CoChat
Sign Up
Cited by 50Genomics and Rare DiseasesGenomics and Phylogenetic StudiesCancer Genomics and Diagnostics

Variant effect predictor correlation with functional assays is reflective of clinical classification performance

Open access
Benjamin Livesey, Joseph A. Marsh·2025·Genome biology
DOI 10.1186/s13059-025-03575-wPMC PMC12016141

Published version of a preprint

Open this paper in CoChat — free account

Abstract

BACKGROUND: Understanding the relationship between protein sequence and function is crucial for accurate classification of missense variants. Variant effect predictors (VEPs) play a vital role in deciphering this complex relationship, yet evaluating their performance remains challenging for several reasons, including data circularity, where the same or related data is used for training and assessment. High-throughput experimental strategies like deep mutational scanning (DMS) offer a promising solution. RESULTS: In this study, we extend upon our previous benchmarking approach, assessing the performance of 97 VEPs using missense DMS measurements from 36 different human proteins. In addition, a new pairwise, VEP-centric approach mitigates the impact of missing predictions on overall performance comparison. We observe a strong correspondence between VEP performance in DMS-based benchmarks and clinical variant classification, especially for predictors that have not been directly trained on human clinical variants. CONCLUSIONS: Our results suggest that comparing VEP performance against diverse functional assays represents a reliable strategy for assessing their relative performance in clinical variant classification. However, major challenges in clinical interpretation of VEP scores persist, highlighting the need for further research to fully leverage computational predictors for genetic diagnosis. We also address practical considerations for end users in terms of choice of methodology.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

OpenAlex · 10.1186/s13059-025-03575-w

Cites

19 of 19 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 92

  1. [1]Grimm DG, Azencott C-A, Aicheler F, Gieraths U, MacArthur DG, Samocha KE, et al. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Hum Mutat. 2015;36:513–23. 25684150 PMC4409520 · DOI · Paper page
  2. [3]Gunning AC, Fryer V, Fasham J, Crosby AH, Ellard S, Baple EL, et al. Assessing performance of pathogenicity predictors using clinically relevant variant datasets. J Med Genet. 2021;58:547–55. 32843488 PMC8327323 · DOI
  3. [4]Walters-Sen LC, Hashimoto S, Thrush DL, Reshmi S, Gastier-Foster JM, Astbury C, et al. Variability in pathogenicity prediction programs: impact on clinical diagnostics. Mol Genet Genomic Med. 2015;3:99–110. 25802880 PMC4367082 · DOI
  4. [5]Niroula A, Vihinen M. How good are pathogenicity predictors in detecting benign variants? PLOS Comput Biol. 2019;15: e1006481. 30742610 PMC6386394 · DOI
  5. [6]Weile J, Roth FP. Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas. Hum Genet. 2018;137:665–78. 30073413 PMC6153521 · DOI
  6. [7]Fowler DM, Adams DJ, Gloyn AL, Hahn WC, Marks DS, Muffley LA, et al. An Atlas of Variant Effects to understand the genome at nucleotide resolution. Genome Biol. 2023;24:147. 37394429 PMC10316620 · DOI
  7. [9]Mahmood K, Jung C-H, Philip G, Georgeson P, Chung J, Pope BJ, et al. Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics. Hum Genomics. 2017;11:10. 28511696 PMC5433009 · DOI
  8. [10]Critical Assessment of Genome Interpretation Consortium. CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods. Genome Biol. 2024;25:53. 38389099 PMC10882881 · DOI
  9. [11]Zhang J, Kinch LN, Cong Q, Weile J, Sun S, Cote AG, et al. Assessing predictions of fitness effects of missense mutations in SUMO-conjugating enzyme UBE2I. Hum Mutat. 2017;38:1051–63. 28817247 PMC5746193 · DOI
  10. [12]Pejaver V, Babbi G, Casadio R, Folkman L, Katsonis P, Kundu K, et al. Assessment of methods for predicting the effects of PTEN and TPMT protein variants. Hum Mutat. 2019;40:1495–506. 31184403 PMC6744362 · DOI
  11. [13]Chen B, Solis-Villa C, Hakenberg J, Qiao W, Srinivasan RR, Yasuda M, et al. Acute intermittent porphyria: predicted pathogenicity of HMBS variants indicates extremely low penetrance of the autosomal dominant disease. Hum Mutat. 2016; PMC5063710 27539938
  12. [14]Notin P, Kollasch A, Ritter D, van Niekerk L, Paul S, Spinner H, et al. ProteinGym: large-scale benchmarks for protein fitness prediction and design. Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S, editors. Adv Neural Inf Process Syst. 2023;36:64331–79.
  13. [15]Livesey BJ, Marsh JA. Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations. Mol Syst Biol. 2020;16: e9380. 32627955 PMC7336272 · DOI
  14. [16]Livesey BJ, Marsh JA. Updated benchmarking of variant effect predictors using deep mutational scanning. Mol Syst Biol. 2023;19: e11474. 37310135 PMC10407742 · DOI
  15. [17]Gerasimavicius L, Livesey BJ, Marsh JA. Correspondence between functional scores from deep mutational scans and predicted effects on protein stability. Protein Sci. 2023;32: e4688. 37243972 PMC10273344 · DOI · Paper page
  16. [18]Rubin AF, Stone J, Bianchi AH, Capodanno BJ, Da EY, Dias M, et al. MaveDB 2024: a curated community database with over seven million variant effects from multiplexed functional assays. Genome Biol. 2025;26:13. 39838450 PMC11753097 · DOI · Paper page
  17. [21]Matreyek KA, Starita LM, Stephany JJ, Martin B, Chiasson MA, Gray VE, et al. Multiplex assessment of protein variant abundance by massively parallel sequencing. Nat Genet. 2018;50:874–82. 29785012 PMC5980760 · DOI · Paper page
  18. [22]Lin Y-J, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors. Hum Genomics. 2024;18:90. 39198917 PMC11360829 · DOI
  19. [23]Livesey BJ, Badonyi M, Dias M, Frazer J, Kumar S, Lindorff-Larsen K, et al. Guidelines for releasing a variant effect predictor. Genome Biol. 2025;26:97 40234898 PMC11998465 · DOI
  20. [24]Gray VE, Hause RJ, Luebeck J, Shendure J, Fowler DM. Quantitative missense variant effect prediction using large-scale mutagenesis data. Cell Syst. 2018;6:116-124.e3. 29226803 PMC5799033 · DOI
  21. [25]Cheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, Applebaum T, et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science. 2023;381:eadg7492. 37733863
  22. [26]Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA, Thomas NST, et al. Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat. 2003;21:577–81. 12754702 · DOI
  23. [27]Pathak AK, Bora N, Badonyi M, Livesey BJ, Consortium S, Ngeow J, et al. Pervasive ancestry bias in variant effect predictors. bioRxiv; 2024 [cited 2024 Jun 12]. p. 2024.05.20.594987. Available from: 10.1101/2024.05.20.594987v2 · DOI
  24. [28]Jagota M, Ye C, Albors C, Rastogi R, Koehl A, Ioannidis N, et al. Cross-protein transfer learning substantially improves disease variant prediction. Genome Biol. 2023;24:182. 37550700 PMC10408151 · DOI
  25. [29]Munro D, Singh M. DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction. Bioinformatics. 2021;36:5322–9. 33325500 PMC8016454 · DOI
  26. [30]Wu Y, Li R, Sun S, Weile J, Roth FP. Improved pathogenicity prediction for rare human missense variants. Am J Hum Genet. 2021;108:1891–906. 34551312 PMC8546039 · DOI
  27. [31]Meier J, Rao R, Verkuil R, Liu J, Sercu T, Rives A. Language models enable zero-shot prediction of the effects of mutations on protein function. Adv Neural Inf Process Syst. 2021;34:29287–303.
  28. [33]Thomas PD, Kejariwal A. Coding single-nucleotide polymorphisms associated with complex vs. Mendelian disease: evolutionary evidence for differences in molecular effects. Proc Natl Acad Sci U S A. 2004;101:15398–403. PMC523449 15492219
  29. [34]Kim HY, Kim D. Prediction of mutation effects using a deep temporal convolutional network. Bioinformatics. 2020;36:2047–52. 31746978 · DOI
  30. [35]Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet Editor Board Jonathan Haines Al. 2013;07:Unit7.20. PMC4480630 23315928
  31. [38]Orenbuch R, Kollasch AW, Spinner HD, Shearer CA, Hopf TA, Franceschi D, et al. Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders. medRxiv; 2023 [cited 2023 Dec 7]. p. 2023.11.27.23299062. Available from: 10.1101/2023.11.27.23299062v1 · DOI
  32. [39]Tekpinar M, David L, Henry T, Carbone A. PRESCOTT: a population aware, epistatic and structural model accurately predicts missense effect [Internet]. medRxiv; 2024 [cited 2024 Feb 7]. p. 2024.02.03.24302219. Available from: 10.1101/2024.02.03.24302219v1 PMC12054230 40329382 · DOI
  33. [40]Laine E, Karami Y, Carbone A. GEMME: a simple and fast global epistatic model predicting mutational effects. Mol Biol Evol. 2019;36:2604–19. 31406981 PMC6805226 · DOI
  34. [41]Notin P, Niekerk LV, Kollasch AW, Ritter D, Gal Y, Marks DS. TranceptEVE: combining family-specific and family-agnostic models of protein sequences for improved fitness prediction. bioRxiv; 2022 [cited 2023 Dec 7]. p. 2022.12.07.519495. Available from: 10.1101/2022.12.07.519495v2 · DOI
  35. [42]Notin P, Dias M, Frazer J, Marchena-Hurtado J, Gomez AN, Marks D, et al. Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S, editors. Proc 39th Int Conf Mach Learn. 2022;162:16990–7017.
  36. [43]Su J, Han C, Zhou Y, Shan J, Zhou X, Yuan F. SaProt: protein language modeling with structure-aware vocabulary [Internet]. bioRxiv; 2024 [cited 2024 Oct 31]. p. 2023.10.01.560349. Available from: 10.1101/2023.10.01.560349v5 · DOI
  37. [45]Shin J-E, Riesselman AJ, Kollasch AW, McMahon C, Simon E, Sander C, et al. Protein design and variant prediction using autoregressive generative models. Nat Commun. 2021;12:2403. 33893299 PMC8065141 · DOI · Paper page
  38. [46]Rastogi R, Chung R, Li S, Li C, Lee K, Woo J, et al. Critical assessment of missense variant effect predictors on disease-relevant variant data. Hum Genet. 2025;144:281–93. 40113603 PMC11976771 · DOI
  39. [48]Marquet C, Heinzinger M, Olenyi T, Dallago C, Erckert K, Bernhofer M, et al. Embeddings from protein language models predict conservation and variant effects. Hum Genet. 2022;141:1629–47. 34967936 PMC8716573 · DOI
  40. [49]Tong S-Y, Fan K, Zhou Z-W, Liu L-Y, Zhang S-Q, Fu Y, et al. mvPPT: a highly efficient and sensitive pathogenicity prediction tool for missense variants. Genomics Proteomics Bioinformatics. 2023;21:414–26. 35940520 PMC10626173 · DOI · Paper page
  41. [50]Reeb J, Wirth T, Rost B. Variant effect predictions capture some aspects of deep mutational scanning experiments. BMC Bioinformatics. 2020;21:107. 32183714 PMC7077003 · DOI
  42. [51]Li N, Mazaika E, Theotokis P, Zhang X, Jang M, Ahmad M, et al. Variant annotation across homologous proteins (“Paralogue Annotation”) identifies disease-causing missense variants with high precision, and is widely applicable across protein families. bioRxiv; 2023 [cited 2024 May 1]. p. 2023.08.07.552236. Available from: 10.1101/2023.08.07.552236v1 · DOI
  43. [52]Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet. 2016;99:877–85. 27666373 PMC5065685 · DOI · Paper page
  44. [53]Molotkov I, Koboldt DC, Artomov M. SNPred outperforms other ensemble-based SNV pathogenicity predictors and elucidates the challenges of using ClinVar for evaluation of variant classification quality. medRxiv; 2023 [cited 2023 Dec 7]. p. 2023.09.07.23295192. Available from: 10.1101/2023.09.07.23295192v2 · DOI
  45. [54]Li C, Zhi D, Wang K, Liu X. MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning. Genome Med. 2022;14:115. 36209109 PMC9548151 · DOI
  46. [55]Yates CM, Filippis I, Kelley LA, Sternberg MJE. SuSPect: enhanced prediction of single amino acid variant (SAV) phenotype using network features. J Mol Biol. 2014;426:2692–701. 24810707 PMC4087249 · DOI
  47. [57]Sim N-L, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012;40:W452-457. 22689647 PMC3394338 · DOI
  48. [58]The UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49:D480–9. 33237286 PMC7778908 · DOI · Paper page
  49. [59]Li H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics. 2011;27:718–9. 21208982 PMC3042176 · DOI
  50. [60]Tabet DR, Kuang D, Lancaster MC, Li R, Liu K, Weile J, et al. Benchmarking computational variant effect predictors by their ability to infer human traits. Genome Biol. 2024;25:172. 38951922 PMC11218265 · DOI
  51. [61]Gerasimavicius L, Livesey BJ, Marsh JA. Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure. Nat Commun. 2022;13:3895. 35794153 PMC9259657 · DOI
  52. [62]Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021;373:871–6. 34282049 PMC7612213 · DOI · Paper page
  53. [64]Fawzy M, Marsh JA. Understanding the heterogeneous performance of variant effect predictors across human protein-coding genes. Sci Rep. 2024;14:26114. 39478110 PMC11526010 · DOI · Paper page
  54. [65]Tejura M, Fayer S, McEwen AE, Flynn J, Starita LM, Fowler DM. Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes. Am J Hum Genet. 2024;111:2031–43. 39173626 PMC11393694 · DOI · Paper page
  55. [66]Dias M, Orenbuch R, Marks DS, Frazer J. Toward trustable use of machine learning models of variant effects in the clinic. Am J Hum Genet. 2024;111:2589–93. 39561772 PMC11639075 · DOI
  56. [67]Esposito D, Weile J, Shendure J, Starita LM, Papenfuss AT, Roth FP, et al. MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect. Genome Biol. 2019;20:223. 31679514 PMC6827219 · DOI
  57. [68]Liu X, Li C, Mou C, Dong Y, Tu Y. dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Med. 2020;12:103. 33261662 PMC7709417 · DOI
  58. [71]Livesey BJ, Marsh JA. Scripts and files required to generate data for figures in “Variant effect predictor correlation with functional assays is reflective of clinical performance.” Figshare. 2025; Available from: 10.6084/m9.figshare.28295408.v2 PMC12016141 40264194 · DOI
  59. [72]Meitlis I, Allenspach EJ, Bauman BM, Phan IQ, Dabbah G, Schmitt EG, et al. Multiplexed functional assessment of genetic variants in CARD11. Am J Hum Genet. 2020;107:1029–43. 33202260 PMC7820631 · DOI
  60. [73]van Loggerenberg W, Sowlati-Hashjin S, Weile J, Hamilton R, Chawla A, Sheykhkarimli D, et al. Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation. Am J Hum Genet. 2023;110:1769–86. 37729906 PMC10577081 · DOI
  61. [74]Gersing S, Cagiada M, Gebbia M, Gjesing AP, Coté AG, Seesankar G, et al. A comprehensive map of human glucokinase variant activity. Genome Biol. 2023;24:97. 37101203 PMC10131484 · DOI · Paper page
  62. [75]Huttinger ZM, Haynes LM, Yee A, Kretz CA, Holding ML, Siemieniak DR, et al. Deep mutational scanning of the plasminogen activator inhibitor-1 functional landscape. Sci Rep. 2021;11:18827. 34552126 PMC8458277 · DOI
  63. [76]McDonnell AF, Plech M, Livesey BJ, Gerasimavicius L, Owen LJ, Hall HN, et al. Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants. Mol Syst Biol. 2024;20:825–44. 38849565 PMC11219921 · DOI · Paper page
  64. [77]Majithia AR, Tsuda B, Agostini M, Gnanapradeepan K, Rice R, Peloso G, et al. Prospective functional classification of all possible missense variants in PPARG. Nat Genet. 2016;48:1570–5. 27749844 PMC5131844 · DOI · Paper page
  65. [78]Miller PG, Sathappa M, Moroco JA, Jiang W, Qian Y, Iqbal S, et al. Allosteric inhibition of PPM1D serine/threonine phosphatase via an altered conformational state. Nat Commun. 2022;13:3778. 35773251 PMC9246869 · DOI
  66. [79]Kwon JJ, Hajian B, Bian Y, Young LC, Amor AJ, Fuller JR, et al. Structure–function analysis of the SHOC2–MRAS–PP1C holophosphatase complex. Nature. 2022;609:408–15. 35831509 PMC9694338 · DOI
  67. [80]Ahler E, Register AC, Chakraborty S, Fang L, Dieter EM, Sitko KA, et al. A combined approach reveals a regulatory mechanism coupling Src’s kinase activity, localization, and phosphotransferase-independent functions. Mol Cell. 2019;74:393-408.e20. 30956043 PMC6474823 · DOI
  68. [81]Clausen L, Voutsinos V, Cagiada M, Johansson KE, Grønbæk-Thygesen M, Nariya S, et al. A mutational atlas for Parkin proteostasis. Nat Commun. 2024;15:1541. 38378758 PMC10879094 · DOI · Paper page
  69. [82]Weng C, Faure AJ, Escobedo A, Lehner B. The energetic and allosteric landscape for KRAS inhibition. Nature. 2024;626:643–52. 38109937 PMC10866706 · DOI
  70. [83]Grønbæk-Thygesen M, Voutsinos V, Johansson KE, Schulze TK, Cagiada M, Pedersen L, et al. Deep mutational scanning reveals a correlation between degradation and toxicity of thousands of aspartoacylase variants. Nat Commun. 2024;15:4026. 38740822 PMC11091098 · DOI
  71. [84]Won D-G, Kim D-W, Woo J, Lee K. 3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints. Bioinformatics. 2021;37:4626–34. 34270679 PMC8665754 · DOI
  72. [86]Schubach M, Maass T, Nazaretyan L, Röner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res. 2024;52:D1143–54. PMC10767851 38183205
  73. [87]Li S, van der Velde KJ, de Ridder D, van Dijk ADJ, Soudis D, Zwerwer LR, et al. CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations. Genome Med. 2020;12:75. 32831124 PMC7446154 · DOI
  74. [88]Yang KK, Fusi N, Lu AX. Convolutions are competitive with transformers for protein sequence pretraining. Cell Syst. 2024;15:286-294.e2. 38428432 · DOI · Paper page
  75. [89]Pei J, Grishin NV. The DBSAV database: predicting deleteriousness of single amino acid variations in the human proteome. J Mol Biol. 2021;433: 166915. 33676930 PMC8119332 · DOI
  76. [90]Brandes N, Goldman G, Wang CH, Ye CJ, Ntranos V. Genome-wide prediction of disease variant effects with a deep protein language model. Nat Genet. 2023;55:1512–22. 37563329 PMC10484790 · DOI
  77. [93]Takeda J-I, Nanatsue K, Yamagishi R, Ito M, Haga N, Hirata H, et al. InMeRF: prediction of pathogenicity of missense variants by individual modeling for each amino acid substitution. NAR Genomics Bioinforma. 2020;2:lqaa038. PMC7671370 33543123
  78. [94]Huang Y-F, Siepel A. Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease. Genome Res. 2019;29:1310–21. 31249063 PMC6673719 · DOI
  79. [95]Danzi MC, Dohrn MF, Fazal S, Beijer D, Rebelo AP, Cintra V, et al. Deep structured learning for variant prioritization in Mendelian diseases. Nat Commun. 2023;14:4167. 37443090 PMC10345112 · DOI
  80. [96]Chennen K, Weber T, Lornage X, Kress A, Böhm J, Thompson J, et al. MISTIC: a prediction tool to reveal disease-relevant deleterious missense variants. PLoS ONE. 2020;15: e0236962. 32735577 PMC7394404 · DOI
  81. [97]Petrazzini BO, Balick DJ, Forrest IS, Cho J, Rocheleau G, Jordan DM, et al. Ensemble and consensus approaches to prediction of recessive inheritance for missense variants in human disease. Cell Rep Methods. 2024;4: 100914. 39657681 PMC11704621 · DOI
  82. [98]Jiang TT, Fang L, Wang K. Deciphering, “the language of nature”: a transformer-based language model for deleterious mutations in proteins. The Innovation. 2023;4: 100487. 37636282 PMC10448337 · DOI
  83. [99]Quinodoz M, Peter VG, Cisarova K, Royer-Bertrand B, Stenson PD, Cooper DN, et al. Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity. Am J Hum Genet. 2022;109:457–70. 35120630 PMC8948164 · DOI
  84. [100]Kuru N, Dereli O, Akkoyun E, Bircan A, Tastan O, Adebali O. PHACT: phylogeny-aware computing of tolerance for missense mutations. Mol Biol Evol. 2022;39:msac114. PMC9178230 35639618
  85. [101]Dereli O, Kuru N, Akkoyun E, Bircan A, Tastan O, Adebali O. PHACTboost: a phylogeny-aware pathogenicity predictor for missense mutations via boosting. Mol Biol Evol. 2024;41:msae136. PMC11251492 38934805
  86. [102]Capriotti E, Fariselli P. PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants. Nucleic Acids Res. 2017;45:W247–52. 28482034 PMC5570245 · DOI
  87. [104]Ferruz N, Schmidt S, Höcker B. ProtGPT2 is a deep unsupervised language model for protein design. Nat Commun. 2022;13:4348. 35896542 PMC9329459 · DOI · Paper page
  88. [105]Ponzoni L, Peñaherrera DA, Oltvai ZN, Bahar I. Rhapsody: predicting the pathogenicity of human missense variants. Bioinformatics. 2020;36:3084–92. 32101277 PMC7214033 · DOI
  89. [106]Hesslow D, Zanichelli N, Notin P, Poli I, Marks D. RITA: a study on scaling up generative protein sequence models. arXiv; 2022 [cited 2023 Dec 6]. Available from: http://arxiv.org/abs/2205.05789 · arXiv
  90. [108]Zhao H, Du H, Zhao S, Chen Z, Li Y, Xu K, et al. SIGMA leverages protein structural information to predict the pathogenicity of missense variants. Cell Rep Methods. 2024;4: 100687. 38211594 PMC10831939 · DOI
  91. [109]Wang B, Lei X, Tian W, Perez-Rathke A, Tseng Y-Y, Liang J. Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations. Brief Bioinform. 2023;24:bbad206. PMC10359089 37332013
  92. [110]Huang Y-F. Unified inference of missense variant effects and gene constraints in the human genome. PLOS Genet. 2020;16: e1008922. 32667917 PMC7384676 · DOI

Cited by

Sorted by citations

Authors
Livesey, B., & Marsh, J. A.
Affiliations
Institute of Genetics and Cancer (GB)
Corresponding author
Joseph A. Marsh
ORCID
Venue
Genome biology
Publisher
Springer Science and Business Media LLC
Published
2025-04-22
Volume
26
Issue
1
Pages
104-104
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-025-03575-w
PMC
PMC12016141
OpenAlex
W4409669254
Cited by
50
References
19
Citations by year
2026: 24 · 2025: 20 · 2024: 6
FWCI
25.87
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders
Medical Research Council (MC_UU_00035/9)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
variant effect predictors, missense mutations, deep mutational scanning, functional assays, benchmarking, genetic diagnosis
MeSH terms
Humans, Computational Biology, Mutation, Missense

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review