bcRep (version 1.2.2)

bcRep-package: Advanced Analysis of B Cell Receptor Repertoire Data

Description

This package helps to analyze IMGT/HighV-QUEST output, in more detail. It functions well with B cells, but can also be used for T cell data, in some cases. Using this package you can read IMGT/HighV-QUEST output files and study sequences and clones. In special their functionality, junction frames, gene usage and mutations. Functions to analyze clones out of the IMGT/HighV-QUEST output, but also to compare sequences and clones, are provided.

Details

ll{ Package: bcRep Type: Package Version: 1.0 Date: 2015-09-09 License: GPL-2 } For many of the functions you need output data from IMGT/HighV-QUEST. In this case the required input file is given in help and you can use readIMGT() to read these files. For sequence analysis you can use functions like sequences.functionality() or sequences.junctionFrame(), which give an overview about functionality or junction frame usage of sequences. Further gene usage (geneUsage()) and gene/gene combinations (sequences.geneComb()) can be analyzed. The function sequences.mutation() returns an overview of all mutations, replacement and silent mutations, as well as the R/S ratio in several regions like V, FR1-3, CDR1-2. IMGT/HighV-QUEST output can also be used to analyze clones (clones()). Therefore criteria can be changed and results of for example different individuals be compared (clones.shared()). Further more amino acid distributions, as well as richness and diversity of sequences, like CDR3, can be studied (AADistribution(), trueDiversity()). Special plot function for most of the methods are provided.

References

Alamyar, E. et al., IMGT/HighV-QUEST: A High-Throughput System and Web Portal for the Analysis of Rearranged Nucleotide Sequences of Antigen Receptors, JOBIM 2010, Paper 63 (2010).

Brochet, X. et al., Nucl. Acids Res. 36, W503-508 (2008). PMID: 18503082

IMGT/V-QUEST Documentation: http://www.imgt.org/IMGT_vquest/share/textes/imgtvquest.html#output3

IMGT Repertoire (IG and TR): http://www.imgt.org/IMGTrepertoire/LocusGenes/

Lou Jost: Entropy and diversity; OIKOS 113:2 (2006)

Examples

data(summarytab)
data(aaseqtab)
data(aaseqtab2)
data(mutationtab)
data(clones.ind)
data(clones.allind)

## Combine IMGT/HighV-QUEST folders and read data
combineIMGT(folders = c("pathTo/IMGT1a", "pathTo/IMGT1b", "pathTo/IMGT1c"), 
name = "NewProject)
tab<-readIMGT("PathTo/file.txt",filterNoResults=TRUE)


## Get information about functionality and filter for functional sequences
functionality<-sequences.functionality(data = summarytab$Functionality)
ProductiveSequences<-sequences.getProductives(summarytab)

## Gene usage
Vsubgroup.usage<-geneUsage(genes = clones.ind$V_gene, 
     functionality = clones.ind$Functionality_all_sequences, level = "subgroup", 
     abundance="relative")

Vgenes.comp<-compare.geneUsage(gene.list = list(aaseqtab$V_GENE_and_allele, 
     aaseqtab2$V_GENE_and_allele), level = "subgroup", abundance = "relative", 
     names = c("IndA", "IndB"), nrCores = 1)
plotCompareGeneUsage(comp.tab = Vgenes.comp, color = c("gray97", "darkblue"), PDF = "Example")


## Gene/gene combinations
VDcomb.tab<-sequences.geneComb(family1 = summarytab$V_GENE_and_allele, 
     family2 = summarytab$D_GENE_and_allele, level = "subgroup", abundance = "relative")
plotGeneComb(geneComb.tab=VDcomb.tab, color="red", withNA=FALSE,PDF="test")


## Mutation analysis
mutation.V<-sequences.mutation(mutationtab = mutationtab, summarytab = summarytab, 
     sequence = "V")
mutation.CDR1<-sequences.mutation(mutationtab = mutationtab, sequence = "CDR1", 
     functionality = TRUE, junctionFr = TRUE)

## Defining, Filtering and Plotting Clone features
clones.tab<-clones(aaseqtab=aaseqtab,summarytab=summarytab, identity=0.85, useJ=TRUE, 
     dispCDR3aa=TRUE, dispFunctionality.ratio=TRUE, dispFunctionality.list=TRUE)
plotClonesCDR3Length(CDR3Length = clones.ind$CDR3_length_AA, 
     functionality = clones.ind$Functionality_all_sequences,  
     color="gray",abundance="relative", PDF="test")
clones.func<-clones.filterFunctionality(clones.filterFunctionality(clones.tab = clones.ind, 
     filter = "productive")   

## Find shared clones between individuals
sharedclones<-clones.shared(clones.tab = clones.allind, identity = 0.85, useJ = TRUE, 
     dispD = TRUE, dispCDR3aa = TRUE)
sharedclones.summary<-clones.shared.summary(shared.tab = sharedclones)

## True diversity
trueDiv<-trueDiversity(sequences = aaseqtab$CDR3_IMGT, order = 1)
plotTrueDiversity(trueDiversity.tab=trueDiv,color="red",PDF="test")

trueDiv.comp<-compare.trueDversity(sequence.list = list(aaseqtab$CDR3_IMGT, 
     aaseqtab2$CDR3_IMGT), names = c("IndA", "IndB"), order = 1, nrCores = 1)
plotCompareTrueDiversity(comp.tab = trueDiv.comp, PDF = "Example")


## Gini index
gini<-gini<-clones.giniIndex(clone.size=clones.ind$total_number_of_sequences)