SpacePAC-package: Identifying mutational clusters in 3D protein space using...

In gryslik/SpacePAC: Identification of Mutational Clusters in 3D Protein Space via Simulation.

Description

The SpacePAC package identifies non-random amino acid clusters in proteins in 3D space and is a sister package to iPAC and GraphPAC. SpacePAC considers 1, 2 or 3 non-overlapping spheres with radii specified by the user and through simulation, attempts to identify spheres where there are more mutations than expected by random chance alone. These results are then outputted in the form of a list with p-values.

Details

Please see get.Positions and get.AlignedPositions in the iPAC package for information about obtaining positional data.

Author(s)

Gregory Ryslik, Yuwei Cheng, Hongyu Zhao

Maintainer: Gregory Ryslik <[e-mail]>

References

Gregory Ryslik and Hongyu Zhao (2012). iPAC: Identification of Protein Amino acid Clustering. R package version 1.1.3. http://www.bioconductor.org/.

Gregory Ryslik and Hongyu Zhao (2013). GraphPAC: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach. R Package version 1.0.0 http://www.bioconductor.org/.

Bioconductor: Open software development for computational biology and bioinformatics R. Gentleman, V. J. Carey, D. M. Bates, B.Bolstad, M. Dettling, S. Dudoit, B. Ellis, L. Gautier, Y. Ge, and others 2004, Genome Biology, Vol. 5, R80

See Also

get.Positions SpaceClust

Examples

## Not run: 
CIF <- "https://files.rcsb.org/view/3GFT.cif"
Fasta <- "https://www.uniprot.org/uniprot/P01116-2.fasta"
KRAS.Positions <- get.Positions(CIF, Fasta, "A")
data(KRAS.Mutations)

#Calculate the required clusters
SpaceClust(KRAS.Mutations, KRAS.Positions$Positions, radii.vector = c(1,2,3,4))


## End(Not run)

gryslik/SpacePAC documentation built on May 4, 2019, 3:13 a.m.