FilterQuery - geneyx/geneyx.analysis.api GitHub Wiki

Category FIELD KEYWORD DESCRIPTION TYPE Example QUERY Query DATA
LOCATION CHR #CHR Chromosome String ‘1’ #CHR==’1’
POS #POS Position Interger 69063 #POS==69063
#CHRPOS Chromosome:Position String ‘1:69063’ #CHRPOS== ‘1:69063’
GENE GENE #GENE Gene Name String data[#Gene]=== true {“BRCA1”:true,”BRCA2”:true,”TNNC1”:true}
GENOMIC AND GENETIC DATA REF #REF Reference Allele String ‘A’ #REF==’A’
ALT #ALT Alternate Allele String ‘T’ #ALT==’T’
AA #AA Amino Acid String ‘M289T’ #AA==’M289T’
HGVS #HGVS Representation of the variant in HGVS (Human Genome Variation Society) nomenclature String ‘c.6390C>T / p.Gly2130Gly’ #HGVS==’c.6390C>T / p.Gly2130Gly’
ZYG #GT Genotype Proband String ‘HET’, ‘HOM’, ‘HEM’,’REF’ #GT==’HET’
#FATHER_GT Genotype of Father String ‘HET’, ‘HOM’, ‘HEM’,’REF’ #FATHER_GT==’HET’
#MOTHER_GT Genotype of Mother String ‘HET’, ‘HOM’, ‘HEM’,’REF’ #MOTHER_GT==’HET’
REFSEQ #REFSEQ The transcript ID(s) relevant to the effect on the protein. This track contains RefSeq Gene transcripts annotated by the NCBI Homo sapiens Annotation Release. String ‘NM_001458.5’ #REFSEQ==’NM_001385640.1’
EXON # #EXON Exon # – The impacted exon of the gene integer 1 N/A
CODON #CODON Codon change String ‘ggC/ggT’ #CODON==’ggC/ggT’
DBSNP #RS_NUMBER Known identifier (dbSNP RSID) String ‘rs746751083’ #RS_NUMBER==’rs746751083’
DBSNP VERSION #DBVER The earliest dbSNP version which included the variant Interger 144 #DBVER>144
RMSK #RMSK Percentage of bases overlapping genomic repeats Float (0-100) 10 #RMSK>10
PAF #PAF Phenotype Allele Frequency Float 10 #PAF>10
MOTHER_AFF #MOTHER_AFF Affection status in Mother String ‘Unkown’,’Affected’,’Unaffected’ #MOTHER_AFF==’Affected’
FATHER_AFF #FATHER_AFF Affection status in Father String ‘Unkown’,’Affected’,’Unaffected’ #FATHER_AFF==’Affected’
ACMG ACMG #ACMG ACMG Classification String ‘Pathogenic’, ‘Likely Pathogenic’, ‘Variant of Unknown Significance’, ‘Likely Benign’, ‘Benign’ #ACMG==’Pathogenic’
#ACMGLEVEL The ACMG class level in number integer “Benign’: 1,’Likely benign’: 2,’Uncertain significance’: 3,’Uncertain-significance’: -3,’Likely pathogenic’: 4,’Pathogenic’: 5,’0’: 0” #ACMGLEVEL==1
DOM/REC #ACMG_REC_DOM_LEVEL The MAX acmg level between REC/DOM - for fast track only integer 1 #ACMG_REC_DOM_LEVEL>1
INT_DOMAIN #INT_DOMAIN Interger Domain integer 1 #INT_DOMAIN>1
VARIANT CALLING Q&R Q&R #QR_Score “Classifies calls by quality. Low = coverage < 10x and GQ < 15; Med = coverage < 20x and GQ < 50; High = coverage >20x and GQ = 50.” String ‘High’, ‘Med’, ‘Low’ #QR_Score==’High’
DEPTH #DEPTH Read depth (total reads) Interger 50 #DEPTH>50
DP2 #DP2 Read Depth (Ref/Alt) String ‘12,24’ #DP2==’12,24’
%ALT #ALT_PERCENT The percentage of reads showing the alternative allele  Interger 48.50 #ALT_PERCENT>48.50
GQ #GQ Genotype Quality Interger 50 #GQ>50
FILTER #FILTER Quality filter status based on the variant caller String ‘PASS’ #FILTER==’PASS’
PL N/A Phred-scaled genotype likelihoods (HOM REF, HET, HOM ALT) N/A N/A N/A
AMP N/A Amplification score (coverage/median coverage)  N/A N/A N/A
CLINICAL EVIDENCE PHENO #PHENO Phenotype score float N/A N/A
MATCHED COUNT #MATCHED_PHENO_CNT Number of matched phenotypes integer N/A N/A
MATCHED #MATCHED_PHENO The matched phenotypes array [‘brain’,’bone’] N/A
CLINVAR #CV_CLINSIG The ClinVar database from NCBI String ‘Pathogenic’, ‘Likely Pathogenic’, ‘Variant of Unknown Significance’, ‘Likely Benign’, ‘Benign’ #CV_CLINSIG==’Pathogenic’
OMIM #OMIM_GENE OMIM © is a comprehensive, authoritative compendium of human genes and genetic phenotypes that is freely available and updated daily. Boolean ‘BRCA1’ data[#OMIM_GENE]=== true {“BRCA1”:true,”BRCA2”:true,”TNNC1”:true}
OMIM INHERITANCE #OMIM_GENE_ABBR OMIM © is a comprehensive, authoritative compendium of human genes and genetic phenotypes that is freely available and updated daily. OMIM is authored and edited at the McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, under the direction of Dr. Ada Hamosh. Its official home is omim.org. This track displays inheritance information. Includes: Autosomal Dominant (AD), Autosomal Recessive (AR), Multifactorial (MG), Somatic Mutation (SO). string N/A N/A
CIVIC N/A N/A N/A N/A N/A
PUBS #MM MasterMind Document Count Interger 11 #MM>11
IN HOUSE VARIANT N/A N/A N/A N/A N/A
GENE N/A N/A N/A N/A N/A
AF (%) #LOCAL_AF The allele frequency of the variant in all samples of the curreThe allele frequency of the variant in all the samples of the current account. Interger 0 #LOCAL_AF>10
#LOCAL HET #LOCAL_HET The count of samples with this heterozygous variant in this account. Interger 0 #LOCAL_HET>0
#LOCAL HOM #LOCAL_HOM The count of samples with this homozygous variant in this account. Interger 0 #LOCAL_HOM>0
#LOCAL HEM #LOCAL_HEM N/A N/A N/A N/A
EFFECT & PREDICTION EFFECT #EFFECT The effect of the variant on the protein (including splicing effects) String ‘Downstream’,’Exon’,’Exon, Splice Site region’,’Frameshift’,’Frameshift, Intron, Splice site donor’, ‘Splice site region’,’Frameshift, Nonsense’,’Frameshift, Splice site’,’Frameshift, Stop readthrough’,’Inframe indel’,’Inframe indel, Splice site region’,’Intergenic’,’Intron’,’Intron, Splice site acceptor’,’Intron, Splice site donor’,’Intron, Splice site region’,’Missense’,’Missense + Inframe indel’,’Missense, Splice site region’,’Nonsense’,’Synonymous’,’Splice site region’,’Splice site region, nonsynonymous’,’Splice site region, synonymous’,’Start gained’,’Start gained, UTR’,’Start lost’,’Stop change’,’Stop readthrough’,’UTR_3_Prime’,’UTR_5_Prime’,’Upstream’,” #EFFECT==’Missense’
SEV #SEVERITY Severity (High, Med, Low) of the impact on the protein based on the Effect and, in case of missense, the predicted damage to the protein. String ‘Low’, ‘Med’, ‘High’ #SEVERITY==’High’
CADD(PHRED) #CADD_PRED Combined Annotation Dependent Depletion (CADD) is a tool for scoring the deleteriousness of single nucleotide variants as well as insertion/deletions variants in the human genome. CADD Scores are computed for all SNVs and ~20 million known insertions/deletions.PHRED-scaled scores (“scaled C-scores”) ranging from 1 to 99, based on the rank of each variant relative to all possible 8.6 billion substitutions in the human reference genome. For example, reference genome single nucleotide variants at the 10th-% of CADD scores are assigned a PHRED Score of 10, top 1% to PHRED-20, top 0.1% to PHRED-30, etc. N/A N/A N/A
CADD(RAW) #CADD_RAW Raw CADD scores, or “C-scores”, come straight from the Support Vector Machine model. These values have no absolute unit of meaning. However, raw values do have relative meaning, with higher values indicating that a variant is more likely to be simulated (or “not observed”) and therefore more likely to have deleterious effects. N/A N/A N/A
REVEL #REVEL_SCORE Rare Exome Variant Ensemble Learner (REVEL) is an ensembl method for predicting the pathogenicity of missense variants based on a combination of scores from 13 individual tools: MutPred, FATHMM v2.3, VEST 3.0, PolyPhen-2, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP++, SiPhy, phyloP, and phastCons. Interger 10 #REVEL_SCORE>0.1
SPLICE-AI #SAIS SpliceAI is a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. In the paper, Jaganathan et al 2019, a detailed characterization is provided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs. N/A N/A N/A
PHYLOP #PHYLOP  PhyloP (phylogenetic p-values) conservation score based on the multiple alignments of 20 mammalian genomes (including human). The larger the score, the more conserved the site. Scores range from -20.0 to 10.003.easures evolutionary conservation at individual alignment sites N/A N/A N/A
GERP NR #GERP_NR Genomic Evolutionary Rate Profiling Neutral Rate (GERP NR) is a method for producing position-specific estimates of evolutionary constraint using maximum likelihood evolutionary rate estimation. Integer 1 #GERP_NR>1
GERP RS #GERP_RS Genomic Evolutionary Rate Profiling Rejected Substitutions (GERP NR) is a method for producing position-specific estimates of evolutionary constraint using maximum likelihood evolutionary rate estimation. Interger 1 #GERP_RS>1
LRT PRED #LRT_PRED Likelihood Ratio Test (LRT) prediction, D(eleterious), N(eutral) or U(nknown). String ‘D’,’N’,’U’ #LRT_PRED==’D’
MUTTASTER #MUTATIONTASTER_PRED MutationTaster prediction, “A” (“disease_causing_automatic”), “D” (“disease_causing”), “N” (“polymorphism, [probably harmless]”) or “P” (“polymorphism_automatic, [known to be harmless]”). String ‘D’ #MUTATIONTASTER_PRED==’D’
POLYPHEN2HDIV #POLYPHEN2_HDIV_PRED Polyphen2 prediction based on HumDiv, “D” (“probably damaging”), “P” (“possibly damaging”), and “B” (“benign”). String ‘D’ #POLYPHEN2_HDIV_PRED==’D’
POLYPHEN2HVAR #POLYPHEN2_HVAR_PRED Polyphen2 prediction based on HumVar, “D” (“probably damaging”), “P” (“possibly damaging”) and “B” (“benign”). String ‘D’ #POLYPHEN2_HVAR_PRED==’D’
SIFT #SIFT_SCORE Sort intolerated from tolerated (SIFT) scores range from 0 to 1. The smaller the score the more likely the SNP has damaging effect. Deleterious (sift<=0.05); tolerated (sift>0.05). Integer 0 #SIFT_SCORE>0
UNIPROT #UNIPROT_ACC The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The entry is the accession of the protein. String ‘P07602’ “#UNIPROT_ACC==’P07602’
ADA SCORE #ADA_SCORE Ensembl prediction score based on ada-boost. Ranges 0 to 1. The larger the score the higher probability the scSNV will affect splicing. The suggested cutoff for a binary prediction (affecting splicing vs. not affecting splicing) is 0.6. Integer 0.1 #ADA_SCORE>0.1
RF SCORE #RF_SCORE Ensembl prediction score based on random forests. Ranges 0 to 1. The larger the score the higher probability the scSNV will affect splicing. The suggested cutoff for a binary prediction (affecting splicing vs. not affecting splicing) is 0.6. Interger 0.5 #RF_SCORE>0.5”
LEN #SV_LEN The CNV/SV length Interger 123 #SV_LEN>123
%RMSK #SV_RSML_PERCENT The percentage of bases overlapping genomic repeats Float (0-100) 10 #SV_RSML_PERCENT>10
M.EXONS #SV_EXONS Maximum nuber of exons affected within genes by this CNV Interger 10 #SV_EXONS>10
ENHS #SV_ENHANCERS Number of enhancers affected by this CNV Interger 10 #SV_ENHANCERS>1
GENES #SV_GENES Number of genes affected by this CNV Interger 10 #SV_GENES>1
MAX AF (GAIN) #SV_MAX_AF_GAIN Maximum allele frequency for duplications in reference populations Interger (<100) 10 #SV_MAX_AF_GAIN <10
MAX AF (LOSS) #SV_MAX_AF_LOSS Maximum allele frequency for deletions in reference populations Interger (<100) 10 #SV_MAX_AF_LOSS <10
FREQUENCY MAX AF(%) #MAX_AF Maximum observed allele frequency in 1000 Genomes, ESP and gnomAD Interger 1 #MAX_AF>1
#HOM #HOM_TOTAL The count of samples with this homozygous allele in gnomAD Interger 10 #HOM_TOTAL<10
#HET #HET_TOTAL The count of samples with this heterozygous allele in gnomAD  Interger 2 N/A
#HEM #HEM_TOTAL The count of samples with this hemizygous allele in gnomAD  Interger 3 #HEM_TOTAL>3
MitoMap AF (%) #MM_AF The allele frequency of the variant in MitoMap  Float (0-100) .2 #MM_AF>.2
Internal Allele Frequency in WGS(%) #INT_WGS_AF Internal allele frequency of this variant in WGS  Float (0-100) .2 #INT_WGS_AF>.2
Internal Allele Frequency in WES(%) #INT_WES_AF Internal allele frequency of this variant in WES  Float (0-100) .2 #INT_WES_AF>.2
1K GENOME AF (%) #1000GP1_AF The allele frequency of the variant in 1000 Genome  Interger .2 #1000GP1_AF>.2
ESP AFRICAN AF (%) #ESP6500_AA_AF The allele frequency of the variant in ESP (Exome Sequencing Project) African Americans population  Interger .3 #ESP6500_AA_AF>.3
ESP EUROPEAN AF (%) #ESP6500_EA_AF The allele frequency of the variant in ESP (Exome Sequencing Project) European population Interger .4 #ESP6500_EA_AF>.4
DBSNP AF (%) #DBGMAF The minor allele frequency of the variant in dbSNP  Interger .5 #DBGMAF>.5
HAN AF (%) #CVR_AF The allele frequency of the variant in CONVERGE project for Han Chinese population Interger .6 N/A
GNE AF (%) #GNE_AF The gnomAD Exomes allele frequency  Interger .7 #GNE_AF>.7
GNG AF (%) #GNG_AF The gnomAD Genomes allele frequency  Interger .8 #GNG_AF>.8
GNE AMR #GNE_AF_AMR The gnomAD Exomes Latino/Admixed American allele frequency  Interger .9 #GNE_AF_AMR>.9
GNG AMR #GNG_AF_AMR The gnomAD Genomes Latino/Admixed American allele frequency  Interger .10 #GNG_AF_AMR>.1
GNE AFR #GNE_AF_AFR The gnomAD Exomes African/African American allele frequency   Interger .11 #GNE_AF_AFR>.11
GNG AFR #GNG_AF_AFR The gnomAD Genomes African/African American allele frequency  Interger .12 #GNG_AF_AFR>.12
GNE ASJ #GNE_AF_ASJ The gnomAD Exomes Ashkenazi Jewish allele frequency  Interger .13 #GNE_AF_ASJ>.13
GNG ASJ #GNG_AF_ASJ  The gnomAD Genomes Ashkenazi Jewish allele frequency  Interger .14 #GNG_AF_ASJ>.14
GNE EAS #GNE_AF_EAS The gnomAD Exomes East Asian allele frequency  Interger .15 #GNE_AF_EAS>.15
GNG EAS #GNG_AF_EAS  The gnomAD Genomes East Asian allele frequency  Interger .16 #GNG_AF_EAS>.16
GNE FIN #GNE_AF_FIN The gnomAD Exomes European (Finnish) allele frequency Interger .17 #GNE_AF_FIN>.17
GNG FIN #GNG_AF_FIN The gnomAD Genomes European (Finnish) allele frequency Interger .18 #GNG_AF_FIN>.18
GNE NFE #GNE_AF_NFE The gnomAD Exomes European (non-Finnish) allele frequency  Interger .19 #GNE_AF_NFE>.19
GNG NFE #GNG_AF_NFE The gnomAD Genomes European (non-Finnish) allele frequency  Interger .20 #GNG_AF_NFE>.20
GNE OTH #GNE_AF_OTH The gnomAD Exomes Other allele frequency Interger .21 #GNE_AF_OTH>.21
GNE SAS #GNG_AF_SAS The gnomAD Exomes South Asian allele frequency  Interger .22 #GNG_AF_SAS>.22
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