The Human Gene Mutation Database (HGMD®): optimizing its use in a clinical diagnostic or research setting

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

A global reference for human genetic variation

The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247

A method and server for predicting damaging missense mutations.

Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194

Analysis of protein-coding genetic variation in 60,706 humans

Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587

CADD: predicting the deleteriousness of variants throughout the human genome

Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J., & Kircher, M. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1016 · Cited by 3,534

The contribution of de novo coding mutations to autism spectrum disorder

Iossifov, I., O’Roak, B. J., Sanders, S. J., Ronemus, M., Krumm, N., Levy, D., Stessman, H. A., Witherspoon, K. T., Vives, L., Patterson, K. E., et al. (2014) · Nature · doi:10.1038/nature13908 · Cited by 2,585

Expansion of the Gene Ontology knowledgebase and resources

The Gene Ontology Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1108 · Cited by 1,850

Patterns and rates of exonic de novo mutations in autism spectrum disorders

Neale, B. M., Kou, Y., Liu, L., Ma’ayan, A., Samocha, K. E., Sabo, A., Lin, C.-F., Stevens, C., Wang, L.-S., Makarov, V., et al. (2012) · Nature · doi:10.1038/nature11011 · Cited by 1,734

High Rate of Recurrent De Novo Mutations in Developmental and Epileptic Encephalopathies

Hamdan, F. F., Myers, C. T., Cossette, P., Lemay, P., Spiegelman, D., Laporte, A. D., Nassif, C., Diallo, O., Monlong, J., Cadieux-Dion, M., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.008 · Cited by 445

Using high-resolution variant frequencies to empower clinical genome interpretation

Whiffin, N., Minikel, E., Walsh, R., O’Donnell-Luria, A. H., Karczewski, K., Ing, A. Y., Barton, P. J. R., Funke, B., Cook, S. A., MacArthur, D., et al. (2017) · Genetics in Medicine · doi:10.1038/gim.2017.26 · Cited by 441

Inferring the molecular and phenotypic impact of amino acid variants with MutPred2

Pejaver, V., Urresti, J., Lugo-Martinez, J., Pagel, K. A., Lin, G. N., Nam, H.-J., Mort, M., Cooper, D. N., Sebat, J., Iakoucheva, L. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19669-x · Cited by 923

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 772

The landscape of tolerated genetic variation in humans and primates

(2023) · doi:10.1126/science.abn8197 · Cited by 182

Physiological roles of mammalian transmembrane adenylyl cyclase isoforms

Ostrom, K. F., LaVigne, J. E., Brust, T. F., Seifert, R., Dessauer, C. W., Watts, V. J., & Ostrom, R. S. (2022) · Physiological Reviews · doi:10.1152/physrev.00013.2021 · Cited by 131

The molecular structure of IFT-A and IFT-B in anterograde intraflagellar transport trains

Lacey, S. E., Foster, H. E., & Pigino, G. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00905-5 · Cited by 119

Protein codes promote selective subcellular compartmentalization

Kilgore, H. R., Chinn, I., Mikhael, P. G., Mitnikov, I., Van Dongen, C., Zylberberg, G., Afeyan, L., Banani, S. F., Wilson-Hawken, S., Lee, T. I., et al. (2025) · Science · doi:10.1126/science.adq2634 · Cited by 99

Fast and deep phosphoproteome analysis with the Orbitrap Astral mass spectrometer

(2024) · doi:10.1038/s41467-024-51274-0 · Cited by 83

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set

Stein, D., Kars, M. E., Wu, Y., Bayrak, Ç. S., Stenson, P. D., Cooper, D. N., Schlessinger, A., & Itan, Y. (2023) · Genome Medicine · doi:10.1186/s13073-023-01261-9 · Cited by 53

Biomedical data, computational methods and tools for evaluating disease–disease associations

Xiang, J., Zhang, J., Zhao, Y., Wu, F.-X., & Li, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac006 · Cited by 49

The kidney slit diaphragm resembles a fishnet

Birtasu, A. N., Wieland, K., Ermel, U. H., Arghittu, S. M., Manger, S., Kocylowski, M. K., Fakler, B., Scheffer, M. P., Covino, R., Grahammer, F., et al. (2025) · Kidney International · doi:10.1016/j.kint.2025.08.028 · Cited by 32

Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and structures

(2024) · doi:10.1038/s41592-024-02409-0 · Cited by 29

Efficient correction of ABCA4 variants by CRISPR-Cas9 in hiPSCs derived from Stargardt disease patients

Siles, L., Ruiz-Nogales, S., Navinés-Ferrer, A., Méndez-Vendrell, P., & Pomares, E. (2023) · Molecular Therapy - Nucleic Acids · doi:10.1016/j.omtn.2023.02.032 · Cited by 29

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Sen, N., Anishchenko, I., Bordin, N., Sillitoe, I., Velankar, S., Baker, D., & Orengo, C. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac187 · Cited by 25

Discordance between a deep learning model and clinical-grade variant pathogenicity classification in a rare disease cohort

Kong, S. W., Lee, I.-H., Collen, L. V., Field, M., Manrai, A. K., Snapper, S. B., & Mandl, K. D. (2025) · npj Genomic Medicine · doi:10.1038/s41525-025-00480-w · Cited by 18

Decoding Missense Variants by Incorporating Phase Separation via Machine Learning

Feng, M., Wei, X., Zheng, X., Liu, L., Lin, L., Xia, M., He, G., Shi, Y., & Lu, Q. (2024) · Nature Communications · doi:10.1038/s41467-024-52580-3 · Cited by 15

High-Efficiency CRISPR/Cas9-Mediated Correction of a Homozygous Mutation in Achromatopsia-Patient-Derived iPSCs

Siles, L., Gaudó, P., & Pomares, E. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043655 · Cited by 9

Spectrum and Prevalence of Rare APOE Variants and Their Association with Familial Dysbetalipoproteinemia

Blokhina, A. V., Ershova, A. I., Kiseleva, A. V., Sotnikova, E. A., Zharikova, A. A., Zaicenoka, M., Vyatkin, Y. V., Ramensky, V. E., Kutsenko, V. A., Garbuzova, E. V., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252312651 · Cited by 8

Integrative analysis of pathogenic variants in glucose-6-phosphatase based on an AlphaFold2 model

Sinclair, M., Stein, R. A., Sheehan, J. H., Hawes, E. M., O’Brien, R. M., Tajkhorshid, E., & Claxton, D. P. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae036 · Cited by 8

R-code for calculating fluctuation assay results and 95% confidence intervals based on Ma–Sandri–Sarkar Maximum Likelihood

Abdalla, O., Walker, C., & Ishimori, K. (2024) · Software Impacts · doi:10.1016/j.simpa.2024.100661 · Cited by 6

InpherNet accelerates monogenic disease diagnosis using patients’ candidate genes’ neighbors

Yoo, B., Birgmeier, J., Bernstein, J. A., & Bejerano, G. (2021) · Genetics in Medicine · doi:10.1038/s41436-021-01238-2 · Cited by 4

Rescue of the disease-associated phenotype in CRISPR-corrected hiPSCs as a therapeutic approach for inherited retinal dystrophies

Siles, L., & Pomares, E. (2025) · Molecular Therapy Nucleic Acids · doi:10.1016/j.omtn.2025.102482 · Cited by 4

An interpretable molecular framework for predicting cancer driver missense mutations

Yang, Y., Sun, W., Liu, Y., Zhang, J., & Li, M. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2524289123 · Cited by 3

MAVISp : A modular structure‐based framework for protein variant effects

(2026) · doi:10.1002/pro.70548 · Cited by 3

A predictive framework for stop-loss variants with C-terminal extensions

Yoon, J. G., Lee, S., Kim, Y., Gee, H. Y., Lee, M. G., & Lee, K.-A. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag031 · Cited by 3

Uncovering genetic mimics in multiple sclerosis: A single-center clinical exome sequencing study

Mandler, J. M., Härtl, J., Cordts, I., Sturm, M., Hedderich, D. M., Bafligil, C., Baki, E., Becker, B., Machetanz, G., Haack, T. B., et al. (2024) · Multiple Sclerosis Journal - Experimental, Translational and Clinical · doi:10.1177/20552173241263491 · Cited by 2

Decoding Missense Variants by Incorporating Phase Separation via Machine Learning

Feng, M., Wei, X., Zheng, X., Liu, L., Lin, L., Xia, M., He, G., Shi, Y., & Lu, Q. (2024) · doi:10.1101/2024.04.01.587546