression, with application to genetic fine mapping
Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M. (2020) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12388 · Cited by 1,121 · see citing works
Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations
Fulco, C. P., Nasser, J., Jones, T. R., Munson, G., Bergman, D. T., Subramanian, V., Grossman, S. R., Anyoha, R., Doughty, B. R., Patwardhan, T. A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0538-0 · Cited by 1,111 · see citing works
JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles
Rauluševičiūtė, I., Puig, R. R., Blanc‐Mathieu, R., Castro-Mondragon, J. A., Ferenc, K., Kumar, V., Lemma, R. B., Lucas, J., Chèneby, J., Baranas̆ić, D., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1059 · Cited by 1,110 · see citing works
Splicing regulation: From a parts list of regulatory elements to an integrated splicing code
Wang, Z., & Burge, C. B. (2008) · RNA · doi:10.1261/rna.876308 · Cited by 1,076 · see citing works
Kelley, D. R., Snoek, J., & Rinn, J. L. (2016) · Genome Research · doi:10.1101/gr.200535.115 · Cited by 1,048 · see citing works
The role of regulatory variation in complex traits and disease
Albert, F. W., & Kruglyak, L. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3891 · Cited by 1,047 · see citing works
Predictive Identification of Exonic Splicing Enhancers in Human Genes
Fairbrother, W. G., Yeh, R., Sharp, P. A., & Burge, C. B. (2002) · Science · doi:10.1126/science.1073774 · Cited by 1,020 · see citing works
The power of genetic diversity in genome-wide association studies of lipids
Graham, S. E., Clarke, S. L., Wu, K.-H. H., Kanoni, S., Zajac, G. J. M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T. W., et al. (2021) · Nature · doi:10.1038/s41586-021-04064-3 · Cited by 972 · see citing works
New developments on the Encyclopedia of DNA Elements (ENCODE) data portal
Luo, Y., Hitz, B. C., Gabdank, I., Hilton, J. A., Kagda, M. S., Lam, B. R., Myers, Z., Sud, P., Jou, J., Lin, K. Z., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1062 · Cited by 949 · see citing works
The genomic landscape of pediatric and young adult T-lineage acute lymphoblastic leukemia
Liu, Y., Easton, J., Shao, Y., Maciaszek, J., Wang, Z., Wilkinson, M. R., McCastlain, K., Edmonson, M., Pounds, S. B., Shi, L., et al. (2017) · Nature Genetics · doi:10.1038/ng.3909 · Cited by 918 · see citing works
scMC learns biological variation through the alignment of multiple single-cell genomics datasets
Zhang, L., & Nie, Q. H. (2021) · Genome biology · doi:10.1186/s13059-020-02238-2 · Cited by 848 · see citing works
An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element
Mansour, M. R., Abraham, B. J., Anders, L., Berezovskaya, A., Gutierrez, A., Durbin, A. D., Etchin, J., Lawton, L., Sallan, S. E., Silverman, L. B., et al. (2014) · Science · doi:10.1126/science.1259037 · Cited by 801 · see citing works
Uberon, an integrative multi-species anatomy ontology
Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A. (2012) · Genome biology · doi:10.1186/gb-2012-13-1-r5 · Cited by 732 · see citing works
Genome-wide enhancer maps link risk variants to disease genes
Nasser, J., Bergman, D. T., Fulco, C. P., Guckelberger, P., Doughty, B. R., Patwardhan, T. A., Jones, T. R., Nguyen, T. H., Ulirsch, J. C., Lekschas, F., et al. (2021) · Nature · doi:10.1038/s41586-021-03446-x · Cited by 669 · see citing works
Nucleotide Transformer: building and evaluating robust foundation models for human genomics
Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., Lopez Carranza, N., Grzywaczewski, A. H., Oteri, F., Dallago, C., Trop, E., de Almeida, B. P., Sirelkhatim, H., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02523-z · Cited by 656 · see citing works
Base-resolution models of transcription-factor binding reveal soft motif syntax
Avsec, Ž., Weilert, M., Shrikumar, A., Krueger, S., Alexandari, A., Dalal, K., Fropf, R., McAnany, C., Gagneur, J., Kundaje, A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00782-6 · Cited by 610 · see citing works
Self-citationMountjoy, E., Schmidt, E. M., Carmona, M., Schwartzentruber, J., Peat, G., Miranda, A., Fumis, L., Hayhurst, J., Buniello, A., Karim, M. A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00945-5 · Cited by 581 · 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
Sequential regulatory activity prediction across chromosomes with convolutional neural networks
Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research · doi:10.1101/gr.227819.117 · Cited by 537 · see citing works
Open Targets: a platform for therapeutic target identification and validation
Koscielny, G., An, P., Carvalho‐Silva, D., Cham, J. A., Fumis, L., Gasparyan, R., Hasan, S., Karamanis, N., Maguire, M., Papa, E., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1055 · Cited by 500 · see citing works
Trevino, A. E., Müller, F., Andersen, J., Sundaram, L., Kathiria, A., Shcherbina, A., Farh, K., Chang, H. Y., Pașca, A. M., Kundaje, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.039 · Cited by 478 · see citing works
The sequences of 150,119 genomes in the UK Biobank
Halldorsson, B. V., Eggertsson, H. P., Moore, K. H. S., Hauswedell, H., Eiriksson, O., Ulfarsson, M. O., Palsson, G., Hardarson, M. T., Oddsson, A., Jensson, B. O., et al. (2022) · Nature · doi:10.1038/s41586-022-04965-x · Cited by 448 · see citing works
A compendium of uniformly processed human gene expression and splicing quantitative trait loci
Kerimov, N., Hayhurst, J. D., Peikova, K., Manning, J. R., Walter, P., Kolberg, L., Samoviča, M., Sakthivel, M. P., Kuzmin, I., Trevanion, S. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00924-w · Cited by 440 · see citing works
Are splicing mutations the most frequent cause of hereditary disease?
López-Bigas, N., Audit, B., Ouzounis, C., Parra, G., & Guigó, R. (2005) · FEBS Letters · doi:10.1016/j.febslet.2005.02.047 · Cited by 412 · see citing works
UniPROBE: an online database of protein binding microarray data on protein-DNA interactions
Newburger, D. E., & Bulyk, M. L. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn660 · Cited by 367 · see citing works
The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability
Diehl, A. D., Meehan, T. F., Bradford, Y. M., Brush, M., Dahdul, W., Dougall, D. S., He, Y. O., Osumi-Sutherland, D., Ruttenberg, A., Sarntivijai, S., et al. (2016) · Journal of Biomedical Semantics · doi:10.1186/s13326-016-0088-7 · Cited by 332 · see citing works
Predicting 3D genome folding from DNA sequence with Akita
Fudenberg, G., Kelley, D. R., & Pollard, K. S. (2020) · Nature Methods · doi:10.1038/s41592-020-0958-x · Cited by 292 · see citing works
Predicting RNA splicing from DNA sequence using Pangolin
Zeng, T., & Li, Y. I. (2022) · Genome Biology · doi:10.1186/s13059-022-02664-4 · Cited by 283 · see citing works
Positional cloning of a novel gene on chromosome 16q causing Bardet-Biedl syndrome (BBS2)
Nishimura, D. Y. (2001) · Human Molecular Genetics · doi:10.1093/hmg/10.8.865 · Cited by 271 · see citing works
A sequence-based global map of regulatory activity for deciphering human genetics
Chen, K. M., Wong, A. K., Troyanskaya, O. G., & Zhou, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01102-2 · Cited by 269 · see citing works
Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation
Linder, J., Srivastava, D., Yuan, H., Agarwal, V., & Kelley, D. R. (2025) · Nature Genetics · doi:10.1038/s41588-024-02053-6 · Cited by 265 · see citing works
Spectrum of Mutations in Gitelman Syndrome
Vargas‐Poussou, R., Dahan, K., Kahila, D., Vénisse, A., Riveira‐Muñoz, E., Debaix, H., Grisart, B., Bridoux, F., Unwin, R. J., Moulin, B., et al. (2011) · Journal of the American Society of Nephrology · doi:10.1681/asn.2010090907 · Cited by 241 · see citing works
Wang, R., Nambiar, R., Zheng, D., & Tian, B. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1000 · Cited by 239 · see citing works
Kircher, M., Xiong, C., Martin, B., Schubach, M., Inoue, F., Bell, R. J. A., Costello, J. F., Shendure, J., & Ahituv, N. (2019) · Nature Communications · doi:10.1038/s41467-019-11526-w · Cited by 234 · see citing works
Genome modeling and design across all domains of life with Evo 2Preprint
Brixi, G., Durrant, M. G., Ku, J., Poli, M., Brockman, G., Chang, D., Gonzalez, G. A., King, S. B., Li, D. B., Merchant, A. T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.18.638918 · Cited by 218 · see citing works
Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale
Zhou, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01065-4 · Cited by 177 · see citing works
Cell-type-directed design of synthetic enhancers
Taskiran, I. I., Spanier, K. I., Dickmänken, H., Kempynck, N., Pančíková, A., Ekşi, E. C., Hulselmans, G., Ismail, J. N., Theunis, K., Vandepoel, R., et al. (2024) · Nature · doi:10.1038/s41586-023-06936-2 · Cited by 175 · see citing works
The 4D Nucleome Data Portal as a resource for searching and visualizing curated nucleomics data
Reiff, S. B., Schroeder, A. J., Kırlı, K., Cosolo, A., Bakker, C., Mercado, L., Lee, S., Veit, A. D., Balashov, A. K., Vitzthum, C. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29697-4 · Cited by 173 · see citing works
Detection of aberrant gene expression events in RNA sequencing data
Yépez, V. A., Mertes, C., Müller, M., Klaproth-Andrade, D., Wachutka, L., Frésard, L., Gušić, M., Scheller, I. F., Goldberg, P. F., Prokisch, H., et al. (2021) · Nature Protocols · doi:10.1038/s41596-020-00462-5 · Cited by 167 · see citing works
Pampari, A., Shcherbina, A., Kvon, E. Z., Kosicki, M., Nair, S., Kundu, S., Kathiria, A. S., Risca, V. I., Kuningas, K., Alasoo, K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.25.630221 · Cited by 161 · see citing works
A harmonized public resource of deeply sequenced diverse human genomesPreprint
Koenig, Z., Yohannes, M. T., Nkambule, L. L., Zhao, X., Goodrich, J. K., Kim, H. A., Wilson, M. W., Tiao, G., Hao, S. P., Sahakian, N., et al. (2024) · Genome Research · doi:10.1101/gr.278378.123 · Cited by 160 · see citing works
DNA language models are powerful predictors of genome-wide variant effects
Benegas, G., Batra, S. S., & Song, Y. S. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311219120 · Cited by 157 · see citing works
An encyclopedia of human enhancer–gene regulatory interactions
Gschwind, A. R., Mualim, K. S., Karbalayghareh, A., Sheth, M. U., Dey, K. K., Jagoda, E., Nurtdinov, R., Xi, W., Tan, A. S., Galante, J., et al. (2026) · Nature · doi:10.1038/s41586-026-10781-4 · Cited by 148 · see citing works
Self-citationQuantifying the regulatory effect size of cis -acting genetic variation using allelic fold changePreprint
Mohammadi, P., Castel, S. E., Brown, A. A., & Lappalainen, T. (2017) · Genome Research · doi:10.1101/gr.216747.116 · Cited by 146 · see citing works
WNT7B Promotes Bone Formation in part through mTORC1
Chen, J., Tu, X., Esen, E., Joeng, K. S., Lin, C., Arbeit, J. M., Rüegg, M. A., Hall, M. N., Ma, L., & Long, F. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004145 · Cited by 142 · see citing works
Sasse, A., Ng, B., Spiro, A. E., Tasaki, S., Bennett, D. A., Gaiteri, C., De Jager, P. L., Chikina, M., & Mostafavi, S. (2023) · Nature Genetics · doi:10.1038/s41588-023-01524-6 · Cited by 139 · see citing works
Chong, R., Insigne, K. D., Yao, D., Burghard, C. P., Wang, J., Hsiao, Y.-H. E., Jones, E. M., Goodman, D. B., Xiao, X., & Kosuri, S. (2019) · Molecular Cell · doi:10.1016/j.molcel.2018.10.037 · Cited by 130 · see citing works
Karollus, A., Mauermeier, T., & Gagneur, J. (2023) · Genome Biology · doi:10.1186/s13059-023-02899-9 · Cited by 128 · see citing works
Self-citationSarntivijai, 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
Personal transcriptome variation is poorly explained by current genomic deep learning models
Huang, C., Shuai, R. W., Baokar, P., Chung, R., Rastogi, R., Kathail, P., & Ioannidis, N. M. (2023) · Nature Genetics · doi:10.1038/s41588-023-01574-w · Cited by 127 · see citing works
Pooled ChIP-Seq Links Variation in Transcription Factor Binding to Complex Disease Risk
Tehranchi, A. K., Myrthil, M., Martin, T., Hie, B. L., Golan, D., & Fraser, H. B. (2016) · Cell · doi:10.1016/j.cell.2016.03.041 · Cited by 120 · see citing works
ASCOT identifies key regulators of neuronal subtype-specific splicing
Ling, J. P., Wilks, C., Charles, R., Leavey, P. J., Ghosh, D., Jiang, L., Santiago, C. P., Pang, B., Venkataraman, A., Clark, B. S., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14020-5 · Cited by 107 · see citing works
Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep NetworksPreprint
De, S., & Smith, S. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10444 · Cited by 97 · see citing works
Self-citationAberrant splicing prediction across human tissues
Wagner, N., Çelik, M. H., Hölzlwimmer, F. R., Mertes, C., Prokisch, H., Yépez, V. A., & Gagneur, J. (2023) · Nature Genetics · doi:10.1038/s41588-023-01373-3 · Cited by 90 · see citing works
Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo
de Almeida, B. P., Schaub, C., Pagani, M., Secchia, S., Furlong, E. E. M., & Stark, A. (2024) · Nature · doi:10.1038/s41586-023-06905-9 · Cited by 87 · see citing works
The spectrum of beta-thalassemia genes in China and Southeast Asia
Kazazian, H. J., Dowling, C., Waber, P. G., Huang, S. S.-L., & Wh, L. (1986) · Blood · doi:10.1182/blood.v68.4.964.964 · Cited by 84 · see citing works
Kaneko, K., Furuyama, K., Fujiwara, T., Kobayashi, R., Ishida, H., Harigae, H., & Shibahara, S. (2013) · Haematologica · doi:10.3324/haematol.2013.085449 · Cited by 82 · see citing works
Multiscale footprints reveal the organization of cis-regulatory elements
Yan, H., Horlbeck, M. A., Zhang, R., Ma, S., Shrestha, R., Kartha, V. K., Duarte, F. M., Hock, C., Savage, R. E., Labade, A. S., et al. (2025) · Nature · doi:10.1038/s41586-024-08443-4 · Cited by 80 · see citing works
Yuregir, G., Aksoy, K., Çürük, M. A., Dikmen, N., Fei, Y. J., BAYSAL, E., & Huisman, T. H. J. (1992) · British Journal of Haematology · doi:10.1111/j.1365-2141.1992.tb04568.x · Cited by 72 · see citing works
Bolduc, V., Foley, A. R., Solomon-Degefa, H., Sarathy, A., Donkervoort, S. C., Hu, Y., Chen, G. S., Sizov, K., Nalls, M., Zhou, H., et al. (2019) · JCI Insight · doi:10.1172/jci.insight.124403 · Cited by 64 · see citing works
Discovery of regulatory noncoding variants in individual cancer genomes by using cis-X
Liu, Y., Li, C., Shen, S., Chen, X., Szlachta, K., Edmonson, M. N., Shao, Y., Ma, X., Hyle, J., Wright, S., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0659-5 · Cited by 64 · see citing works
Genetic effects on promoter usage are highly context-specific and contribute to complex traits
Alasoo, K., Rodrigues, J., Danesh, J., Freitag, D. F., Paul, D. S., & Gaffney, D. J. (2019) · eLife · doi:10.7554/elife.41673 · Cited by 63 · see citing works
Kerimov, N., Tambets, R., Hayhurst, J. D., Rahu, I., Kolberg, P., Raudvere, U., Kuzmin, I., Chowdhary, A., Vija, A., Teras, H., et al. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010932 · Cited by 60 · see citing works
Species-aware DNA language models capture regulatory elements and their evolution
Karollus, A., Hingerl, J., Gankin, D., Grosshauser, M., Klemon, K., & Gagneur, J. (2024) · Genome biology · doi:10.1186/s13059-024-03221-x · Cited by 57 · see citing works
Self-citationShigaki, D., Adato, O., Adhikari, A. N., Dong, S., Hawkins‐Hooker, A., Inoue, F., Juven‐Gershon, T., Kenlay, H., Martin, B., Patra, A., et al. (2019) · Human Mutation · doi:10.1002/humu.23797 · Cited by 54 · see citing works
Improved detection of aberrant splicing with FRASER 2.0 and the intron Jaccard index
Scheller, I. F., Lutz, K., Mertes, C., Yépez, V. A., & Gagneur, J. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.10.014 · Cited by 54 · see citing works
Deciphering the impact of genetic variation on human polyadenylation using APARENT2
Linder, J., Koplik, S. E., Kundaje, A., & Seelig, G. (2022) · Genome Biology · doi:10.1186/s13059-022-02799-4 · Cited by 49 · see citing works
Matsunaga, A., Sasaki, J., Han, H., Huang, W., Kugi, M., Koga, T., Ichiki, S., Shinkawa, T., & Arakawa, K. (1999) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/01.atv.19.2.348 · Cited by 45 · see citing works
Gillis, E., Kempers, M. J. E., Salemink, S., Timmermans, J., Cheriex, E. C., Bekkers, S. C. A. M., Fransén, E., de Die-Smulders, C. E. M., Loeys, B., & Laer, L. V. (2014) · Human Mutation · doi:10.1002/humu.22540 · Cited by 44 · see citing works
Collins, F. S., Boehm, C., Waber, P. G., Cj, S., Weissman, S., Forget, B. G., & Kazazian, H. J. (1984) · Blood · doi:10.1182/blood.v64.6.1292.1292 · Cited by 38 · see citing works
scooby: modeling multimodal genomic profiles from DNA sequence at single-cell resolution
Hingerl, J., Martens, L. D., Karollus, A., Manz, T., Buenrostro, J. D., Theis, F. J., & Gagneur, J. (2025) · Nature Methods · doi:10.1038/s41592-025-02854-5 · Cited by 37 · see citing works
Self-citationQuantifying splice-site usage: a simple yet powerful approach to analyze splicing
Dent, C., Singh, S., Mukherjee, S., Mishra, S., Sarwade, R. D., Shamaya, N., Loo, K. P., Harrison, P. F., Sureshkumar, S., Powell, D., et al. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab041 · Cited by 34 · see citing works
Heibel, S. K., Mew, N. A., Caldovic, L., Daikhin, Y., Yudkoff, M., & Tuchman, M. (2011) · Human Mutation · doi:10.1002/humu.21553 · Cited by 32 · see citing works
Dissecting the cis-regulatory syntax of transcription initiation with deep learningPreprint
Cochran, K., Yin, M., Mantripragada, A., Schreiber, J. M., Marinov, G. K., Shah, S. R., Yu, H., Lis, J. T., & Kundaje, A. B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596138 · Cited by 32 · see citing works
TAL1 activation in T-cell acute lymphoblastic leukemia: a novel oncogenic 3’ neo-enhancer
Smith, C., Goyal, A., Weichenhan, D., Allemand, E., Mayakonda, A., Toprak, U., Riedel, A., Balducci, E., Manojkumar, M., Pejkovska, A., et al. (2023) · Haematologica · doi:10.3324/haematol.2022.281583 · Cited by 30 · see citing works
Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human GeneticsPreprint
Benegas, G., Eraslan, G., & Song, Y. S. (2025) · bioRxiv · doi:10.1101/2025.02.11.637758 · Cited by 29 · see citing works
An RNA foundation model enables discovery of disease mechanisms and candidate therapeuticsPreprint
Celaj, A., Gao, A. J., Lau, T., Holgersen, E. M., Lo, A., Lodaya, V., Cole, C. B., Denroche, R. E., Spickett, C., Wagih, O., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.20.558508 · Cited by 29 · see citing works
Splam: a deep-learning-based splice site predictor that improves spliced alignments
Chao, K.-H., Mao, A., Salzberg, S. L., & Pertea, M. (2024) · Genome biology · doi:10.1186/s13059-024-03379-4 · Cited by 27 · see citing works
Decoding sequence determinants of gene expression in diverse cellular and disease states
Lal, A., Karollus, A., Gunsalus, L. M., Garfield, D., Nair, S., Tseng, A. M., Gordon, M. G., Blischak, J., Geijn, B. V. D., Bhangale, T., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03102-0 · Cited by 24 · see citing works
Self-citationATP7B variant c.1934T > G p.Met645Arg causes Wilson disease by promoting exon 6 skipping
Merico, D., Spickett, C., O’Hara, M., Kakaradov, B., Deshwar, A. G., Fradkin, P., Gandhi, S., Gao, J., Grant, S., Kron, K. J., et al. (2020) · npj Genomic Medicine · doi:10.1038/s41525-020-0123-6 · Cited by 22 · see citing works
Reference-informed prediction of alternative splicing and splicing-altering mutations from sequencesPreprint
Xu, C., Bao, S., Wang, Y., Li, W., Chen, H., Shen, Y., Jiang, T., & Zhang, C. (2024) · Genome Research · doi:10.1101/gr.279044.124 · Cited by 15 · see citing works
Uncertainty-aware genomic deep learning with knowledge distillation
Zhou, J., Rizzo, K., Christensen, T. A., Tang, Z., & Koo, P. K. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00053-3 · Cited by 14 · see citing works
Alternative Splicing and Disease
Jeanteur, P. (2006) · Progress in molecular and subcellular biology · doi:10.1007/978-3-540-34449-0 · Cited by 9 · see citing works
Parameter-Efficient Fine-Tuning of a Supervised Regulatory Sequence ModelPreprint
Yuan, H., Linder, J., & Kelley, D. R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.26.656171 · Cited by 8 · see citing works
Finucane, H. K., Parsa, S., Guez, J., Kanai, M., Satterstrom, F. K., Nkambule, L. L., Daly, M. J., Seed, C., & Karczewski, K. J. (2024) · doi:10.1101/2024.09.17.613327 · Cited by 4 · see citing works
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An encyclopedia of human enhancer–gene regulatory interactions
Gschwind, A. R., Mualim, K. S., Karbalayghareh, A., Sheth, M. U., Dey, K. K., Jagoda, E., Nurtdinov, R., Xi, W., Tan, A. S., Galante, J., et al. (2026) · Nature · doi:10.1038/s41586-026-10781-4 · Cited by 148 · see citing works
Self-citationBenchmarking DNA foundation models for genomic and genetic tasks
Feng, H., Wu, L., Zhao, B., Huff, C. D., Zhang, J. J., Wu, J., Lin, L., Wei, P., & Wu, C. (2025) · Nature Communications · doi:10.1038/s41467-025-65823-8 · Cited by 64 · see citing works
Advancing bioinformatics with language models: components, applications, and perspectives
Liu, J., Yang, M., Yu, Y., Xu, H., Wang, T., Li, K., & Zhou, X. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag367 · Cited by 52 · see citing works
Somatic epimutations cap genetic determinism in the human diploid chromatin epigenome
Vollger, M. R., Swanson, E. G., Neph, S. J., Mallory, B. J., Dubocanin, D., Ho, C., Cheng, Y.-H. H., Ranchalis, J., Munson, K. M., Sedeño-Cortés, A. E., et al. (2026) · Cell · doi:10.1016/j.cell.2026.09.034 · Cited by 37 · see citing works
Harnessing artificial intelligence to advance CRISPR-based genome editing technologies
Thomson, T., Li, G. L., Strilchuk, A. W., Cui, H., Wang, B., & Li, B. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00907-1 · Cited by 32 · see citing works
Rare and common genetic variants shape single-cell immune gene expression
Cuomo, A. S. E., Spenceley, E., Tanudisastro, H. A., Bowen, B., Henry, A., Huang, H. L., Xue, A., Zhou, W., Welland, M. J., Bryen, S. J., et al. (2026) · Nature · doi:10.1101/2025.03.20.25324352 · Cited by 30 · see citing works
Reimagining research papers as interactive and reliable AI agents
Miao, J., Davis, J. R., Zhang, Y., Pritchard, J. K., & Zou, J. (2026) · Nature · doi:10.1038/s41586-026-11044-y · Cited by 29 · see citing works
Genetic analysis of circulating metabolic traits in 619,372 individuals
Tambets, R., Jesse, M., Kronberg, J., van der Graaf, A., Abner, E., Võsa, U., Rahu, I., Taba, N., Kolde, A., Yarish, D., et al. (2026) · Nature · doi:10.1038/s41586-026-10532-5 · Cited by 26 · see citing works
A multispecies foundation model for genome function prediction, gene annotation and sequence designPreprint
Boshar, S., Evans, B., Tang, Z., Adel, Y., Bornachot, Y., Sidbon, S., Lorbeer, F. K., Picard, A., Rajesh, C., Seitz, E., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.22.695963 · Cited by 26 · see citing works
CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species
Kempynck, N., Winter, S. D., Blaauw, C. H., Konstantakos, V., Ekşi, E. C., Dieltiens, S., Abaffyová, D., Bercier, V., Taskiran, I. I., Hulselmans, G. J., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03057-2 · Cited by 24 · see citing works
Predicting genome-wide functional constraints with GPN-Star
Ye, C., Benegas, G., Albors, C., Li, J. C., Prillo, S., Fields, P. D., Clarke, B., & Song, Y. S. (2026) · Nature · doi:10.1038/s41586-026-11005-5 · Cited by 22 · see citing works
Park, K., & Jeon, M. (2025) · Pharmaceuticals · doi:10.3390/ph18121793 · Cited by 20 · see citing works
Fine-tuning sequence-to-expression models on personal genome and transcriptome data
Rastogi, R., Reddy, A. J., Chung, R., & Ioannidis, N. M. (2026) · Genome biology · doi:10.1186/s13059-026-04091-1 · Cited by 19 · see citing works
Chromatin-dependent motif syntax defines differentiation trajectories
Durdu, S., Iskar, M., Isbel, L., Hoerner, L., Wirbelauer, C., Burger, L., Heß, D., Iešmantavičius, V., & Schübeler, D. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.07.005 · Cited by 17 · see citing works
Computational frameworks for enhanced extracellular vesicle biomarker discovery
Kim, J., Yang, J. D., Agopian, V. G., Zhu, Y., Tseng, H., & You, S. (2026) · Experimental & Molecular Medicine · doi:10.1038/s12276-025-01622-x · Cited by 16 · see citing works
Genos: a human-centric genomic foundation model
Lin, A., Xie, B., Cheng, Y., Wang, C., Chen, D., Wang, E., Lü, F., Xue, G.-R., Zhang, H., Zhan, J., et al. (2025) · GigaScience · doi:10.1093/gigascience/giaf132 · Cited by 14 · see citing works
Convergence of machine learning and genomics for precision oncology
Reardon, B. M., Culhane, A. C., & Allen, E. M. V. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-025-00897-6 · Cited by 14 · see citing works
HydraRNA: a hybrid architecture based full-length RNA language model
Li, G., Jiang, F., Zhu, J., Cui, H., Wang, Z., & Chen, W. (2025) · Genome Biology · doi:10.1186/s13059-025-03853-7 · Cited by 10 · see citing works
Quantum Computing for Genomics: Conceptual Challenges and Practical Perspectives
Maurizio, A., & Mazzola, G. (2025) · PRX Life · doi:10.1103/h49j-bsc6 · Cited by 10 · see citing works
Pakpahan, I., Sihombing, M., Liu, H., Wang, M., Su, Z., & Fang, M. (2026) · GigaScience · doi:10.1093/gigascience/giag004 · Cited by 10 · see citing works
The DNA dialect: a comprehensive guide to pretrained genomic language models
Veiner, M., & Supek, F. (2026) · Molecular Systems Biology · doi:10.1038/s44320-025-00184-4 · Cited by 9 · see citing works
The genetic foundations of convergent traits
Allard, J. B., & Kumar, S. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00933-7 · Cited by 9 · see citing works
Omics-driven plant breeding through phenomics-enviromics crosstalk
Li, H., Gao, S., Gebrewahid, T. W., Li, W., Wang, J., Han, Z., Reynolds, M. P., Araus, J. L., Hearne, S., & Xu, Y. (2026) · Nature Communications · doi:10.1038/s41467-026-73097-x · Cited by 9 · see citing works
Decoding common and rare noncoding variant effects across cellular and developmental contexts
Marderstein, A. R., Kundu, S., Padhi, E. M., Deshpande, S., Wang, A., Robb, E., Sun, Y., Yun, C. M., Pomales-Matos, D. A., Xie, Y., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02619-6 · Cited by 9 · see citing works
Towards the construction of a virtual yeast
Qian, L., Zhou, Z., Zhou, P., Dong, Z., Zhang, X., Dai, Z., Gao, Z., Sun, S., Roy, K. R., Wang, S., et al. (2026) · Nature · doi:10.1038/s41586-026-10574-9 · Cited by 10 · see citing works
Richter, M., & Admasu, A. S. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146795 · Cited by 9 · see citing works
A multi-tissue atlas of genetic regulatory effects in sheepPreprint
Gong, M., Zhuang, Z., Sun, X., Xu, Y., Zhang, H.-H., Wang, Y., Guan, D., Li, R., Lu, X., Bai, Z., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.03.686346 · Cited by 9 · see citing works
Sequencing approaches in hereditary cancer testing: strengths, limitations and future directions
Belhadj, S., Hatch, C. J., LaDuca, H., Horton, C., Berger, S. I., & Karam, R. (2026) · European Journal of Human Genetics · doi:10.1038/s41431-026-02078-x · Cited by 8 · see citing works
Tools and tactics for studying alternative splicing
Sousa‐Luís, R., & Carmo‐Fonseca, M. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00952-4 · Cited by 8 · see citing works
Flexible use of conserved motifs constrains genome access in cell type evolution
Chai, C., Gibson, J., Li, P., McDonald, B. D., Pampari, A., Patel, A., Kundaje, A. B., & Wang, B. (2026) · Nature Ecology & Evolution · doi:10.1038/s41559-026-03164-5 · Cited by 8 · see citing works
Translating functional molecular knowledge into crop-breeding success
Ramstein, G. P., Zhai, J., Buckler, E. S., Tenaillon, M. I., & Jannink, J. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00968-w · Cited by 7 · see citing works
Large language models for biological sequence analysis in infectious disease research
Luo, J., Cai, X., & Li, Y. (2025) · Biosafety and Health · doi:10.1016/j.bsheal.2025.09.007 · Cited by 6 · see citing works
Lee, J., Kim, D. H., Choi, J.-W., Shong, M., & Lee, C. K. (2025) · Journal of Inflammation Research · doi:10.2147/jir.s546260 · Cited by 6 · see citing works
Genomic language model-based genomic prediction in plant breeding
Alagarasan, G., Li, H., Xu, Y., Crossa, J. L., & Varshney, R. K. (2026) · Trends in Plant Science · doi:10.1016/j.tplants.2026.04.012 · Cited by 6 · see citing works
PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes
Li, C., Zhang, Q., Chen, H., Lin, K., Luo, C., Yang, M., Xu, W., Yao, F., Yan, J., Yang, Q., et al. (2026) · Advanced Science · doi:10.1002/advs.75772 · Cited by 6 · see citing works
Toward generalizable and interpretable AI in regulatory genomics
NAGAI, M., Murphy, A. E., Rizzo, K., & Koo, P. K. (2026) · Nature Genetics · doi:10.1038/s41588-026-02670-3 · Cited by 6 · see citing works
del Río, J. S., Talavera, A., Fernández‐Pozo, N., Veredas, F. J., & Claros, M. G. (2025) · Journal of Experimental Botany · doi:10.1093/jxb/eraf498 · Cited by 6 · see citing works
The Last Decade of Medicinal Plant Genomics: Advances and Challenges
Chen, J., Zhang, Y., Li, Y., Liu, Y., Li, Q. X., Lv, Z., Georgiev, M. I., & Liao, P. (2025) · Engineering · doi:10.1016/j.eng.2025.09.024 · Cited by 5 · see citing works
Liu, K., Luo, Z., Zhang, W., Pan, Q., Su, X., Yang, Z., Zhang, Q., Wang, B., Tang, B., he, Z., et al. (2026) · Molecular Cancer · doi:10.1186/s12943-026-02589-7 · Cited by 5 · see citing works
Toward life with a 19–amino acid alphabet through generative artificial intelligence design
Liu, L., Rochereau, C., Kozlov, S., Urtecho, G., Liu, X., Zhao, J., Wang, J., Huang, Y., Qu, Y., Zhang, Z., et al. (2026) · Science · doi:10.1126/science.aeb5171 · Cited by 5 · see citing works
Abriata, L. A. (2026) · Communications Biology · doi:10.1038/s42003-026-10112-3 · Cited by 5 · see citing works
Artificial intelligence tools for enzyme engineering and metabolic engineering
Volk, M., Pillalamarri, V. K., Guo, R., Liu, G., & Zhao, H. (2026) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2026.103522 · Cited by 5 · see citing works
McAnany, C. E., Weilert, M., Mehta, G., Kamulegeya, F., Gardner, J., Schreiber, J. M., Kundaje, A. B., & Zeitlinger, J. (2026) · Nature Communications · doi:10.1038/s41467-026-74807-1 · Cited by 5 · see citing works
Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflowPreprint
Rosen, J. D., Vasanthakumari, A. D., Salomon, K., de Lange, N., Dash, P. M., Keukeleire, P., Hassan, A., Barrera, A., Krupkin, B., Oualline, G., et al. (2026) · Genome Research · doi:10.1101/gr.281462.125 · Cited by 5 · see citing works
Designing AI-programmable therapeutics with the EDEN family of foundation modelsPreprint
Munsamy, G., Ayres, G., Greco, C., Kam, K. Y. K., Minto-Cowcher, G., John, J. S., Bohnuud, T., Bakalar, M. H., Chow, W., Pecoraro, R. J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.12.699009 · Cited by 5 · see citing works
Zambon, M., Mantica, F., Dias, M., Frazer, J., & Irimia, M. (2025) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2025.102387 · Cited by 4 · see citing works
Mitsumori, R., Ozaki, K., Saito, Y., Shigemizu, D., Iwata, A., Murayama, S., Akishita, M., Arai, T., Niida, S., & Toba, K. (2025) · Journal of Human Genetics · doi:10.1038/s10038-025-01438-7 · Cited by 4 · see citing works
Sharing approaches in predictive genomics across animals, plants and humans
Arirangan, S., de Oliveira, L. F., Hasan, M. N., Sherman, A. B., Tuinstra, M. R., Brito, L. F., Wedow, R., & Tegtmeyer, M. (2026) · Nature Genetics · doi:10.1038/s41588-025-02491-w · Cited by 4 · see citing works
Computational tools for tandem repeat detection using long-read sequencing
Liu, Q., & Li, J. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag031 · Cited by 4 · see citing works
Artificial Intelligence in Transcriptomics: From Human-in-the-Loop to Agentic AI
Gentile, G., Morello, G., Cognata, V. L., Guarnaccia, M. C., & Cavallaro, S. (2026) · Journal of Personalized Medicine · doi:10.3390/jpm16040181 · Cited by 4 · see citing works
PLANeT: Understanding and leveraging the genome of land plants for a sustainable future
Wang, L., Amar, M., Arbizu, C. I., Bowman, J. L., Cannon, C. H., Cantrill, D. J., Cao, X., Chen, S., Chen, X., Chen, X., et al. (2026) · Cell · doi:10.1016/j.cell.2026.01.026 · Cited by 4 · see citing works
Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches
Chasapi, M. N., Kontis, N., Lehmann, R., Tasneem, R., Patel, N. S., Khan, S. A., de Morentin, X. M., Chasapi, I. N., Aplakidou, E., Γαλάρας, Α., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag236 · Cited by 4 · see citing works
Advances and challenges of splicing prediction with AI
Shen, N., You, N., & Liu, C. (2026) · Nature Genetics · doi:10.1038/s41588-026-02629-4 · Cited by 4 · see citing works
Rockenbach, K., Zanini, S. F., Tidy, A. C., Morris, R. J., Wells, R., & Golicz, A. A. (2026) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiag473 · Cited by 4 · see citing works
Generative Design of Cell Type-Specific RNA Splicing Elements for Programmable Gene RegulationPreprint
Chen, X. D., Jim, M., Vallurupalli, M., Cao, K., Torres, A. N., Leong, W., Zhang, Y., Wollensak, D., Gong, Q., Sun, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.05.686847 · Cited by 4 · see citing works
Winter, S. D., Mannens, C. C. A., Christiaens, V., Vandepoel, R., Hu, L., Linnarsson, S., & Aerts, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.14.699402 · Cited by 4 · see citing works
A modality gap in personal-genome prediction by sequence-to-function modelsPreprint
Mostafavi, S., Tu, X., Spiro, A. E., & Chikina, M. D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.01.702969 · Cited by 4 · see citing works
Genomic heterogeneity inflates the performance of variant pathogenicity predictions
Lu, B., Liu, X., Lin, P., & Brandes, N. (2026) · Nature Communications · doi:10.1101/2025.09.05.674459 · Cited by 4 · see citing works
“Those Evenings of the Brain – When Not a Moon Disclose a Sign”
Deary, I. J., Harris, S. E., Fürtjes, A. E., & Cox, S. R. (2026) · Intelligence & Cognitive Abilities · doi:10.65550/001c.154856 · Cited by 3 · see citing works
Bailey, J., Spina, S. C., Hu, A., Phan, N., Getman, R. B., & Kimmel, B. R. (2026) · ACS Engineering Au · doi:10.1021/acsengineeringau.5c00099 · Cited by 3 · see citing works
From foundation models to autonomous agents in biology
Huang, S., Lang, M., Chen, Z., Yang, C., Huang, X., Mohtashaminia, Z., & Peng, Y. (2026) · Genomics Communications · doi:10.48130/gcomm-0026-0005 · Cited by 3 · see citing works
Mechanisms of inflammatory bowel diseases: Insights from human genetics and chemical biology
Graham, D. B., & Xavier, R. J. (2026) · Immunity · doi:10.1016/j.immuni.2026.04.002 · Cited by 3 · see citing works
Decoding disease complexity: Multi-Omics integration and AI in precision medicine
Amini, H., & Wang, J. (2026) · The Journal of Precision Medicine Health and Disease · doi:10.1016/j.premed.2026.100047 · Cited by 3 · see citing works
Guo, C., Hua, D., Gan, S., Zhang, Y.-D., Liu, H., Ding, M., Cao, M., Wen, Q., Chen, Y., Lu, J., et al. (2026) · Molecular Biology and Evolution · doi:10.1093/molbev/msag190 · Cited by 3 · see citing works
Mechanistic machine learning for prediction of prime editing outcomes
Hsu, A., Chen, P. J., Li, A. H., Hemez, C. F., Gao, X. D., Terrey, M., Nelson, C., Selvam, V., Cristian, A., McElroy, A. N., et al. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03261-7 · Cited by 3 · see citing works
A practical guide to studying genome function using single-molecule genomics
Krebs, A. R., Altemose, N., Churchman, L. S., Greenleaf, W. J., Ramani, V., Stadler, M. B., & Stergachis, A. B. (2026) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-026-01017-4 · Cited by 3 · see citing works
Inferring binding specificities of human transcription factors with the wisdom of crowds
Gryzunov, N., Penzar, D., Kamenets, V., Vyaltsev, V., Kozin, I., Eliseeva, I. A., Nozdrin, V., Vorontsov, I. E., Bushuev, S., Strekalovskikh, V. V., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.118025 · Cited by 3 · see citing works
Predicting the regulatory genome
Tiwari, S., Karbalayghareh, A., & Leslie, C. S. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00887-2 · Cited by 3 · see citing works
3D genome sequencing technology in its mid-teens: past, present, and future
Chai, H., & Ruan, Y. (2025) · Trends in Genetics · doi:10.1016/j.tig.2025.07.010 · Cited by 3 · see citing works
Context-dependent regulatory variants in Alzheimer’s diseasePreprint
Chen, Z., Liu, Y., Brown, A. R., Sestili, H. H., Ramamurthy, E., Xiong, X., Prokopenko, D., Phan, B. N., Gadey, L., van de Weerd, R., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.11.659973 · Cited by 3 · see citing works
Aberrant splicing prediction during human organ developmentPreprint
Wagner, N., Неверов, А., Martin-Geary, A. C., Londhe, S., Schröder, C., Yépez, V. A., Whiffin, N., & Gagneur, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.16.665183 · Cited by 3 · see citing works
Ghosal, S., Barhomi, Y., Ganapathi, T., Krystosik, A., Krishnan, L., Guntury, S., Li, D., Initiative, A. D. N., Casale, F. P., & Karaletsos, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.31.685862 · Cited by 3 · see citing works
Matos, M. R., Ghatan, S., Bankier, S., Thompson, T. V., Lundy, K., Suzuki, M., Doña-Termine, R., Stauber, J., Reynolds, D. M., Rosales, K., et al. (2026) · medRxiv · doi:10.64898/2026.01.27.26344979 · Cited by 3 · see citing works
Sweeney, M. D., & Kang, H. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.04.703796 · Cited by 3 · see citing works
Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapesPreprint
Qiu, C., Daza, R. M., Welsh, I. C., Patwardhan, R. P., Martin, B. K., Li, T. W., Yang, S., Mannens, C. C. A., Winter, S. D., Kempynck, N., et al. (2026) · doi:10.62329/hxkk6249 · Cited by 3 · see citing works
OneGenome-Rice (OGR): A genomic foundation model for ricePreprint
Qian, B., Liang, C., Qin, C., Liu, C., Zhang, C., Xu, C., Li, D., Xue, G., He, H., Zhang, H., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.21.719822 · Cited by 3 · see citing works
Lee, C., Davenport, M. H., & Jarvis, E. D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.21.719679 · Cited by 3 · see citing works
OpenSplice: the impact of half a million mutations on the alternative splicing of 600 human exonsPreprint
Quarantani, G., Clarke, J., Thompson, M. J., Sang, F., Valcárcel, J., & Lehner, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.22.727141 · Cited by 3 · see citing works
A high-level programming language for generative biology with ProtoPreprint
Merchant, A. T., Guo, D., Viggiano, B., Brennan-Almaraz, L., Hur, E., Mai, T., Yin, P., King, S. H., Ashley, E. A., & Hie, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.22.733870 · Cited by 3 · see citing works
Drusinsky, S., & Pollard, K. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.08.750172 · Cited by 3 · see citing works
De-extinction and beyond: trait design powered by generative AI
Wang, S., Li, W., & Feng, G. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.09.015 · Cited by 2 · see citing works
Crop metabolic engineering towards enhanced resistance to pests and pathogens
Wang, J., Yang, J., Yu, J., Liu, Y., Wang, Y., El‐Demerdash, A., Zhou, W., & Wu, D. (2026) · The Crop Journal · doi:10.1016/j.cj.2026.01.001 · Cited by 2 · see citing works
Mapping the gene regulatory landscape of archaic hominin introgression in modern Papuans
Comerford, M., Vespasiani, D. M., Shukla, N., Cook, L. E., Yermakovich, D., Dannemann, M., Leavesley, M., Kinipi, C., Ricaut, F., Brucato, N., et al. (2026) · PLoS Genetics · doi:10.1371/journal.pgen.1012067 · Cited by 2 · see citing works
AI-driven discovery in protein science for immunology and infectious disease research
Helmy, M., Shafei, F. S. E., Pellegrina, D. V. D. S., Jin, L., Alhossary, A., Wilson, H. L., Mansour, T. A., & Selvarajoo, K. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1760257 · Cited by 2 · see citing works
Game of clones: decipher lineage plasticity in hormone-driven cancers
Leonita, A., Cheng, S., Warrick, J., Kim, I. Y., Deng, S., & Mu, P. (2026) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-026-06171-8 · Cited by 2 · see citing works
Yang, J. H., & Bashor, C. J. (2026) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2026.115883 · Cited by 2 · see citing works
Advances in genomics-driven genetic decoding and genomic design breeding in tomato
Ding, Z., Fei, Z., Huang, S., & Wu, Y. N. (2026) · Horticulture Research · doi:10.1093/hr/uhag172 · Cited by 2 · see citing works
Rainford, P. F., Occhipinti, A., Wang, B., Stepney, S., Angione, C., Verma, S., Marucci, L., Sharma, K., Harris, S. A., Füchslin, R. M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72667-3 · Cited by 2 · see citing works
Balatsky, V., Саєнко, А. М., & Peka, M. (2026) · BMC Genomics · doi:10.1186/s12864-026-12904-7 · Cited by 2 · see citing works
Liang, M., Li, C., Zhang, W.-P., & Wang, D. (2026) · Bioresource Technology · doi:10.1016/j.biortech.2026.134833 · Cited by 2 · see citing works
Zhou, Z., Wu, B., Zheng, X., Song, L., Zhang, S., Han, D., Liu, Z., & Gao, Y. (2026) · Nature Computational Science · doi:10.1038/s43588-026-00988-w · Cited by 2 · see citing works
DeGroat, W., & Kreimer, A. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag554 · Cited by 2 · see citing works
Explicit dynamic cross-strand interactions for DNA sequence language modelling
Yang, C., Liu, Y., Ling, L., Liu, Y., Li, F. H., Chen, C., Wang, L., Yu, F., Qiao, L., Zeng, X., et al. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01249-1 · Cited by 2 · see citing works
From DNA to drug discovery AI models for cardiovascular precision medicine
Kodera, S., & Takeda, N. (2026) · Journal of Cardiology · doi:10.1016/j.jjcc.2026.06.007 · Cited by 3 · see citing works
Genomic advances in orphan and underutilized Brassicaceae crops and their wild relatives
Amas, J. C., Natarajan, R. K., Thomas, W. J. W., Edwards, D., Batley, J., & Dolatabadian, A. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1878836 · Cited by 2 · see citing works
Near-perfect genome sequencing in medical genetics
Sabbagh, Q., Gilissen, C., Yntema, H. G., Vissers, L. E. L. M., & Hoischen, A. (2026) · Nature Genetics · doi:10.1038/s41588-026-02645-4 · Cited by 2 · see citing works
Hwang, S., Lee, M. J., Gong, J., Park, I. G., Kim, M., Cho, J., Kang, J., Kim, U., Lee, Y., Park, S., et al. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01314 · Cited by 2 · see citing works
AI in Genomics: From Variant Calling to Multi‐Omics Integration
Sultana, H., Mohanty, S., Solomon, A. D., Iqbal, M. Y., Wani, A. K., Kumar, V., & Khattri, A. (2026) · BioEssays · doi:10.1002/bies.70160 · Cited by 2 · see citing works
Decoding immune signaling landscapes: what AlphaGenome reveals
Fagbohun, O. F. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02844-9 · Cited by 2 · see citing works
- Authors
- Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., Thomas, R., Dutordoir, V., Perino, M., De, S., Karollus, A., Gayoso, A., Sargeant, T., Mottram, A., Wong, L. H., Drotár, P., Kosiorek, A., Senior, A., Tanburn, R., Applebaum, T., Basu, S., Hassabis, D., & Kohli, P.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- Corresponding author
- Kohli, Pushmeet
- ORCID
- Avsec, Žiga
- Latysheva, Natasha
- Cheng, Jun
- Novati, Guido
- Taylor, Kyle R.
- Ward, Tom
- Bycroft, Clare
- Nicolaisen, Lauren
- Arvaniti, Eirini
- Pan, Joshua
- Thomas, Raina
- Dutordoir, Vincent
- Perino, Matteo
- De, Soham
- Karollus, Alexander
- Gayoso, Adam
- Sargeant, Toby
- Mottram, Anne
- Wong, Lai Hong
- Drotár, Pavol
- Kosiorek, Adam
- Senior, Andrew
- Applebaum, Taylor
- Basu, Souradeep
- Hassabis, Demis
- Kohli, Pushmeet
- Venue
- Nature
- Publisher
- openRxiv
- Published
- 2025-06-27
- Volume
- 649
- Issue
- 8099
- Pages
- 1206-1218
- ISSN
- 0028-0836 (ISSN-L)
- PMC
- PMC12851941
- OpenAlex
- W7125967877
- Cited by
- 395
- References
- 110
- Citations by year
- 2026: 352 · 2025: 43
- FWCI
- 729.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- AlphaGenome, variant effect prediction, gene expression prediction, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chromatin contact maps, single base pair resolution, regulatory variants, human and mouse genomes
- MeSH terms
- Deep Learning, Animals, Chromatin, Gene Expression Regulation, Humans, Models, Genetic, Proto-Oncogene Proteins, Transcription Factors, Genetic Variation, Genome, Human, Genome, Sequence Analysis, DNA, RNA Splice Sites, Genomics, Mice
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