Function

Summary Table

NAME	CATEGORY	CITATION	YEAR
ESM-2	Evolutionary and Generative Protein Models	Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).	NA
EVE	Evolutionary and Generative Protein Models	Frazer J, Notin P, Dias M, Gomez A, ...&, Marks DS. (2021) Disease variant prediction with deep generative models of evolutionary data Nature, 599 (7883) 91-95. doi:10.1038/s41586-021-04043-8. PMID 34707284	2021
ProGen2	Evolutionary and Generative Protein Models	Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. ProGen2: Exploring the boundaries of protein language models. Cell Syst. 14, 968-978.e3 (2023).	NA
ProtBERT	Evolutionary and Generative Protein Models	Elnaggar A. et al. (2021) ProtTrans: towards cracking the language of lifes code through self-supervised deep learning and high performance computing. IEEE Trans. Pattern Anal. Mach. Intell., 1, 1.	NA
ProteinBERT	Evolutionary and Generative Protein Models	Brandes, N., Ofer, D., Peleg, Y., Rappoport, N. & Linial, M. ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics 38, 2102–2110 (2022).	NA
ClinVar	Functional Annotation	Landrum, M. J. et al. ClinVar: updates to support classifications of both germline and somatic variants. Nucleic Acids Res. 53, D1313–D1321 (2025).	NA
dbNSFP v4	Functional Annotation	Liu X, Li C, Mou C, Dong Y, ...&, Tu Y. (2020) dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs Genome Med., 12 (1) 103. doi:10.1186/s13073-020-00803-9. PMID 33261662	2020
DAVID	Pathway and Gene Ontology Enrichment	Huang, D. W. et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8, R183 (2007).	NA
Gene Ontology	Pathway and Gene Ontology Enrichment	Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).	NA
AlphaFold 2	Structure Prediction	Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).	NA
AlphaFold 3	Structure Prediction	Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).	NA
AlphaFold	Structure Prediction	Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).	NA
AlphaMissense	Variant Effect Prediction	Cheng J, Novati G, Pan J, Bycroft C, ...&, Avsec Ž. (2023) Accurate proteome-wide missense variant effect prediction with AlphaMissense Science, 381 (6664) eadg7492. doi:10.1126/science.adg7492. PMID 37733863	2023
CADD v1.4	Variant Effect Prediction	Rentzsch P, Witten D, Cooper GM, Shendure J, ...&, Kircher M. (2019) CADD: predicting the deleteriousness of variants throughout the human genome Nucleic Acids Res., 47 (D1) D886-D894. doi:10.1093/nar/gky1016. PMID 30371827	2019
CADD v1.6 (CADD-Splice)	Variant Effect Prediction	Rentzsch, P., Schubach, M., Shendure, J. & Kircher, M. CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 13, 31 (2021).	NA
CADD v1.7	Variant Effect Prediction	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. 52, D1143–D1154 (2024).	NA
CADD	Variant Effect Prediction	Kircher M, Witten DM, Jain P, O'Roak BJ, ...&, Shendure J. (2014) A general framework for estimating the relative pathogenicity of human genetic variants Nat. Genet., 46 (3) 310-315. doi:10.1038/ng.2892. PMID 24487276	2014
M-CAP	Variant Effect Prediction	Jagadeesh, K. A. et al. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat. Genet. 48, 1581–1586 (2016).	NA
MVP	Variant Effect Prediction	Qi, H. et al. MVP predicts the pathogenicity of missense variants by deep learning. Nat. Commun. 12, 510 (2021).	NA
MetaLR / MetaSVM	Variant Effect Prediction	Dong, C. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24, 2125–2137 (2015).	NA
MutationAssessor	Variant Effect Prediction	Su, Y. et al. MutationAssessor in cBioPortal. bioRxivorg (2025) doi:10.1101/2025.08.10.669566.	NA
PolyPhen-2	Variant Effect Prediction	Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2. Curr. Protoc. Hum. Genet. 76, 7.20.1-7.20.41 (2013).	NA
PrimateAI-3D	Variant Effect Prediction	Gao, H. et al. The landscape of tolerated genetic variation in humans and primates. Science 380, eabn8153 (2023).	NA
REVEL	Variant Effect Prediction	Ioannidis, N. M. et al. REVEL: An ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 99, 877–885 (2016).	NA
SIFT	Variant Effect Prediction	Ng, P. C. & Henikoff, S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).	NA
UNEECON	Variant Effect Prediction	Huang, Y.-F. Unified inference of missense variant effects and gene constraints in the human genome. PLoS Genet. 16, e1008922 (2020).	NA
VEST4	Variant Effect Prediction	Carter, H., Douville, C., Stenson, P. D., Cooper, D. N. & Karchin, R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics 14 Suppl 3, S3 (2013).	NA

Evolutionary and Generative Protein Models

ESM-2

• NAME : ESM-2
• SHORT NAME : ESM-2
• URL : https://esmatlas.com/
• FULL NAME : Evolutionary Scale Modeling v2
• KEYWORDS : transformer, LLM, structure, sequence
• USE : embeddings, structure prediction
• DESCRIPTION : Large-scale protein language model enabling structure/function prediction.
• CITATION : Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
• PUBMED_LINK : 36927031

EVE

• NAME : EVE
• SHORT NAME : EVE
• URL : https://evemodel.org/
• FULL NAME : Evolutionary model of Variant Effect
• KEYWORDS : evolutionary, MSA, variant effect
• USE : missense scoring
• DESCRIPTION : VAE-based unsupervised model to predict variant impact using MSAs.
• TITLE : Disease variant prediction with deep generative models of evolutionary data
• ABSTRACT : Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences1-3. In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods4-10 have relied on training machine learning models on known disease labels. As these labels are sparse, biased and of variable quality, the resulting models have been considered insufficiently reliable11. Here we propose an approach that leverages deep generative models to predict variant pathogenicity without relying on labels. By modelling the distribution of sequence variation across organisms, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (evolutionary model of variant effect) not only outperforms computational approaches that rely on labelled data but also performs on par with, if not better than, predictions from high-throughput experiments, which are increasingly used as evidence for variant classification12-16. We predict the pathogenicity of more than 36 million variants across 3,219 disease genes and provide evidence for the classification of more than 256,000 variants of unknown significance. Our work suggests that models of evolutionary information can provide valuable independent evidence for variant interpretation that will be widely useful in research and clinical settings.
• CITATION : Frazer J, Notin P, Dias M, Gomez A, ...&, Marks DS. (2021) Disease variant prediction with deep generative models of evolutionary data Nature, 599 (7883) 91-95. doi:10.1038/s41586-021-04043-8. PMID 34707284
• JOURNAL_INFO : Nature ; Nature ; 2021 ; 599 ; 7883 ; 91-95
• PUBMED_LINK : 34707284

ProGen2

• NAME : ProGen2
• SHORT NAME : ProGen2
• URL : https://github.com/salesforce/progen
• FULL NAME : ProGen2
• KEYWORDS : protein design, LLM
• USE : sequence generation
• DESCRIPTION : Generative protein design using LLMs trained on protein sequences.
• CITATION : Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. ProGen2: Exploring the boundaries of protein language models. Cell Syst. 14, 968-978.e3 (2023).
• PUBMED_LINK : 37909046

ProtBERT

• NAME : ProtBERT
• SHORT NAME : ProtBERT
• URL : https://huggingface.co/Rostlab/prot_bert
• FULL NAME : ProtBERT
• KEYWORDS : protein LM, transformer, embeddings
• USE : feature extraction
• DESCRIPTION : BERT-based protein language model for downstream functional tasks.
• CITATION : Elnaggar A. et al. (2021) ProtTrans: towards cracking the language of lifes code through self-supervised deep learning and high performance computing. IEEE Trans. Pattern Anal. Mach. Intell., 1, 1.
• PUBMED_LINK : 34232869

ProteinBERT

• NAME : ProteinBERT
• CITATION : Brandes, N., Ofer, D., Peleg, Y., Rappoport, N. & Linial, M. ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics 38, 2102–2110 (2022).
• PUBMED_LINK : 35020807

Functional Annotation

ClinVar

• NAME : ClinVar
• SHORT NAME : ClinVar
• URL : https://www.ncbi.nlm.nih.gov/clinvar/
• FULL NAME : ClinVar
• KEYWORDS : pathogenicity, variant, clinical
• USE : clinical annotation
• DESCRIPTION : Archive of clinically relevant variants with interpretations.
• CITATION : Landrum, M. J. et al. ClinVar: updates to support classifications of both germline and somatic variants. Nucleic Acids Res. 53, D1313–D1321 (2025).
• PUBMED_LINK : 39578691

dbNSFP v4

• NAME : dbNSFP v4
• SHORT NAME : dbNSFP
• URL : https://sites.google.com/site/jpopgen/dbNSFP
• FULL NAME : Database for Nonsynonymous SNPs’ Functional Predictions
• KEYWORDS : annotation, variant, missense
• USE : meta-annotation
• DESCRIPTION : Database aggregating functional predictions and annotations for nonsynonymous variants.
• TITLE : dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs
• ABSTRACT : Whole exome sequencing has been increasingly used in human disease studies. Prioritization based on appropriate functional annotations has been used as an indispensable step to select candidate variants. Here we present the latest updates to dbNSFP (version 4.1), a database designed to facilitate this step by providing deleteriousness prediction and functional annotation for all potential nonsynonymous and splice-site SNVs (a total of 84,013,093) in the human genome. The current version compiled 36 deleteriousness prediction scores, including 12 transcript-specific scores, and other variant and gene-level functional annotations. The database is available at http://database.liulab.science/dbNSFP with a downloadable version and a web-service.
• CITATION : Liu X, Li C, Mou C, Dong Y, ...&, Tu Y. (2020) dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs Genome Med., 12 (1) 103. doi:10.1186/s13073-020-00803-9. PMID 33261662
• JOURNAL_INFO : Genome medicine ; Genome Med. ; 2020 ; 12 ; 1 ; 103
• PUBMED_LINK : 33261662

Pathway and Gene Ontology Enrichment

DAVID

• NAME : DAVID
• SHORT NAME : DAVID
• URL : https://david.ncifcrf.gov/
• FULL NAME : Database for Annotation, Visualization and Integrated Discovery
• KEYWORDS : functional enrichment, GO, pathway
• USE : enrichment analysis
• DESCRIPTION : Functional annotation and enrichment analysis platform.
• CITATION : Huang, D. W. et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8, R183 (2007).
• PUBMED_LINK : 17784955

Gene Ontology

• NAME : Gene Ontology
• SHORT NAME : GO
• URL : http://geneontology.org/
• FULL NAME : Gene Ontology
• KEYWORDS : GO terms, pathways
• USE : enrichment analysis
• DESCRIPTION : Controlled vocabulary for gene function classification.
• CITATION : Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
• PUBMED_LINK : 10802651

Structure Prediction

AlphaFold

• NAME : AlphaFold
• URL : https://alphafold.ebi.ac.uk/
• CITATION : Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
• PUBMED_LINK : 31942072

AlphaFold 2

• NAME : AlphaFold 2
• SHORT NAME : AlphaFold
• URL : https://alphafold.ebi.ac.uk/
• FULL NAME : AlphaFold Protein Structure Database
• KEYWORDS : protein structure, deep learning, folding
• USE : structure prediction
• DESCRIPTION : High-accuracy protein structure prediction using deep learning.
• SERVER : EMBL-EBI
• CITATION : Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
• PUBMED_LINK : 34265844

AlphaFold 3

• NAME : AlphaFold 3
• URL : https://alphafold.ebi.ac.uk/
• CITATION : Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
• PUBMED_LINK : 38718835

Variant Effect Prediction

AlphaMissense

• NAME : AlphaMissense
• SHORT NAME : AlphaMissense
• URL : https://github.com/google-deepmind/alphamissense
• FULL NAME : AlphaMissense
• KEYWORDS : missense, pathogenicity, variant effect, deep learning
• USE : variant effect scoring
• DESCRIPTION : Deep learning model predicting pathogenicity of all possible missense variants in human proteins.
• TITLE : Accurate proteome-wide missense variant effect prediction with AlphaMissense
• ABSTRACT : The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.
• CITATION : Cheng J, Novati G, Pan J, Bycroft C, ...&, Avsec Ž. (2023) Accurate proteome-wide missense variant effect prediction with AlphaMissense Science, 381 (6664) eadg7492. doi:10.1126/science.adg7492. PMID 37733863
• JOURNAL_INFO : Science ; Science ; 2023 ; 381 ; 6664 ; eadg7492
• PUBMED_LINK : 37733863

CADD

• NAME : CADD
• SHORT NAME : CADD
• URL : https://cadd.gs.washington.edu/
• FULL NAME : Combined Annotation–Dependent Depletion
• KEYWORDS : genome-wide, deleteriousness, annotation
• USE : prioritization, filtering
• DESCRIPTION : Combined Annotation–Dependent Depletion; integrates multiple annotations to score variant deleteriousness.
• TITLE : A general framework for estimating the relative pathogenicity of human genetic variants
• ABSTRACT : Current methods for annotating and interpreting human genetic variation tend to exploit a single information type (for example, conservation) and/or are restricted in scope (for example, to missense changes). Here we describe Combined Annotation-Dependent Depletion (CADD), a method for objectively integrating many diverse annotations into a single measure (C score) for each variant. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants. We precompute C scores for all 8.6 billion possible human single-nucleotide variants and enable scoring of short insertions-deletions. C scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects and complex trait associations, and they highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method.
• CITATION : Kircher M, Witten DM, Jain P, O'Roak BJ, ...&, Shendure J. (2014) A general framework for estimating the relative pathogenicity of human genetic variants Nat. Genet., 46 (3) 310-315. doi:10.1038/ng.2892. PMID 24487276
• JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2014 ; 46 ; 3 ; 310-315
• PUBMED_LINK : 24487276

CADD v1.4

• NAME : CADD v1.4
• URL : https://cadd.gs.washington.edu/
• TITLE : CADD: predicting the deleteriousness of variants throughout the human genome
• ABSTRACT : Combined Annotation-Dependent Depletion (CADD) is a widely used measure of variant deleteriousness that can effectively prioritize causal variants in genetic analyses, particularly highly penetrant contributors to severe Mendelian disorders. CADD is an integrative annotation built from more than 60 genomic features, and can score human single nucleotide variants and short insertion and deletions anywhere in the reference assembly. CADD uses a machine learning model trained on a binary distinction between simulated de novo variants and variants that have arisen and become fixed in human populations since the split between humans and chimpanzees; the former are free of selective pressure and may thus include both neutral and deleterious alleles, while the latter are overwhelmingly neutral (or, at most, weakly deleterious) by virtue of having survived millions of years of purifying selection. Here we review the latest updates to CADD, including the most recent version, 1.4, which supports the human genome build GRCh38. We also present updates to our website that include simplified variant lookup, extended documentation, an Application Program Interface and improved mechanisms for integrating CADD scores into other tools or applications. CADD scores, software and documentation are available at https://cadd.gs.washington.edu.
• COPYRIGHT : http://creativecommons.org/licenses/by/4.0/
• CITATION : Rentzsch P, Witten D, Cooper GM, Shendure J, ...&, Kircher M. (2019) CADD: predicting the deleteriousness of variants throughout the human genome Nucleic Acids Res., 47 (D1) D886-D894. doi:10.1093/nar/gky1016. PMID 30371827
• JOURNAL_INFO : Nucleic acids research ; Nucleic Acids Res. ; 2019 ; 47 ; D1 ; D886-D894
• PUBMED_LINK : 30371827

CADD v1.6 (CADD-Splice)

• NAME : CADD v1.6 (CADD-Splice)
• URL : https://cadd.gs.washington.edu/
• CITATION : Rentzsch, P., Schubach, M., Shendure, J. & Kircher, M. CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 13, 31 (2021).
• PUBMED_LINK : 33618777

CADD v1.7

• NAME : CADD v1.7
• URL : https://cadd.gs.washington.edu/
• CITATION : 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. 52, D1143–D1154 (2024).
• PUBMED_LINK : 38183205

M-CAP

• NAME : M-CAP
• SHORT NAME : M-CAP
• URL : https://bejerano.stanford.edu/mcap/
• FULL NAME : Mendelian Clinically Applicable Pathogenicity
• KEYWORDS : missense, clinical
• USE : clinical scoring
• DESCRIPTION : Rare missense pathogenicity classifier for clinical interpretation.
• CITATION : Jagadeesh, K. A. et al. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat. Genet. 48, 1581–1586 (2016).
• PUBMED_LINK : 27776117

MVP

• NAME : MVP
• SHORT NAME : MVP
• URL : https://figshare.com/articles/dataset/Predicting_pathogenicity_of_missense_variants_by_deep_learning/13204118
• FULL NAME : Missense Variant Pathogenicity prediction
• KEYWORDS : deep residual network, pathogenic missense variant
• DESCRIPTION : A new prediction method that uses deep residual network to leverage large training data sets and many correlated predictors
• CITATION : Qi, H. et al. MVP predicts the pathogenicity of missense variants by deep learning. Nat. Commun. 12, 510 (2021).
• PUBMED_LINK : 33479230

MetaLR / MetaSVM

• NAME : MetaLR / MetaSVM
• SHORT NAME : MetaLR
• URL : https://www.ncbi.nlm.nih.gov/clinvar/docs/scoreinfo/
• FULL NAME : MetaLR / MetaSVM
• KEYWORDS : ensemble, missense
• USE : prioritization
• DESCRIPTION : Ensemble pathogenicity scores integrating multiple annotations.
• CITATION : Dong, C. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24, 2125–2137 (2015).
• PUBMED_LINK : 25552646

MutationAssessor

• NAME : MutationAssessor
• SHORT NAME : MutationAssessor
• URL : http://mutationassessor.org/
• FULL NAME : MutationAssessor
• KEYWORDS : conservation, function
• USE : variant effect
• DESCRIPTION : Predicts functional impact based on evolutionary conservation.
• CITATION : Su, Y. et al. MutationAssessor in cBioPortal. bioRxivorg (2025) doi:10.1101/2025.08.10.669566.
• PUBMED_LINK : 40832239

PolyPhen-2

• NAME : PolyPhen-2
• SHORT NAME : PolyPhen-2
• URL : http://genetics.bwh.harvard.edu/pph2/
• FULL NAME : Polymorphism Phenotyping v2
• KEYWORDS : missense, conservation
• USE : variant scoring
• DESCRIPTION : Predicts functional impact of amino acid substitutions.
• CITATION : Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2. Curr. Protoc. Hum. Genet. 76, 7.20.1-7.20.41 (2013).
• PUBMED_LINK : 23315928

PrimateAI-3D

• NAME : PrimateAI-3D
• SHORT NAME : PrimateAI-3D
• URL : https://www.broadinstitute.org
• FULL NAME : PrimateAI-3D
• KEYWORDS : deep learning, primate, missense
• USE : clinical variant scoring
• DESCRIPTION : DL model trained on primate variation + 3D structure.
• CITATION : Gao, H. et al. The landscape of tolerated genetic variation in humans and primates. Science 380, eabn8153 (2023).
• PUBMED_LINK : 37262156

REVEL

• NAME : REVEL
• SHORT NAME : REVEL
• URL : https://sites.google.com/site/revelgenomics/
• FULL NAME : Rare Exome Variant Ensemble Learner
• KEYWORDS : ensemble, missense
• USE : pathogenicity scoring
• DESCRIPTION : Ensemble method integrating multiple tools to predict pathogenicity.
• CITATION : Ioannidis, N. M. et al. REVEL: An ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 99, 877–885 (2016).
• PUBMED_LINK : 27666373

SIFT

• NAME : SIFT
• SHORT NAME : SIFT
• URL : https://sift.bii.a-star.edu.sg/
• FULL NAME : Sorting Intolerant From Tolerant
• KEYWORDS : conservation, missense
• USE : variant scoring
• DESCRIPTION : Predicts whether substitutions affect protein function.
• CITATION : Ng, P. C. & Henikoff, S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).
• PUBMED_LINK : 12824425

UNEECON

• NAME : UNEECON
• SHORT NAME : UNEECON
• URL : https://github.com/yifei-lab/UNEECON
• DESCRIPTION : UNEECON is a statistical method for inferring deleterious mutations and constrained genes in human and potentially other species.
• CITATION : Huang, Y.-F. Unified inference of missense variant effects and gene constraints in the human genome. PLoS Genet. 16, e1008922 (2020).
• PUBMED_LINK : 32667917

VEST4

• NAME : VEST4
• SHORT NAME : VEST4
• URL : https://www.cravat.us/
• FULL NAME : Variant Effect Scoring Tool v4
• KEYWORDS : ML, SNV
• USE : variant scoring
• DESCRIPTION : Machine learning pathogenicity score for SNVs.
• CITATION : Carter, H., Douville, C., Stenson, P. D., Cooper, D. N. & Karchin, R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics 14 Suppl 3, S3 (2013).
• PUBMED_LINK : 23819870