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["Category","Name","Prediction model","Training data","Information used","DOI","Method description","Features"]
["B","SIFT","position specific scoring matrix","SWISS-PROT/TrEMBL, 1,750 deleterious and 2,254 tolerant nsSNVs of E. coli LacI gene","sequence homology based on PSI-BLAST","10.1093/nar/gkg509, 10.1038/nprot.2009.86","statistical method using position-specific scoring matrix with dirichlet priors","sequence based"]
["B","LRT","likelihood ratio test of codon neutrality","coding sequences of 32 vertebrate species from Ensembl","sequence homology","10.1101/gr.092619.109","Log ratio test","sequence based"]
["B","MutationAssessor","combinatorial entropy formalism","homologous sequences from Uniprot identified by BLAST; SNVs from COSMIC database","sequence homology of protein families and subfamilies within and between species","10.1093/nar/gkr407","Measurement of residue conservation through use of the entropy of the residue distribution in an alignment column","sequence based"]
["B","FATHMM","hidden Markov models","homologous sequences from UniRef90, SUPERFAMILY and Pfam; SNVs from HGMD and UniProt","sequence homology","10.1002/humu.22225","Hidden Markov Model","sequence based"]
["B","PROVEAN","data alignment score","SNVs from UniProt/HUMSAVAR","sequence homology","10.1371/journal.pone.0046688","BLOSUM62-base prediction score calculated over clusters of homologous and distantly related sequences.","sequence based"]
["Meta","VEST3","random forest","SNVs from HGMD and the exome sequencing project","86 sequence features","10.1186/1471-2164-14-S3-S3","Machine learning approach","sequence based"]
["Meta","MetaSVM","radial kernel support vector machine","36,192 SNVs from UnIprot","nine prediction scores and allele frequencies in 1000G","10.1093/hmg/ddu733","Integration of a Support Vector Machine and Logistic Regression","annotation"]
["Meta","MetaLR","logistic regression","36,192 SNVs from UnPprot","nine prediction scores and allele frequencies in 1000G","10.1093/hmg/ddu733","Integration of a Support Vector Machine and Logistic Regression","annotation"]
["Meta","M.CAP","gradient boosting tree classifier","63,418 pathogenic variants from HGMD and 3,268,665 predominantly benign variants from ExAC","annotations","10.1038/ng.3703","Gradient boosting tree classifier","sequence based, annotation"]
["ML","POLYPHEN2","naive Bayes classifier (both)","UniProtKB/UniRef100, PDB/DSSP, UCSC alignments of 45 vertebrate genomes; 5,564 Mendelian disease mutations and 7,539 divergence SNVs from close mammalian homolog proteins (HDIV), 22,196 disease-associated SNVs and 21,119 common SNVs (HVAR)","eight sequence-based and three structure-based predictive features (both)","10.1038/nmeth0410-248","Naive bayes approach coupled with entropy-based discretization","sequence based, structure based"]
["ML","MutationTaster","naive Bayes classifier","UniProt, homologous genes in humans and 10 other species, dbSNP, HapMap; SNVs from 1000 G (1000 Genomes Project), HGMD","DNA sequence conservation, splice site prediction, mRNA stability prediction, protein features; regulatory features,","10.1038/nmeth0810-575","Naive bayes model based on integrated data source","sequence based, annotation"]
["ML","CADD","linear kernel support vector machine","16,627,775 “observed” variants (Ensembl) and 49,407,057 “simulated” variants","63 annotations (949 features)","10.1038/ng.2892","Machine learning algorithm","annotation"]
["ML","DANN","deep neural network","16,627,775 “observed” variants (Ensembl) and 49,407,057 “simulated” variants","63 annotations (949 features)","10.1093/bioinformatics/btu703","Deep Neural Network","annotation"]
["ML","fathmm-MKL coding","multiple kernel learning","SNVs from HGMD and 1000G","Conservation, epigenetic, signals","10.1093/bioinformatics/btv009","Multiple Kernel Learning","sequence based, annotation"]
["ML","GenoCanyon","unsupervised statistical learning","collection of the comparative genomic conservation scores and biochemical signals obtained from the ENCODE project","conservation,, biochemical annotations","10.1038/srep10576","Functional prediction score based on conservation and biochemical annotations using an unsupervised statistical learning.","annotation"]
["Cons","GERP++","maximum likelihood evolutionary rate estimation","TBA alignments of hg18 to 43 other vertebrate species from UCSC genome browser","multiple alignments and phylogenetic tree","10.1038/nmeth0410-250, 10.1371/journal.pcbi.1001025","Rejected substitutions score computation to infer the constrained region","sequence based"]
["Cons","phyloP7way vertebrate","distributions of the number of substitutions based on phylogenetic hidden Markov model","genomes of 33 placental mammals (joined with 20p until 3.0); Genomes of seven vertebrate (3.0 and after)","multiple alignments and phylogenetic tree","10.1101/gr.097857.109","Exact P value computation under a continuous markov substitution model","sequence based"]
["Cons","phyloP20way mammalian","distributions of the number of substitutions based on phylogenetic hidden Markov model","genomes of 33 placental mammals (joined with 7p until 3.0); genomes of 20 mammals (3.0 and after)","multiple alignments and phylogenetic tree","10.1101/gr.097857.109","Exact P value computation under a continuous markov substitution model","sequence based"]
["Cons","phastCons7way vertebrate","two-state phylogenetic hidden Markov model","genome of seven vertebrates","multiple alignments and phylogenetic tree","10.1101/gr.3715005","Phylogenetic Hidden Markov Model","sequence based"]
["Cons","phastCons 20 way mammalian","two-state phylogenetic hidden Markov model","genome of 20 mammals","multiple alignments and phylogenetic tree","10.1101/gr.3715005","Phylogenetic Hidden Markov Model","sequence based"]
["Cons","SiPhy 29way logOdds","inference of nucleotide substitution pattern per site","genomes of 29 mammals","multiple alignments","10.1093/bioinformatics/btp190","Likelihood estimation under a hidden markov substitution model","sequence based"]
["For prediction tools, ``Category''  indicates wether the tool is based on biological principles (B), machine learning (ML), or meta-analysis (Meta). Cons indicates conservation scoring tools."]
["Table generated integrating 10.1002/humu.21517 10.1002/humu.22376 10.1002/humu.22932 and 10.1155/2013/675851 together with references cited in the table itself."]

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["Category","Name","Pearson's R","p.value"]
["B","SIFT",-0.492693593126496,7.86520025782793E-19]
["B","LRT",-0.485741571754052,2.76399965889224E-18]
["B","MutationAssessor",-0.572624715843703,5.22338233837103E-26]
["B","FATHMM",-0.0536965836887436,0.185355542268365]
["B","PROVEAN",-0.545998196818865,1.85686019850398E-23]
["Meta","VEST3",-0.699096961749003,1.08135986842866E-42]
["Meta","MetaSVM",0.285094212280664,0.999999383398135]
["Meta","MetaLR",-0.48159505258479,5.77078023661405E-18]
["Meta","M.CAP",-0.255011339231556,8.0926278306164E-6]
["ML","POLYPHEN2 HDIV",-0.67242439660367,1.66902251695978E-38]
["ML","POLYPHEN2 HVAR",-0.648259091852265,4.53060629189368E-35]
["ML","MutationTaster",-0.499081347907845,2.41608723730069E-19]
["ML","CADD",-0.594735642839247,1.78136084420956E-28]
["ML","DANN",-0.388038042530044,8.50663210202117E-12]
["ML","fathmm-MKL coding",-0.434074935486277,1.35352652820997E-14]
["ML","GenoCanyon",-0.282232976648075,7.95470742116967E-7]
["n","GERP++",-0.404626333163986,9.34458177414145E-13]
["Cons","phyloP7way vertebrate",-0.440882206460522,4.78944876205002E-15]
["Cons","phyloP20way mammalian",-0.214119202909645,1.53799695949381E-4]
["Cons","phastCons7way vertebrate",-0.486189444312042,2.5512137848642E-18]
["Cons","phastCons 20 way mammalian",-0.255779857507571,7.34903887695436E-6]
["Cons","SiPhy 29way logOdds",-0.388856282708572,7.65047825930055E-12]

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["Name","URL","Qualitative levels","Qualitative threshold","description","Extra info","Version added"]
["SIFT","https://sift.bii.a-star.edu.sg/ http://sift.jcvi.org","Deleterious) T(olerated)","rankscore > 0.395","calculates bayesian probabilities: p(mutation|disease)","By using PSI-BLAST, a prediction can be made on the effect of a nonsynonymous mutation within a protein.",1.0]
["POLYPHEN2","https://genetics.bwh.harvard.edu/pph2/dokuwiki/start","B(enign) D(amaging probably) P(ossibly damaging)","rankscore > 0.3528/0.45833 (binary, HDIV/HVAR)","calculates probabilities as ratio between p(subst) at each site for each amino and p(must_freq_subst)","Predicts potential impact of a nonsynonymous variant using comparative and physical characteristics.",1.0]
["VEST3","https://wiki.chasmsoftware.org/ http://karchinlab.org/apps/appVest.html","-","-","uses random forest to identify likely functional missense mutations","to determine the probability that a missense mutation will impair the functionality of a protein.",3.0]
["MetaSVM","https://wglab.org/members/15-member-detail/36-coco-dong","D(amaging) [T(olerated)]","rankscore > 0.82268","Our support vector machine (SVM) based ensemble prediction score, which incorporated 10 scores (SIFT, PolyPhen-2 HDIV, PolyPhen-2 HVAR, GERP++, MutationTaster, Mutation Assessor, FATHMM, LRT, SiPhy, PhyloP) and the maximum frequency observed in the 1000 genomes populations.","to integrate nine deleterious prediction scores of missense mutations.",3.0]
["LRT","https://www.genetics.wustl.edu/jflab/lrt_query.html  http://genome.cshlp.org/content/19/9/1553.long","D(eleterious N(eutral) [U(nknown)]","not possible, because pred not solely determined by score","?","?",1.0]
["MutationTaster","https://www.mutationtaster.org","A(disease_causing_automatic)  D(disease_causing) N(polymorphism)","rankscore > 0.31709 (D to N)","?","?",1.0]
["MutationAssessor","https://mutationassessor.or","{predicted functional} H(igh)  M(medium) {predicted non-func} N(eutral) L(ow)","rankscore>0.941 H rankscore> 0.61456 M rankscore>0.26284 L else N","The computational protocol performs the analysis as follows: given a mutated protein name and a mutated residue position, it searches for sequence homologs, builds a multiple sequence alignment, clusters sequences into subfamilies and scores a mutation by global and sub-family specific conservation patterns. Mutations affecting either type of conserved residue are likely to be functional.","and estimate the mutation impact using the difference of the entropy caused by the mutation",2.0]
["FATHMM","https://fathmm.biocompute.org.uk","D(amaging) [T(olerated)]","rankscore>0.81332","NA","to predict the functional consequences of SNVs in coding and noncoding variants through a web server.",2.0]
["PROVEAN","https://provean.jcvi.org","D(amaging) N(eutral)","rankscore>0.543","Search for homologous and distantly related sequences, cluster them, select most related clusters, compute a BLOSUM62-based delta score, and make an unbiased average of it.","?",3.0]
["MetaLR","https://wglab.org/members/15-member-detail/36-coco-dong","D(amaging) [T(olerated)]","rankscore>0.81113","Our logistic regression (LR) based ensemble prediction score, which incorporated 10 scores (SIFT, PolyPhen-2 HDIV, PolyPhen-2 HVAR, GERP++, MutationTaster, Mutation Assessor, FATHMM, LRT, SiPhy, PhyloP) and the maximum frequency observed in the 1000 genomes populations.","to integrate nine deleterious prediction scores of missense mutations.",3.0]
["M.CAP","https://bejerano.stanford.edu/MCAP","D(amaging) [T(olerated)]","SCORE>0.025","?","?",3.3]
["CADD","https://cadd.gs.washington.edu/","-",null,"?","to score all possible 8.6 million substitutions in the human reference genome from 1 to 99 based on known and simulated functional variants.",3.0]
["DANN","https://cbcl.ics.uci.edu/public_data/DANN/","-","-","DANN is a functional prediction score retrained based on the training data of CADD using deep neural network.","DANN is a functional prediction score retrained based on the training data of CADD using",3.0]
["fathmm-MKL coding","https://fathmm.biocompute.org.uk","D(amaging) N(eutral)","-","?","?",3.0]
["GenoCanyon","https://genocanyon.med.yale.edu","-","rankscore>0.28317","?","?",3.2]
["GERP++ RS","https://mendel.stanford.edu/sidowlab/downloads/gerp/index.html","-","-","?","?",2.0]
["phyloP7way vertebrate","https://compgen.cshl.edu/phast/background.php","-","-","?","?",3.0]
["phyloP20way mammalian","https://compgen.cshl.edu/phast/background.php","-","-","?","?",3.0]
["phastCons7way vertebrate","https://github.com/CshlSiepelLab/phast","-","-","?","a type of statistical model that considers both the process changes from one site to the next",3.0]
["phastCons20way mammalian","https://github.com/CshlSiepelLab/phast","-","-","?","a type of statistical model that considers both the process changes from one site to the next",3.0]
["SiPhy 29way logOdds","http://portals.broadinstitute.org/genome_bio/siphy/index.html","-","-","?","?",2.0]