allPfamAnalysis: Global analysis of a repository of mutations

In gmelloni/LowMACA: LowMACA - Low frequency Mutation Analysis via Consensus Alignment

Description

Given a repository of mutations, the method allPfamAnalysis launches the analysis of all the Pfams and single sequences which are involved with at least one mutation.

Usage

allPfamAnalysis(repos
, allLowMACAObjects=NULL
, mutation_type=c("missense", "all", "truncating" , "silent") 
, NoSilent=TRUE
, mail=NULL
, perlCommand="perl"
, verbose=FALSE
, conservation=0.1
, use_hmm=FALSE
, datum=FALSE
, clustal_cmd="clustalo"
, BPPARAM=bpparam("SerialParam"))

Arguments

repos	either a data.frame or a filename containing the data to analyze
allLowMACAObjects	filename of a RData file to save all the LowMACA object allPfamsLM produced by the function. It can be usefull for plotting a specific Pfam after the analysis, but it can be a pretty large object. Default NULL
mutation_type	type of mutation to be considered for the analysis. Default to missense.
NoSilent	logical indicating if Silent mutations should be deleted or not. Default TRUE
mail	if not NULL, it must be a valid email address to use EBI clustalo web service. Default is to use a local clustalo installation
perlCommand	a character string containing the path to Perl executable. if missing, "perl" will be used as default. Only used if mail is set
verbose	logical. verbose output or not
conservation	a number between 0 and 1. Represents the minimum level of conservation to test a mutation
use_hmm	When analysing Pfam sequences, it is possible to use the Hidden Markov Model (HMM) of the specific Pfam to align the sequences. Default is FALSE.
datum	When analysing Pfam sequences, use all the genes that belong to the Pfam to generate the alignment. This creates a unique mapping between individual residues and consensus sequence, disregarding the set of sequences that are selected for the analysis. Default is FALSE.
clustal_cmd	path to clustalomega executable. default is to check "clustalo" in the PATH
BPPARAM	An object of class BiocParallelParam-class specifiying parameters related to the parallel execution of some of the tasks and calculations within this function. See function register from the BiocParallel package.

Details

This function takes a data.frame or a tab delimited text file in LowMACA format (see LowMACA_AML) and perform a full analysis of the dataset. It basically divide the mutations into their Pfam and launch many LowMACA analysis as many Pfam are hit by mutations up to the lfm function. Every significant position after lfm is tested at gene level. A binomial test is performed to see if the ratio between the number of mutations in the significant position over the total number of mutations is higher than expected by chance at gene level. The significant mutations of all the lfm functions are aggregated in one single data.frame.

Value

A list of two dataframes named 'AlignedSequence' and 'SingleSequence'

The first dataframe is the result of the alignment based analysis. Every gene is aggregated by its corresponding Pfam domain.

Gene_Symbol	gene symbols of the analyzed genes
Multiple_Aln_pos	positions in the consensus relatively to the sequence analyzed.
Pfam_ID	Pfam name analyzed
binomialPvalue	pvalue of the single gene test, See details
Amino_Acid_Position	amino acidic positions relative to original protein
Amino_Acid_Change	amino acid changes in hgvs format
Sample	Sample barcode where the mutation was found
Tumor_Type	Tumor type of the Sample
Envelope_Start	start of the pfam domain in the protein
Envelope_End	end of the pfam domain in the protein
metric	qvalue of the position in the multiple alignment of Pfam domains
Entrez	entrez ids of the mutations
Entry	Uniprot entry of the protein
UNIPROT	other protein names for Uniprot
Chromosome	cytobands of the genes
Protein.name	extended protein names

The second dataframe represent the result of LowMACA on every couple gene-domain when it is not aligned with any other member of the same Pfam ID.

Gene_Symbol	gene symbols of the analyzed genes
Amino_Acid_Position	amino acidic positions relative to original protein
Amino_Acid_Change	amino acid changes in hgvs format
Sample	Sample barcode where the mutation was found
Tumor_Type	Tumor type of the Sample
Envelope_Start	start of the pfam domain in the protein
Envelope_End	end of the pfam domain in the protein
Multiple_Aln_pos	positions in the consensus relatively to the sequence analyzed. See warnings section
Entrez	entrez ids of the mutations
Entry	Uniprot entry of the protein
UNIPROT	other protein names for Uniprot
Chromosome	cytobands of the genes
Protein.name	extended protein names

Author(s)

Stefano de Pretis , Giorgio Melloni

See Also

lfm, LowMACA_AML

Examples

#Load Homeobox example
data(lmObj)
#Extract the data inside the object as a toy example
myData <- lmMutations(lmObj)$data
#Run allPfamAnalysis on every mutations
significant_muts <- allPfamAnalysis(repos=myData)
#Show the result of alignment based analysis
head(significant_muts$AlignedSequence)
#Show all the genes that harbor significant mutations
unique(significant_muts$AlignedSequence$Gene_Symbol)
#Show the result of the Single Gene based analysis
head(significant_muts$SingleSequence)
#Show all the genes that harbor significant mutations
unique(significant_muts$SingleSequence$Gene_Symbol)

gmelloni/LowMACA documentation built on May 24, 2019, 5:03 a.m.