micropipeline <- function(fnas,
map, # Mapping file
rargs = '-p 1 -cd 95',
pargs = '',
gargs = ''
# pgpl = 'roary', #pangenome pipeline: roary or pewit
# pfam = NULL, # c('pfamA.hmm', 'pfamA.hmm.dat')
# genes = NULL # fasta files
)
{
deps <- checkDependencies()
if (missing(prefix)){
prefix <- format(Sys.time(), "%b%d%H%M%S")
}
#Run main programs
# (Capture outputs in lists)
gffs <- runProkka(fnas)
pang <- runPangenome(prefix, gffs, rargs) # o pewit
xmfa <- runProgressiveMauve(gffs, pargs)
fastaCore <- stripSubsetLCBs(xmfa = xmfa, gffs, msi=500L, nco=length(gffs))
gubbins <- runGubbins(fastaCore, gargs)
pStats <- pangStats(pang[11], progMauve, gubbins[2])
# recSeqs <- extractRecSeqs(gffs, gubbins)
# domContent <- runDCDA(gffs, pfam, map) # Produce PCoA plot
# phylo <- makePhylo(coreAl = c("progMauve", "pang"), gubbins) # mask recomb, phangorn
# genes <- findGenes(pang, genes, map) #Plot ggheatmap-like plot
}
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