monaLisa
: MOtif aNAlysis with LisamonaLisa
was inspired by her father Homer
to look for enriched motifs in sets (bins) of genomic regions, compared to all other
regions ("binned motif enrichment analysis").
It uses known motifs representing transcription factor binding preferences,
for example for the JASPAR2020
Bioconductor package. The regions are for
example promoters or accessible regions, which are grouped into bins according
to a numerical value assigned to each region, such as change of expression
or accessibility. The goal of the analysis is to identify transcription
factors that are associated to that numerical value and thus candidates
to be drivers in the underlying biological process.
In addition to the "binned motif enrichment analysis", monaLisa
can also be
used to address the above question using stability selection (a form of linear
regression), or to look for motif matches in sequences.
Current contributors include:
monaLisa
is available on BioconductormonaLisa
is now published in BioinformaticsTo cite monaLisa
please use the publication found here or see citation("monaLisa")
.
monaLisa
can be installed from Bioconductor via the
BiocManager
package:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("monaLisa")
Here is a minimal example to run a monaLisa
analysis:
library(monaLisa)
mcparams <- BiocParallel::MulticoreParam(10L)
se <- calcBinnedMotifEnrR(seqs = seqs, # DNAStringSet (e.g. peak sequences)
bins = bins, # factor that groups 'seqs'
pwmL = pwms, # PWMatrixList (know motifs)
BPPARAM = mcparams,
min.score = 10,
verbose = TRUE)
The return value se
is a SummarizedExperiment
with motifs in rows and bins
in columns, and multiple assays with significance and magnitude of the enrichments.
The inputs for calcBinnedMotifEnrR
can be easily obtained using other
Bioconductor packages:
# get sequences ('atacPeaks' is a GRanges)
library(Biostrings)
library(BSgenome.Mmusculus.UCSC.mm10)
seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, atacPeaks)
# bin sequences ('atacPeaksChange' is a numerical vector)
bins <- monaLisa::bin(x = atacPeaksChange, binmode = "equalN", nElement = 400)
# obtain known motifs from Jaspar
library(JASPAR2020)
library(TFBSTools)
pwms <- getMatrixSet(JASPAR2020, list(matrixtype = "PWM", tax_group = "vertebrates"))
The results can be conveniently visualized:
plotBinDensity(atacPeaksChange, bins, legend = FALSE)
plotMotifHeatmaps(se, cluster = TRUE,
which.plots = c("enr", "FDR"),
show_seqlogo = TRUE)
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