mimp: Predict the impact of single variants on phosphorylation.

In omarwagih/rmimp: Predicting the impact of mutations on kinase-substrate phosphorylation

Description

This function takes in mutation, sequence and phosphorylation data to predict the impact the mutation has on phosphorylation.

Usage

mimp(muts, seqs = NULL, psites = NULL, prob.thresh = 0.5,
  log2.thresh = 1, display.results = F, include.cent = F,
  model.data = "hconf", cores = 1, kinases)

Arguments

muts	Mutation data file: a space delimited text file OR data frame containing two columns (1) gene and (1) mutation. Example: TP53 R282W CTNNB1 S33C CTNNB1 S37F
seqs	Sequence data file containing protein sequences in FASTA format OR named list of sequences where each list element is the uppercase sequence and the name of each element is that of the protein. Example: list(GENEA="ARNDGH", GENEB="YVRRHS") If both sequences and phosphosites are omitted, mimp will use sites from phosphositeplus. This means the IDs in your mutation file must be UniProt accession numbers
psites	Phosphorylation data file (optional): a space delimited text file OR data frame containing two columns (1) gene and (1) positions of phosphorylation sites. Example: TP53 280 CTNNB1 29 CTNNB1 44 If both sequences and phosphosites are omitted, mimp will use sites from phosphositeplus. This means the IDs in your mutation file must be UniProt accession numbers
prob.thresh	Probability threshold of gains and losses. This value should be between 0.5 and 1.
log2.thresh	Threshold for the absolute value of log ratio between wild type and mutant scores. Anything less than this value is discarded (default: 1).
display.results	If TRUE results are visualised in an html document after analysis is complete
include.cent	If TRUE, gains and losses caused by mutation in the central STY residue are kept. Scores of peptides with a non-STY central residue is given a score of 0 (default: FALSE).
model.data	Name of specificity model data to use, can be "hconf" : individual experimental kinase specificity models used to scan for rewiring events. For experimental kinase specificity models, grouped by family, set to "hconf-fam". Both are considered high confidence. For lower confidence predicted specificity models , set to "lconf". NOTE: Predicted models are purely speculative and should be used with caution
cores	Number of cores to use - default is 1. More cores will speed up computation.
kinases	Character vector of kinase models to be used - if missing all kinase models are used (default)

Value

The data is returned in a data.frame with the following columns:

gene	Gene with the rewiring event
mut	Mutation causing the rewiring event
psite_pos	Position of the phosphosite
mut_dist	Distance of the mutation relative to the central residue
wt	Sequence of the wildtype phosphosite (before the mutation). Score is NA if the central residue is not S, T or Y
mt	Sequence of the mutated phosphosite (after the mutation). Score is NA if the central residue is not S, T or Y
score_wt	Matrix similarity score of the wildtype phosphosite
score_mt	Matrix similarity score of the mutated phosphosite
log_ratio	Log2 ratio between mutant and wildtype scores. A high positive log ratio represents a high confidence gain-of-phosphorylation event. A high negative log ratio represents a high confidence loss-of-phosphorylation event. This ratio is NA for mutations that affect the central phosphorylation sites
pwm	Name of the kinase being rewired
pwm_fam	Family/subfamily of kinase being rewired. If a kinase subfamily is available the family and subfamily will be separated by an underscore e.g. "DMPK_ROCK". If no subfamily is available, only the family is shown e.g. "GSK"
nseqs	Number of sequences used to construct the PWM. PWMs constructed with a higher number of sequences are generally considered of better quality.
prob	Joint probability of wild type sequence belonging to the foreground distribution and mutated sequence belonging to the background distribution, for loss and vice versa for gain.
effect	Type of rewiring event, can be "loss" or "gain"

Examples

# Get the path to example phosphorylation data 
psite.file = system.file("extdata", "sample_phosphosites.tab", package = "rmimp")

# Get the path to example mutation data 
mut.file = system.file("extdata", "sample_muts.tab", package = "rmimp")

# Get the path to example FASTA sequence data 
seq.file = system.file("extdata", "sample_seqs.fa", package = "rmimp")

# Run rewiring analysis
results = mimp(mut.file, seq.file, psite.file, display.results=TRUE)

# Show head of results
head(results)

omarwagih/rmimp documentation built on May 18, 2020, 3:11 p.m.