SIFTDb-class {VariantAnnotation} | R Documentation

## SIFTDb objects

### Description

The SIFTDb class is a container for storing a connection to a SIFT sqlite database.

### Details

SIFT is a sequence homology-based tool that sorts intolerant from tolerant amino acid substitutions and predicts whether an amino acid substitution in a protein will have a phenotypic effect. SIFT is based on the premise that protein evolution is correlated with protein function. Positions important for function should be conserved in an alignment of the protein family, whereas unimportant positions should appear diverse in an alignment.

SIFT uses multiple alignment information to predict tolerated and deleterious substitutions for every position of the query sequence. The procedure can be outlined in the following steps,

- search for similar sequences

- choose closely related sequences that may share similar function to the query sequence

- obtain the alignment of the chosen sequences

- calculate normalized probabilities for all possible substitutions from the alignment.

Positions with normalized probabilities less than 0.05 are predicted to be deleterious, those greater than or equal to 0.05 are predicted to be tolerated.

### Methods

In the code below, `x` is a `SIFTDb` object.

  `metadata(x)`: Returns `x`'s metadata in a data frame.

  `columns(x)`: Returns the names of the `columns` that can be used to subset the data columns.

  `keys(x)`: Returns the names of the `keys` that can be used to subset the data rows. The `keys` values are the rsid's.

  `select(x, keys = NULL, columns = NULL, ...)`: Returns a subset of data defined by the character vectors `keys` and `columns`. If no `keys` are supplied, all rows are returned. If no `columns` are supplied, all columns are returned. For column descriptions see `?SIFTDbColumns`.

### Author(s)

Valerie Obenchain

### References

SIFT Home: http://sift.jcvi.org/

Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073-81

Ng PC, Henikoff S. Predicting the Effects of Amino Acid Substitutions on Protein Function Annu Rev Genomics Hum Genet. 2006;7:61-80.

Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003 Jul 1;31(13):3812-4.

### Examples

    if (interactive()) {
        library(SIFT.Hsapiens.dbSNP132)

        ## metadata
        metadata(SIFT.Hsapiens.dbSNP132)

        ## available rsid's
        head(keys(SIFT.Hsapiens.dbSNP132))

        ## for column descriptions see ?SIFTDbColumns
        columns(SIFT.Hsapiens.dbSNP132)

        ## subset on keys and columns
        rsids <- c("rs2142947", "rs17970171", "rs8692231", "rs3026284")
        subst <- c("RSID", "PREDICTION", "SCORE")
        select(SIFT.Hsapiens.dbSNP132, keys=rsids, columns=subst)
        select(SIFT.Hsapiens.dbSNP132, keys=rsids[1:2])
    }

[Package VariantAnnotation version 1.30.1 Index]