PolyPhenDb objects
PolyPhenDb-class.RdThe PolyPhenDb class is a container for storing a connection to a PolyPhen sqlite database.
Methods
In the code below, x is a PolyPhenDb object.
metadata(x)Returns
x's metadata in a data frame.columns(x)Returns the names of the
columnsthat can be used to subset the data columns. For column descriptions see?PolyPhenDbColumns.keys(x)Returns the names of the
keysthat can be used to subset the data rows. Thekeysvalues are the rsid's.select(x, keys = NULL, columns = NULL, ...)Returns a subset of data defined by the character vectors
keysandcolumns. If nokeysare supplied, all rows are returned. If nocolumnsare supplied, all columns are returned. See?PolyPhenDbColumnsfor column descriptions.duplicateRSID(x)Returns a named list of duplicate rsid groups. The names are the
keys, the list elements are the rsid's that have been reported as having identical chromosome position and alleles and therefore translating into the same amino acid residue substitution.
Details
PolyPhen (Polymorphism Phenotyping) is a tool which predicts the possible impact of an amino acid substitution on the structure and function of a human protein by applying empirical rules to the sequence, phylogenetic and structural information characterizing the substitution.
PolyPhen makes its predictions using UniProt features, PSIC profiles scores derived from multiple alignment and matches to PDP or PQS structural databases. The procedure can be roughly outlined in the following steps, see the references for complete details,
sequence-based characterization of substitution site
calculation of PSIC profile scores for two amino acid variants
calculation of structural parameters and contacts
prediction
PolyPhen uses empirically derived rules to predict that a non-synonymous SNP is
probably damaging : it is with high confidence supposed to affect protein function or structure
possibly damaging : it is supposed to affect protein function or structure
benign : most likely lacking any phenotypic effect
unknown : when in some rare cases, the lack of data do not allow PolyPhen to make a prediction
References
PolyPhen Home: http://genetics.bwh.harvard.edu/pph2/dokuwiki/
Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. Nat Methods 7(4):248-249 (2010).
Ramensky V, Bork P, Sunyaev S. Human non-synonymous SNPs: server and survey. Nucleic Acids Res 30(17):3894-3900 (2002).
Sunyaev SR, Eisenhaber F, Rodchenkov IV, Eisenhaber B, Tumanyan VG, Kuznetsov EN. PSIC: profile extraction from sequence alignments with position-specific counts of independent observations. Protein Eng 12(5):387-394 (1999).
Examples
library(PolyPhen.Hsapiens.dbSNP131)
## metadata
metadata(PolyPhen.Hsapiens.dbSNP131)
#> name
#> 1 Db type
#> 2 Data source
#> 3 Genome
#> 4 Genus and Species
#> 5 Resource URL
#> 6 dbSNP build
#> 7 Creation time
#> 8 RSQLite version at creation time
#> 9 package
#> value
#> 1 PolyPhenDb
#> 2 PolyPhen2
#> 3 hg19
#> 4 Homo sapiens
#> 5 http://genetics.bwh.harvard.edu/pph2/dokuwiki/downloads
#> 6 131
#> 7 2012-03-13 16:16:18 -0700 (Tue, 13 Mar 2012)
#> 8 0.11.1
#> 9 VariantAnnotation
## available rsid's
head(keys(PolyPhen.Hsapiens.dbSNP131))
#> [1] "rs10000692" "rs10001580" "rs10002700" "rs10003238" "rs10003369"
#> [6] "rs10004"
## column descriptions found at ?PolyPhenDbColumns
columns(PolyPhen.Hsapiens.dbSNP131)
#> [1] "AA1" "AA2" "ACC" "AVENHET" "AVENINT"
#> [6] "AVENSIT" "BASEDON" "BFACT" "CODPOS" "COMMENTS"
#> [11] "CPG" "DPROP" "DSCORE" "DVOL" "EFFECT"
#> [16] "HBONDS" "IDENT" "IDPMAX" "IDPSNP" "IDQMIN"
#> [21] "LENGTH" "MAPREG" "MINDHET" "MINDINT" "MINDJNC"
#> [26] "MINDSIT" "NFILT" "NOBS" "NORMACC" "NSTRUCT"
#> [31] "NT1" "NT2" "OAA1" "OAA2" "OACC"
#> [36] "OPOS" "OSNPID" "PDBCH" "PDBID" "PDBPOS"
#> [41] "PFAMHIT" "PHAT" "POS" "PPH2CLASS" "PPH2FDR"
#> [46] "PPH2FPR" "PPH2PROB" "PPH2TPR" "PREDICTION" "REGION"
#> [51] "RSID" "SCORE1" "SCORE2" "SECSTR" "SITE"
#> [56] "SNPID" "TRAININGSET" "TRANSV"
## subset on keys and columns
subst <- c("AA1", "AA2", "PREDICTION")
rsids <- c("rs2142947", "rs4995127", "rs3026284")
select(PolyPhen.Hsapiens.dbSNP131, keys=rsids, columns=subst)
#> RSID AA1 AA2 PREDICTION
#> 1 rs2142947 F L benign
#> 2 rs4995127 F L probably damaging
#> 3 rs4995127 F L possibly damaging
#> 4 rs3026284 G D probably damaging
## retrieve substitution scores
subst <- c("IDPMAX", "IDPSNP", "IDQMIN")
select(PolyPhen.Hsapiens.dbSNP131, keys=rsids, columns=subst)
#> RSID IDPMAX IDPSNP IDQMIN
#> 1 rs2142947 33.979 33.979 63.615
#> 2 rs2142947 32.679 32.679 61.181
#> 3 rs2142947 33.355 33.355 62.447
#> 4 rs2142947 NA NA NA
#> 5 rs4995127 2.155 NA 61.715
#> 6 rs4995127 2.177 NA 62.343
#> 7 rs4995127 NA NA NA
#> 8 rs3026284 2.233 NA 69.349
#> 9 rs3026284 NA NA NA
## retrieve the PolyPhen-2 classifiers
subst <- c("PPH2CLASS", "PPH2PROB", "PPH2FPR", "PPH2TPR", "PPH2FDR")
select(PolyPhen.Hsapiens.dbSNP131, keys=rsids, columns=subst)
#> RSID PPH2CLASS PPH2PROB PPH2FPR PPH2TPR PPH2FDR
#> 1 rs2142947 neutral 0.070 0.2000 0.929 0.300
#> 2 rs2142947 neutral 0.018 0.2520 0.952 0.345
#> 3 rs2142947 neutral 0.007 0.3010 0.966 0.383
#> 4 rs2142947 <NA> 0.118 0.4140 0.912 NA
#> 5 rs2142947 <NA> 0.043 0.5070 0.947 NA
#> 6 rs2142947 <NA> 0.019 0.5810 0.964 NA
#> 7 rs4995127 deleterious 0.970 0.0629 0.694 0.153
#> 8 rs4995127 <NA> 0.803 0.1860 0.720 NA
#> 9 rs3026284 deleterious 0.936 0.0746 0.737 0.168
#> 10 rs3026284 <NA> 0.919 0.1330 0.636 NA
## duplicate groups of rsid's
duplicateRSID(PolyPhen.Hsapiens.dbSNP131, c("rs71225486", "rs1063796"))
#> $rs71225486
#> [1] "rs76416839"
#>
#> $rs1063796
#> [1] "rs17039307" "rs78671912"
#>