utils-motif: Motif-related utility functions.
In universalmotif: Import, Modify, and Export Motifs with R

Description Usage Arguments Value Author(s) See Also Examples

Motif-related utility functions.

add_gap(motif, gaploc = ncol(motif)%/%2, mingap = 1, maxgap = 5)

compare_columns(x, y, method, bkg1 = rep(1/length(x), length(x)),
  bkg2 = rep(1/length(y), length(y)), nsites1 = 100, nsites2 = 100)

consensus_to_ppm(letter)

consensus_to_ppmAA(letter)

get_consensus(position, alphabet = "DNA", type = "PPM", pseudocount = 1)

get_consensusAA(position, type = "PPM", pseudocount = 0)

get_matches(motif, score, allow.nonfinite = FALSE)

get_scores(motif, allow.nonfinite = FALSE)

icm_to_ppm(position)

motif_score(motif, threshold = c(0, 1), use.freq = 1,
  allow.nonfinite = FALSE, threshold.type = c("total", "fromzero"))

log_string_pval(pval)

pcm_to_ppm(position, pseudocount = 0)

position_icscore(position, bkg = numeric(), type = "PPM",
  pseudocount = 1, nsites = 100, relative_entropy = FALSE,
  schneider_correction = FALSE)

ppm_to_icm(position, bkg = numeric(), schneider_correction = FALSE,
  nsites = 100, relative_entropy = FALSE)

ppm_to_pcm(position, nsites = 100)

ppm_to_pwm(position, bkg = numeric(), pseudocount = 1, nsites = 100,
  smooth = TRUE)

prob_match(motif, match, allow.zero = TRUE)

prob_match_bkg(bkg, match)

pwm_to_ppm(position, bkg = numeric())

round_motif(motif, pct.tolerance = 0.05)

score_match(motif, match, allow.nonfinite = FALSE)

summarise_motifs(motifs, na.rm = TRUE)

ungap(motif, delete = FALSE)

`motif`	Motif object to calculate scores from, or add/remove gap, or round.
`gaploc`	`numeric` Motif gap locations. The gap occurs immediately after every position value. If missing, uses `round(ncol(motif) / 2)`.
`mingap`	`numeric` Minimum gap size. Must have one value for every location. If missing, set to 1.
`maxgap`	`numeric` Maximum gap size. Must have one value for every location. If missing, set to 5.
`x`	`numeric` First column for comparison.
`y`	`numeric` Second column for comparison.
`method`	`character(1)` Column comparison metric. See `compare_motifs()` for details.
`bkg1`	`numeric` Vector of background probabilities for the first column. Only relevant if `method = "ALLR"`.
`bkg2`	`numeric` Vector of background probabilities for the second column. Only relevant if `method = "ALLR"`.
`nsites1`	`numeric(1)` Number of sites for the first column. Only relevant if `method = "ALLR"`.
`nsites2`	`numeric(1)` Number of sites for the second column. Only relevant if `method = "ALLR"`.
`letter`	`character(1)` Any DNA, RNA, or AA IUPAC letter. Ambiguity letters are accepted.
`position`	`numeric` A numeric vector representing the frequency or probability for each alphabet letter at a specific position.
`alphabet`	`character(1)` One of `c('DNA', 'RNA')`.
`type`	`character(1)` One of `c('PCM', 'PPM', 'PWM' 'ICM')`.
`pseudocount`	`numeric(1)` Used to prevent zeroes in motif matrix.
`score`	`numeric(1)` Logodds motif score.
`allow.nonfinite`	`logical(1)` If `FALSE`, then apply a pseudocount if non-finite values are found in the PWM. Note that if the motif has a pseudocount greater than zero and the motif is not currently of type PWM, then this parameter has no effect as the pseudocount will be applied automatically when the motif is converted to a PWM internally. This value is set to `FALSE` by default in order to stay consistent with pre-version 1.8.0 behaviour.
`threshold`	`numeric(1)` Any number of numeric values between 0 and 1 representing score percentage.
`use.freq`	`numeric(1)` Use regular motif or the respective `multifreq` representation.
`threshold.type`	`character` For `"total"`, a threshold of zero represents the minimum possible score. This means the range of scores that can be extracted is from the minimum to the maximum possible scores. For `"fromzero"`, a threshold of zero is a score of zero. This means the range of scores is from zero to the maximum. The `"total"` threshold type can only be used if no non-finite values are present in the PWM.
`pval`	`character(1)` String-formatted p-value.
`bkg`	`numeric` Should be the same length as the alphabet length.
`nsites`	`numeric(1)` Number of sites motif originated from.
`relative_entropy`	`logical(1)` Calculate information content as relative entropy or Kullback-Leibler divergence.
`schneider_correction`	`logical(1)` Apply sample size correction.
`smooth`	`logical(1)` Apply pseudocount correction.
`match`	`character` Sequence string to calculate score from.
`allow.zero`	`logical(1)` If `FALSE`, apply a pseudocount if zero values are found in the background frequencies.
`pct.tolerance`	`numeric(1)` or `character(1)` The minimum tolerated proportion each letter must represent per position in order not to be rounded off, either as a numeric value from 0 to 1 or a percentage written as a string from "0%" to "100%".
`motifs`	`list` A list of universalmotif motifs.
`na.rm`	`logical` Remove columns where all values are `NA`.
`delete`	`logical(1)` Clear gap information from motif. If `FALSE`, then it can be reactivated simply with `add_gap(motif)`.

For consensus_to_ppm() and consensus_to_ppmAA(): a numeric vector of length 4 and 20, respectively.

For get_consensus() and get_consensusAA(): a character vector of length 1.

For get_matches(): a character vector of motif matches.

For motif_score(): a named numeric vector of motif scores.

For log_string_pval(): a numeric vector of length 1.

For position_icscore(): a numeric vector of length 1.

For ppm_to_icm(), icm_to_ppm(), pcm_to_ppm(), ppm_to_pcm(), ppm_to_pwm(), and pwm_to_ppm(): a numeric vector with length equal to input numeric vector.

For prob_match(): a numeric vector of probabilities.

For round_motif(): the input motif, rounded.

For score_match(): a numeric vector with the match motif score.

For summarise_motifs(): a data.frame with columns representing the universalmotif slots.

Benjamin Jean-Marie Tremblay, b2tremblay@uwaterloo.ca

create_motif()

data(examplemotif)
examplemotif0 <- examplemotif
examplemotif0["pseudocount"] <- 0

#######################################################################
## add_gap
## Add gap information to a motif.
m <- create_motif()
# Add a gap size 5-8 between positions 4 and 5:
m <- add_gap(m, gaploc = 4, mingap = 5, maxgap = 8)

#######################################################################
## compare_columns
## Compare two numeric vectors using the metrics from compare_motifs()
compare_columns(c(0.5, 0.1, 0.1, 0.2), c(0.7, 0.1, 0.1, 0.1), "PCC")

#######################################################################
## consensus_to_ppm
## Do the opposite of get_consensus. Note that loss of information is
## inevitable. Generates a sequence matrix.
sapply(c("A", "G", "T", "B"), consensus_to_ppm)

#######################################################################
## consensus_to_ppmAA
## Do the opposite of get_consensusAA and generate a motif matrix.
sapply(c("V", "A", "L"), consensus_to_ppmAA)

#######################################################################
## get_consensus
## Get a consensus string from a DNA/RNA motif.
m <- create_motif()["motif"]
apply(m, 2, get_consensus)

#######################################################################
## get_consensusAA
## Get a consensus string from an amino acid motif. Unless each position
## is clearly dominated by a single amino acid, the resulting string will
## likely be useless.
m <- create_motif(alphabet = "AA")["motif"]
apply(m, 2, get_consensusAA, type = "PPM")

#######################################################################
## get_match
## Get all possible motif matches above input score
get_matches(examplemotif, 0)
get_matches(examplemotif0, 0, allow.nonfinite = TRUE)

#######################################################################
## get_scores
## Get all possible scores for a motif
length(get_scores(examplemotif))
get_scores(examplemotif)
get_scores(examplemotif0, allow.nonfinite = TRUE)

#######################################################################
## icm_to_ppm
## Do the opposite of ppm_to_icm.
m <- create_motif(type = "ICM")["motif"]
apply(m, 2, icm_to_ppm)

#######################################################################
## motif_score
## Calculate motif score from different thresholds
m <- normalize(examplemotif)
motif_score(m, c(0, 0.8, 1))
motif_score(examplemotif0, c(0, 0.8, 1), allow.nonfinite = TRUE,
   threshold.type = "fromzero")

#######################################################################
## log_string_pval
## Get the log of a string-formatted p-value
log_string_pval("1e-400")

#######################################################################
## pcm_to_ppm
## Go from a count type motif to a probability type motif.
m <- create_motif(type = "PCM", nsites = 50)["motif"]
apply(m, 2, pcm_to_ppm, pseudocount = 1)

#######################################################################
## position_icscore
## Similar to ppm_to_icm, except this calculates the position sum.
m <- create_motif()["motif"]
apply(m, 2, position_icscore, type = "PPM", bkg = rep(0.25, 4))

#######################################################################
## ppm_to_icm
## Convert one column from a probability type motif to an information
## content type motif.
m <- create_motif(nsites = 100, pseudocount = 0.8)["motif"]
apply(m, 2, ppm_to_icm, nsites = 100, bkg = rep(0.25, 4))

#######################################################################
## ppm_to_pcm
## Do the opposite of pcm_to_ppm.
m <- create_motif()["motif"]
apply(m, 2, ppm_to_pcm, nsites = 50)

#######################################################################
## ppm_to_pwm
## Go from a probability type motif to a weight type motif.
m <- create_motif()["motif"]
apply(m, 2, ppm_to_pwm, nsites = 100, bkg = rep(0.25, 4))

#######################################################################
## prob_match, prob_match_bkg
## Calculate probability of a particular match based on background
## frequencies
prob_match(examplemotif, "TATATAT")
## Since this motif has a uniform background, the probability of
## finding any motif hit within the sequence is equal
prob_match(examplemotif, "TATATAG")
m <- examplemotif
m["bkg"] <- c(0.3, 0.2, 0.2, 0.3)
prob_match(m, "TATATAT")
## The prob_match_bkg alternative allows you to simply pass along the
## background frequencies
prob_match_bkg(c(A=0.3, C=0.2, G=0.2, T=0.3), c("TATATAT", "TATATAG"))

#######################################################################
## pwm_to_ppm
## Do the opposite of ppm_to_pwm.
m <- create_motif(type = "PWM")["motif"]
apply(m, 2, pwm_to_ppm, bkg = rep(0.25, 4))

#######################################################################
## Note that not all type conversions can be done directly; for those
## type conversions which are unavailable, universalmotif just chains
## together others (i.e. from PCM -> ICM => pcm_to_ppm -> ppm_to_icm)

#######################################################################
## round_motif
## Round down letter scores to 0
m <- create_motif()
## Remove letters from positions which are less than 5% of the total
## position:
round_motif(m, pct.tolerance = 0.05)

#######################################################################
## score_match
## Calculate score of a particular match
score_match(examplemotif, "TATATAT")
score_match(examplemotif, "TATATAG")
score_match(examplemotif0, "TATATAT", allow.nonfinite = TRUE)
score_match(examplemotif0, "TATATAG", allow.nonfinite = TRUE)

#######################################################################
## summarise_motifs
## Create a data.frame of information based on a list of motifs.
m1 <- create_motif()
m2 <- create_motif()
m3 <- create_motif()
summarise_motifs(list(m1, m2, m3))

#######################################################################
## ungap
## Unset motif's gap status. Does not delete actual gap data unless
## delete = TRUE.
m <- create_motif()
m <- add_gap(m, 3, 2, 4)
m <- ungap(m)
# Restore gap data:
m <- add_gap(m)