misha (version 5.3.1)

gseq.pwm: Score DNA sequences with a PWM over a region of interest

Description

Scores full DNA sequences using a Position Weight Matrix (PWM) over a specified region of interest (ROI). The ROI is defined by start_pos and end_pos (1-based, inclusive), with optional extension controlled by extend. All reported positions are on the full input sequence.

Usage

gseq.pwm(
  seqs,
  pssm,
  mode = c("lse", "max", "pos", "count"),
  bidirect = TRUE,
  strand = 0L,
  score.thresh = 0,
  start_pos = NULL,
  end_pos = NULL,
  extend = FALSE,
  spat.factor = NULL,
  spat.bin = 1L,
  spat.min = NULL,
  spat.max = NULL,
  return_strand = FALSE,
  skip_gaps = TRUE,
  gap_chars = c("-", "."),
  neutral_chars = c("N", "n", "*"),
  neutral_chars_policy = c("average", "log_quarter", "na"),
  prior = 0.01
)

Value

Numeric vector (for "lse"/"max"/"count" modes), integer vector (for "pos" mode), or data.frame with pos and strand columns (for "pos" mode with

return_strand=TRUE). Returns NA when no valid windows exist.

Arguments

seqs

character vector of DNA sequences (A/C/G/T/N; case-insensitive)

pssm

numeric matrix or data frame with columns named A, C, G, T (additional columns are allowed and will be ignored)

mode

character; one of "lse", "max", "pos", or "count"

bidirect

logical; if TRUE, scans both strands (default: TRUE)

strand

integer; 1=forward, -1=reverse, 0=both strands (default: 0)

score.thresh

numeric; score threshold for mode="count" (default: 0)

start_pos

integer or NULL; 1-based inclusive start of ROI (default: 1)

end_pos

integer or NULL; 1-based inclusive end of ROI (default: sequence length)

extend

logical or integer; extension of allowed window starts (default: FALSE)

spat.factor

numeric vector; spatial weighting factors (optional)

spat.bin

integer; bin size for spatial weighting

spat.min

numeric; start of scanning window

spat.max

numeric; end of scanning window

return_strand

logical; if TRUE and mode="pos", returns data.frame with pos and strand columns

skip_gaps

logical; if TRUE, treat gap characters as holes and skip them while scanning. Windows are w consecutive non-gap bases (default: TRUE)

gap_chars

character vector; which characters count as gaps (default: c("-", "."))

neutral_chars

character vector; bases treated as unknown and scored with the average log probability per position (default: c("N", "n", "*"))

neutral_chars_policy

character string; how to treat neutral characters. One of "average" (default; use the column's mean log-probability), "log_quarter" (always use log(1/4)), or "na" (return NA when a neutral character is encountered in the scanning window).

prior

numeric; pseudocount added to frequencies (default: 0.01). Set to 0 for no pseudocounts.

Details

This function scores DNA sequences directly without requiring a genomics database. For detailed documentation on PWM scoring modes, parameters, and spatial weighting, see gvtrack.create (functions "pwm", "pwm.max", "pwm.max.pos", "pwm.count").

The ROI (region of interest) is defined by start_pos and end_pos. The extend parameter controls whether motif matches can extend beyond the ROI boundaries.

When skip_gaps=TRUE, characters specified in gap_chars are treated as gaps. Windows are defined as w consecutive non-gap bases. All positions (pos) are reported as 1-based indices on the original full sequence (including gaps). start_pos and end_pos are interpreted as physical coordinates on the full sequence.

Neutral characters (neutral_chars, default c("N", "n", "*")) are treated as unknown bases in both orientations. Each neutral contributes the mean log-probability of the corresponding PSSM column, yielding identical penalties on forward and reverse strands without hard-coded background scores. In mode = "max" the reported value is the single best strand score after applying any spatial weights; forward and reverse contributions are not aggregated. This matches the default behavior of the PWM virtual tracks (pwm.max, pwm.max.pos, etc.).

See Also

gvtrack.create for detailed PWM parameter documentation

Examples

if (FALSE) {
# Create a PSSM (position-specific scoring matrix) with frequency values
pssm <- matrix(
    c(
        0.7, 0.1, 0.1, 0.1, # Position 1: mostly A
        0.1, 0.7, 0.1, 0.1, # Position 2: mostly C
        0.1, 0.1, 0.7, 0.1, # Position 3: mostly G
        0.1, 0.1, 0.1, 0.7 # Position 4: mostly T
    ),
    ncol = 4, byrow = TRUE
)
colnames(pssm) <- c("A", "C", "G", "T")

# Example sequences
seqs <- c("ACGTACGTACGT", "GGGGACGTCCCC", "TTTTTTTTTTT")

# Score sequences using log-sum-exp (default mode)
gseq.pwm(seqs, pssm, mode = "lse")

# Get maximum score
gseq.pwm(seqs, pssm, mode = "max")

# Find position of best match
gseq.pwm(seqs, pssm, mode = "pos")

# Find position with strand information
gseq.pwm(seqs, pssm, mode = "pos", bidirect = TRUE, return_strand = TRUE)

# Count matches above threshold
gseq.pwm(seqs, pssm, mode = "count", score.thresh = 0.5)

# Score only a region of interest
gseq.pwm(seqs, pssm, mode = "max", start_pos = 3, end_pos = 10)

# Allow matches to extend beyond ROI boundaries
gseq.pwm(seqs, pssm, mode = "count", start_pos = 5, end_pos = 8, extend = TRUE)

# Spatial weighting example: higher weight in the center
spatial_weights <- c(0.5, 1.0, 2.0, 1.0, 0.5)
gseq.pwm(seqs, pssm,
    mode = "lse",
    spat.factor = spatial_weights,
    spat.bin = 2
)
}