README.md

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universalmotif

This package allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. Furthermore, this package allows for easy manipulation of motifs, such as creation, trimming, shuffling, P-value calculations, filtering, type conversion, reverse complementation, alphabet switching, random motif site generation, and comparison. Alongside are also included functions for interacting with sequences, such as motif scanning and enrichment, as well as sequence creation and shuffling functions. Finally, this package implements higher-order motifs, allowing for more accurate sequence scanning and motif enrichment.

Installation

Bioconductor release version

if (!requireNamespace("BiocManager", quietly=TRUE))
  install.packages("BiocManager")
BiocManager::install("universalmotif")

GitHub development version

if (!requireNamespace("BiocManager", quietly=TRUE))
  install.packages("BiocManager")
BiocManager::install("bjmt/universalmotif")

Note: building the vignettes when installing from source is not recommended, unless you don't mind waiting an hour for the necessary dependencies to install.

Error when installing from source

If you trying to install the package from source and are getting compiler errors similar to these issues [1, 2, 3], then update your C++ compiler. This is an issue regarding incompatibilities between older compilers and the C++11 lambda functions from the RcppThread package, which is used by the universalmotif package.

Brief overview

All of the functions within the universalmotif package are fairly well documented. You can access the documentation from within R, reading the Bioconductor PDF, or browsing the rdrr.io website (the latter is not always up to date). Additionally, several vignettes come with the package, which you can access from within R or on the Bioconductor website:

You can also look through the slides of my Bioc2021 presentation, which goes over some basics of motif representations, scanning, and motif comparison.

A few key functions are also explored below.

The universalmotif motif class and import/export utilities

The universalmotif class is used to store the motif matrix itself, as well as other basic information such as alphabet, background frequencies, strand, and various other metadata slots. There are a number of ways of getting universalmotif class motifs:

universalmotif class motifs are highly interoperable with other motif formats:

library(universalmotif)

create_motif()
#>
#>        Motif name:   motif
#>          Alphabet:   DNA
#>              Type:   PPM
#>           Strands:   +-
#>          Total IC:   11.46
#>         Consensus:   YGTGMMMRGA
#>
#>      Y G    T    G    M    M    M    R    G    A
#> A 0.17 0 0.00 0.04 0.58 0.62 0.29 0.47 0.08 0.77
#> C 0.36 0 0.01 0.00 0.41 0.36 0.68 0.16 0.05 0.00
#> G 0.00 1 0.03 0.95 0.00 0.00 0.04 0.28 0.86 0.23
#> T 0.47 0 0.96 0.02 0.00 0.03 0.00 0.09 0.00 0.00

See ?universalmotif for a list of available metadata slots. Most slots can be accessed using square brackets, e.g. MotifObject["motif"] accesses the raw numeric matrix. You can also dump the contents of all user-facing motif slots at once into a list, e.g. MotifObject[].

Sequence creation, shuffling and background calculation

An important aspect of motif scanning and enrichment is to compare the results with those from a set of random or background sequences. For this, two functions are provided:

library(universalmotif)

seqs <- create_sequences()

seqs
#>   A DNAStringSet instance of length 100
#>       width seq
#>   [1]   100 AGTACGTTCGCATGGCAGGCATTATTTGCGCTG...TATCAGCCTAGAAGCAGGCGTACCAAGGTCTA
#>   [2]   100 AATATCGGGCGCGAAGCCCGATGCGTGCTCGGA...GATGCAGTTCAAACGAAATCTCGTAAACGTGA
#>   [3]   100 AGTACAGCAATGGGGACATAAGCCGTCTCATCG...CATAGTTCTCGAAATATGAATCTCCAGTCCCA
#>   [4]   100 CAGATGCACTATCACCGTGCCGAGCTCGGTAAC...AATCGCATTGAACTAACAGGGGAGCAAGATAA
#>   [5]   100 CGGCCCCTGGGACGTTGGATCCAGATAAAGCTT...TATGTTCCTTGCCGGAATACGGCACATATCTC
#>   ...   ... ...
#>  [96]   100 CGGTGCAAAATGTGCCGCACACGGTAGTGCGGG...TTACACGCGTCTTTCGGAGAATGAGCTCGGCA
#>  [97]   100 CAGTTAATCTATTAATGAGTCACTTAGGATTCC...GTTGCTTGGATATGGGAGAGAATGGCCAGTAA
#>  [98]   100 GGGTCGTTGGCAGGGATGCACACAGACACGAAT...GTTTGCAAGACAACAGTAGCTAATTGTGCCAA
#>  [99]   100 GCCTTCGGACGCCAAGTCTGCAAACAATTCCTC...CTTCTACGCCAAAACTCTTATCCCTGGCATTC
#> [100]   100 GTCACAGCCAAGCTTTAAGTCTTCCAACCAGGA...ATTGTGGACGGAAGGTACCGTCGTAGATTCGC

seqs.shuffled <- shuffle_sequences(seqs, k = 3)

Additionally, if you are interested in the detailed k-mer content of you sequences you can use get_bkg(). It can be used to calculate sequence background for any size k-mer, and for any sequence alphabet. Results can be shown for individual sequences or merged together. There is also an option to calculate these results in any size windows (with any size overlap between windows) across the sequences.

library(universalmotif)

data(ArabidopsisPromoters)

get_bkg(ArabidopsisPromoters, merge.res = FALSE)
#> DataFrame with 4200 rows and 4 columns
#>         sequence        klet     count probability
#>      <character> <character> <integer>   <numeric>
#> 1      AT4G28150           A       318       0.318
#> 2      AT1G19380           A       309       0.309
#> 3      AT4G19520           A       325       0.325
#> 4      AT1G03850           A       338       0.338
#> 5      AT5G01810           A       317       0.317
#> ...          ...         ...       ...         ...
#> 4196   AT5G22690         TTT        36   0.0360721
#> 4197   AT1G05670         TTT        43   0.0430862
#> 4198   AT1G06160         TTT        56   0.0561122
#> 4199   AT5G24660         TTT        43   0.0430862
#> 4200   AT3G19200         TTT        34   0.0340681

get_bkg(ArabidopsisPromoters, window = TRUE)
#> DataFrame with 840 rows and 5 columns
#>         start      stop        klet     count probability
#>     <numeric> <numeric> <character> <integer>   <numeric>
#> 1           1       100           A      1604      0.3208
#> 2         101       200           A      1636      0.3272
#> 3         201       300           A      1773      0.3546
#> 4         301       400           A      1791      0.3582
#> 5         401       500           A      1716      0.3432
#> ...       ...       ...         ...       ...         ...
#> 836       501       600         TTT       255   0.0520408
#> 837       601       700         TTT       269   0.0548980
#> 838       701       800         TTT       233   0.0475510
#> 839       801       900         TTT       255   0.0520408
#> 840       901      1000         TTT       271   0.0553061

Sequence scanning and higher order motifs

The universalmotif package provides the scan_sequences() function to quickly scan a set of input sequences for motif hits. Additionally, the add_multifreq() function can be used to generate higher order motifs. These can also be used to scan sequences with higher accuracy. By default scan_sequences() calculates a threshold cutoff from a P-value, though this can be changed to a manual logodds threshold.

library(universalmotif)
library(Biostrings)
data(ArabidopsisPromoters)

seqs <- DNAStringSet(rep(c("CAAAACC", "CTTTTCC"), 3))
motif <- create_motif(seqs, pseudocount = 1)

scan_sequences(motif, ArabidopsisPromoters)
#> DataFrame with 53 rows and 14 columns
#>           motif   motif.i    sequence     start      stop     score       match
#>     <character> <integer> <character> <integer> <integer> <numeric> <character>
#> 1         motif         1   AT1G03850       203       209      9.08     CTAATCC
#> 2         motif         1   AT1G06160       956       962      9.08     CTAATCC
#> 3         motif         1   AT1G07490       472       478      9.08     CTTAACC
#> 4         motif         1   AT1G07490       936       942      9.08     CATTTCC
#> 5         motif         1   AT1G19380       139       145      9.08     CTTATCC
#> ...         ...       ...         ...       ...       ...       ...         ...
#> 49        motif         1   AT5G20200       430       436      9.08     CAATTCC
#> 50        motif         1   AT5G22690        81        87      9.08     CAATACC
#> 51        motif         1   AT5G22690       362       368      9.08     CAAATCC
#> 52        motif         1   AT5G58430       332       338      9.08     CATAACC
#> 53        motif         1   AT5G58430       343       349      9.08     CAAATCC
#>     thresh.score min.score max.score score.pct      strand      pvalue    qvalue
#>        <numeric> <numeric> <numeric> <numeric> <character>   <numeric> <numeric>
#> 1           9.08   -19.649      9.08       100           + 0.000976562  0.915758
#> 2           9.08   -19.649      9.08       100           + 0.000976562  0.915758
#> 3           9.08   -19.649      9.08       100           + 0.000976562  0.915758
#> 4           9.08   -19.649      9.08       100           + 0.000976562  0.915758
#> 5           9.08   -19.649      9.08       100           + 0.000976562  0.915758
#> ...          ...       ...       ...       ...         ...         ...       ...
#> 49          9.08   -19.649      9.08       100           + 0.000976562  0.915758
#> 50          9.08   -19.649      9.08       100           + 0.000976562  0.915758
#> 51          9.08   -19.649      9.08       100           + 0.000976562  0.915758
#> 52          9.08   -19.649      9.08       100           + 0.000976562  0.915758
#> 53          9.08   -19.649      9.08       100           + 0.000976562  0.915758

motif.k2 <- add_multifreq(motif, seqs, add.k = 2)
scan_sequences(motif.k2, ArabidopsisPromoters, use.freq = 2, threshold = 1e-6)
#> DataFrame with 8 rows and 14 columns
#>         motif   motif.i    sequence     start      stop     score       match
#>   <character> <integer> <character> <integer> <integer> <numeric> <character>
#> 1       motif         1   AT1G19510       960       965    17.827      CTTTTC
#> 2       motif         1   AT1G49840       959       964    17.827      CTTTTC
#> 3       motif         1   AT1G77210       184       189    17.827      CAAAAC
#> 4       motif         1   AT1G77210       954       959    17.827      CAAAAC
#> 5       motif         1   AT2G37950       751       756    17.827      CAAAAC
#> 6       motif         1   AT3G57640       917       922    17.827      CTTTTC
#> 7       motif         1   AT4G12690       938       943    17.827      CAAAAC
#> 8       motif         1   AT4G14365       977       982    17.827      CTTTTC
#>   thresh.score min.score max.score score.pct      strand      pvalue    qvalue
#>      <numeric> <numeric> <numeric> <numeric> <character>   <numeric> <numeric>
#> 1       17.827   -16.842    17.827       100           + 1.90735e-06 0.0118494
#> 2       17.827   -16.842    17.827       100           + 1.90735e-06 0.0118494
#> 3       17.827   -16.842    17.827       100           + 1.90735e-06 0.0118494
#> 4       17.827   -16.842    17.827       100           + 1.90735e-06 0.0118494
#> 5       17.827   -16.842    17.827       100           + 1.90735e-06 0.0118494
#> 6       17.827   -16.842    17.827       100           + 1.90735e-06 0.0118494
#> 7       17.827   -16.842    17.827       100           + 1.90735e-06 0.0118494
#> 8       17.827   -16.842    17.827       100           + 1.90735e-06 0.0118494

Note the differences between the matching sequences of regular scanning versus higher order scanning.

Motif comparison, merging and viewing

A commonly performed task after de novo motif discovery is to check how closely it might resemble known motifs. This can be performed using the highly customizable compare_motifs() with one of several available metrics. Different motifs can also be merged with merge_motifs(). In addition to motif visualization, view_motifs() can also be used to examine the top-scoring alignment chosen by compare_motifs() and merge_motifs().

library(universalmotif)

new.motif <- create_motif("CGCGAAAAAA", name = "New motif")
old.motif <- create_motif("TATATTTTTT", name = "Old motif")

Using very strict alignment parameters, such as no overhangs:

compare_motifs(c(new.motif, old.motif), method = "PCC", min.overlap = 10)[2]
#> [1] 0.2

merged.motif <- merge_motifs(c(new.motif, old.motif), method = "PCC",
    new.name = "Merged motif", min.overlap = 10)

view_motifs(c(new.motif, old.motif, merged.motif), method = "PCC",
    min.overlap = 10)

After relaxing the alignment parameters:

compare_motifs(c(new.motif, old.motif), method = "PCC", min.overlap = 5)[2]
#> [1] 1

merged.motif <- merge_motifs(c(new.motif, old.motif), method = "PCC",
    new.name = "Merged motif", min.overlap = 5)

view_motifs(c(new.motif, old.motif, merged.motif), method = "PCC",
    min.overlap = 5)

By default compare_motifs() returns a numeric matrix, meaning the output from comparisons between large numbers of motifs can be easily used to generate heatmaps or dendrograms.



bjmt/universalmotif documentation built on Oct. 19, 2021, 11:15 a.m.