The motifmatchr package is designed for analyzing many sequences and
many motifs to find which sequences contain which motifs. It uses the
MOODS C++ library (developedby Pasi Rastas, Janne Korhonen, and Petri
Martinmaki) internally for motif matching. The primary method of
motifmatchr is matchMotifs, which takes in motif PWMs/PFMs
and genomic ranges or sequences and returns either which
ranges/sequences match which motifs or the positions of the matches.
Compared with alternative motif matching functions available in Bioconductor (e.g. matchPWM in Biostrings or searchSeq in TFBSTools), motifmatchr is designed specifically for the use case of determining whether many different sequences/ranges contain many different motifs. For example, when analyzing ChIP-seq or ATAC-seq data one might want to find what motifs in a collection of motifs like the JASPAR database are found in what peaks.
Example use case of motifmatchr with a set of peaks and a few motifs. For additional options for inputs & outputs, see remainder of vignette.
library(motifmatchr)
library(GenomicRanges)
library(SummarizedExperiment)
library(BSgenome)
# load some example motifs
data(example_motifs, package = "motifmatchr")
# Make a set of peaks
peaks <- GRanges(seqnames = c("chr1","chr2","chr2"),
ranges = IRanges(start = c(76585873,42772928,100183786),
width = 500))
# Get motif matches for example motifs in peaks
motif_ix <- matchMotifs(example_motifs, peaks, genome = "hg19")
motifMatches(motif_ix) # Extract matches matrix from result## 3 x 3 sparse Matrix of class "lgCMatrix"
## MA0599.1_KLF5 MA0107.1_RELA MA0137.3_STAT1
## [1,] | | .
## [2,] | . |
## [3,] | . |This method has two mandatory arguments:
Position weight matrices or position frequency matrices, stored in the PWMatrix, PFMatrix, PWMatrixList, or PFMatrixList objects from the TFBSTools package
Either a set of genomic ranges (GenomicRanges or RangedSummarizedExperiment object) or a set of sequences (either DNAStringSet, DNAString, or simple character vector)
If the second argument is a set of genomic ranges, a genome sequence
is also required. By default BSgenome.Hsapiens.UCSC.hg19
is used — you will have to have installed BSgenome.Hsapiens.UCSC.hg19.
If using another genome build, either the appropraiate BSgenome object
for your species or a DNAStringSet or FaFile object for your species
should be passed to the genome argument.
# using peaks
motif_ix_peaks <- matchMotifs(example_motifs, peaks, genome = "hg19")
# using SummarizedExperiment
example_SummarizedExperiment <-
SummarizedExperiment(assays = list(counts = matrix(1,
ncol = 4,
nrow = 3)),
rowRanges = peaks)
motif_ix_SummarizedExperiment <- matchMotifs(example_motifs,
example_SummarizedExperiment,
genome = "hg19")
all.equal(motifMatches(motif_ix_peaks),
motifMatches(motif_ix_SummarizedExperiment))## [1] TRUE# using BSgenomeViews
example_BSgenomeViews <- BSgenomeViews(BSgenome.Hsapiens.UCSC.hg19, peaks)
motif_ix_BSgenomeViews <- matchMotifs(example_motifs, example_BSgenomeViews) ## Warning in matrix(log2(bg(pwm)) - log2(bg_freqs), nrow = 4, ncol = length(pwm),
## : data length [12] is not a sub-multiple or multiple of the number of columns
## [10]
## Warning in matrix(log2(bg(pwm)) - log2(bg_freqs), nrow = 4, ncol = length(pwm),
## : data length [12] is not a sub-multiple or multiple of the number of columns
## [10]## Warning in matrix(log2(bg(pwm)) - log2(bg_freqs), nrow = 4, ncol = length(pwm),
## : data length [12] is not a sub-multiple or multiple of the number of columns
## [11]## [1] "Attributes: < Component \"i\": Numeric: lengths (6, 9) differ >"
## [2] "Attributes: < Component \"p\": Mean relative difference: 0.5 >"
## [3] "Attributes: < Component \"x\": Lengths (6, 9) differ (comparison on first 6 components) >"# using DNAStringSet
library(Biostrings)
library(BSgenome.Hsapiens.UCSC.hg19)
example_DNAStringSet <- getSeq(BSgenome.Hsapiens.UCSC.hg19, peaks)
motif_ix_DNAStringSet <- matchMotifs(example_motifs, example_DNAStringSet)
all.equal(motifMatches(motif_ix_peaks), motifMatches(motif_ix_DNAStringSet))## [1] TRUE# using character vector
example_character <- as.character(example_DNAStringSet)
motif_ix_character <- matchMotifs(example_motifs, example_character)
all.equal(motifMatches(motif_ix_peaks), motifMatches(motif_ix_character))## [1] TRUEIn determining motif matches, background nucleotide frequencies are
used. By default the background frequencies are the nucleotide
frequencies within the input sequence – to use alternate frequencies,
supply the bg arument to match_pwms. If the
input sequences are fairly short (as in our vignette example!), it
probably makes sense to use other input frequencies. Here we show how to
use even frequencies:
We can also choose to use the frequency from the genome. In this case:
A genome must be specified if using bg = "genome"!
To specify frequencies per base pair, the order should be “A”,“C”,“G”, then “T”, or those nucleotides should be used as names in the vector.
motif_ix <- matchMotifs(example_motifs, peaks, genome = "hg19",
bg = c("A" = 0.2,"C" = 0.3, "G" = 0.3, "T" = 0.2)) PWMatrix objects have associated background frequencies that can be
accessed using the bg function. If the supplied background
frequencies to match_pwms do not match the frequencies in
the input PWM, then the PWM is adjusted to refect the supplied
background frequencies. The calculated score is based on this adjusted
PWM and not the direct input PWM. To ensure the score is computed using
the exact PWM input, simply make sure the background frequencies passed
to matchMotifs match hose used for the input PWM and stored in bg slot
of PWMatrix object.
motifmatchr expects input PWMs to use either natural logarithm or log 2. If the input is a PFM, the TFBSTools toPWM is used for making the PWM with the default psueodcounts of 0.8 and base 2 logarithm. For more control of the pseudocounts, simply use the toPWM function to convert your PFMs prior to calling matchMotifs.
The default p-value cutoff is 0.00005. No adjustment for multiple comparisons is made in this p-value cutoff. This p-value threshold is used to determine a score threshold. # Outputs
The matchMotifs can return three possible outputs, depending on the
out argument:
(Default, with out = matches) Boolean matrix
indicating which ranges/sequences contain which motifs, stored as
motifMatches in assays slot of SummarizedExperiment object.
The motifMatches methods can be used to extract the boolean
matrix. If either the subject argument is a GenomicRanges
or RangedSummarizedExperiment object OR a ranges argument
is provided, then a RangedSummarizedExeriment is returned rather than a
SummarizedExperiment.
(out = scores) Same as (1) plus two additional
assays – a matrix with the score of the high motif score within each
range/sequence (score only reported if match present) and a matrix with
the number of motif matches. The motifScores and
motifCounts methods can be used to access these
components.
(out = positions) A GenomicRangesList with the
ranges of all matches within the input ranges/sequences. Note that if
the subject argument is a character vector, DNAStringSet,
or DNAString and a ranges argument corresponding to the
ranges represented by the sequences is NOT provided, then a list of
IRangesList objects will be returned instead with the relative positions
withing the sequences.
## class: RangedSummarizedExperiment
## dim: 3 3
## metadata(0):
## assays(1): motifMatches
## rownames: NULL
## rowData names(0):
## colnames(3): MA0599.1_KLF5 MA0107.1_RELA MA0137.3_STAT1
## colData names(1): name## 3 x 3 sparse Matrix of class "lgCMatrix"
## MA0599.1_KLF5 MA0107.1_RELA MA0137.3_STAT1
## [1,] | | .
## [2,] | . |
## [3,] | . |motif_ix_scores <- matchMotifs(example_motifs, peaks,
genome = "hg19", out = "scores")
print(motif_ix_scores)## class: RangedSummarizedExperiment
## dim: 3 3
## metadata(0):
## assays(3): motifScores motifMatches motifCounts
## rownames: NULL
## rowData names(0):
## colnames(3): MA0599.1_KLF5 MA0107.1_RELA MA0137.3_STAT1
## colData names(1): name## 3 x 3 sparse Matrix of class "lgCMatrix"
## MA0599.1_KLF5 MA0107.1_RELA MA0137.3_STAT1
## [1,] | | .
## [2,] | . |
## [3,] | . |## 3 x 3 sparse Matrix of class "dgCMatrix"
## MA0599.1_KLF5 MA0107.1_RELA MA0137.3_STAT1
## [1,] 7.975145 13.55634 .
## [2,] 7.085762 . 16.29401
## [3,] 7.739609 . 14.81298## 3 x 3 sparse Matrix of class "dgCMatrix"
## MA0599.1_KLF5 MA0107.1_RELA MA0137.3_STAT1
## [1,] 1 1 .
## [2,] 1 . 1
## [3,] 1 . 2motif_pos <- matchMotifs(example_motifs, peaks, genome = "hg19",
out = "positions")
print(motif_pos)## GRangesList object of length 3:
## $MA0599.1_KLF5
## GRanges object with 3 ranges and 1 metadata column:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr1 76585928-76585937 - | 7.97515
## [2] chr2 42773248-42773257 - | 7.08576
## [3] chr2 100184023-100184032 - | 7.73961
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $MA0107.1_RELA
## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr1 76585933-76585942 + | 13.5563
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $MA0137.3_STAT1
## GRanges object with 3 ranges and 1 metadata column:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr2 42773087-42773097 - | 16.2940
## [2] chr2 100183852-100183862 + | 14.8130
## [3] chr2 100183852-100183862 - | 13.9536
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths## [1] "2024-12-19"## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
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##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] TFBSTools_1.45.0 BSgenome.Hsapiens.UCSC.hg19_1.4.3
## [3] BSgenome_1.75.0 rtracklayer_1.67.0
## [5] BiocIO_1.17.1 Biostrings_2.75.3
## [7] XVector_0.47.0 SummarizedExperiment_1.37.0
## [9] Biobase_2.67.0 MatrixGenerics_1.19.0
## [11] matrixStats_1.4.1 GenomicRanges_1.59.1
## [13] GenomeInfoDb_1.43.2 IRanges_2.41.2
## [15] S4Vectors_0.45.2 BiocGenerics_0.53.3
## [17] generics_0.1.3 motifmatchr_1.29.0
## [19] rmarkdown_2.29
##
## loaded via a namespace (and not attached):
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## [3] bitops_1.0-9 fastmap_1.2.0
## [5] RCurl_1.98-1.16 pracma_2.4.4
## [7] GenomicAlignments_1.43.0 XML_3.99-0.17
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## [29] BiocParallel_1.41.0 R.oo_1.27.0
## [31] sys_3.4.3 grid_4.4.2
## [33] caTools_1.18.3 xtable_1.8-4
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## [37] ggplot2_3.5.1 poweRlaw_0.80.0
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## [59] bit64_4.5.2 seqLogo_1.73.0
## [61] maketools_1.3.1 jquerylib_0.1.4
## [63] annotate_1.85.0 glue_1.8.0
## [65] codetools_0.2-20 gtable_0.3.6
## [67] stringi_1.8.4 UCSC.utils_1.3.0
## [69] munsell_0.5.1 tibble_3.2.1
## [71] pillar_1.10.0 htmltools_0.5.8.1
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