Find all PWM matches within a set of sequences
Usage
getPwmMatches(
pwm,
stringset,
rc = TRUE,
min_score = "80%",
best_only = FALSE,
break_ties = c("all", "random", "first", "last", "central"),
mc.cores = 1,
...
)Arguments
- pwm
A Position Weight Matrix, list of PWMs or universalmotif list
- stringset
An XStringSet
- rc
logical(1) Also find matches using the reverse complement of pwm
- min_score
The minimum score to return a match
- best_only
logical(1) Only return the best match
- break_ties
Method for breaking ties when only returning the best match Ignored when all matches are returned (the default)
- mc.cores
Passed to mclapply if passing multiple PWMs
- ...
Passed to matchPWM
Value
A DataFrame with columns: seq, score, direction, start,
end, from_centre, seq_width, and match
The first three columns describe the sequence with matches, the score of
the match and whether the match was found using the forward or reverse PWM.
The columns start, end and width describe the where the match was found
in the sequence, whilst from_centre defines the distance between the centre
of the match and the centre of the sequence being queried.
The final column contains the matching fragment of the sequence as an
XStringSet.
When passing a list of PWMs, a list of the above DataFrames will be returned.
Details
Taking a set of sequences as an XStringSet, find all matches above the
supplied score (i.e. threshold) for a single Position Weight Matrix (PWM),
generally representing a transcription factor binding motif.
By default, matches are performed using the PWM as provided and the reverse
complement, however this can easily be disabled by setting rc = FALSE.
The function relies heavily on matchPWM and Views for speed.
When choosing to return the best match (best_only = TRUE), only the match
with the highest score is returned for each sequence.
Should there be tied scores, the best match can be chosen as either the first,
last, most central, all tied matches, or choosing one at random (the default).
Examples
## Load the example PWM
data("ex_pfm")
esr1 <- ex_pfm$ESR1
## Load the example Peaks
data("ar_er_seq")
## Return all matches
getPwmMatches(esr1, ar_er_seq)
#> DataFrame with 22 rows and 8 columns
#> seq score direction start end from_centre seq_width
#> <integer> <numeric> <factor> <integer> <integer> <numeric> <integer>
#> 1 29 18.0880 F 193 207 0 400
#> 2 34 20.8412 R 321 335 128 400
#> 3 60 17.8088 R 154 168 -39 400
#> 4 62 17.5548 R 206 220 13 400
#> 5 98 20.2850 F 13 27 -180 400
#> ... ... ... ... ... ... ... ...
#> 18 478 18.9927 R 134 148 -59 400
#> 19 517 19.0738 F 223 237 30 400
#> 20 552 18.4739 F 232 246 39 400
#> 21 575 17.7611 R 4 18 -189 400
#> 22 646 17.5586 R 209 223 16 400
#> match
#> <DNAStringSet>
#> 1 AGGTCACCCTGGCCC
#> 2 AGGTCACCGTGACCC
#> 3 AGGTGACCCTGACCT
#> 4 GGGTCACACTGTCCT
#> 5 AGGTCACAATGACCT
#> ... ...
#> 18 AGGTCACCCTGACCG
#> 19 GGGTCAGCATGACCT
#> 20 AGGACACACTGACCT
#> 21 AGGTCACCCTAACCT
#> 22 AGGTTAGCCTGACCT
## Just the best match
getPwmMatches(esr1, ar_er_seq, best_only = TRUE)
#> DataFrame with 22 rows and 8 columns
#> seq score direction start end from_centre seq_width
#> <integer> <numeric> <factor> <integer> <integer> <numeric> <integer>
#> 1 29 18.0880 F 193 207 0 400
#> 2 34 20.8412 R 321 335 128 400
#> 3 60 17.8088 R 154 168 -39 400
#> 4 62 17.5548 R 206 220 13 400
#> 5 98 20.2850 F 13 27 -180 400
#> ... ... ... ... ... ... ... ...
#> 18 478 18.9927 R 134 148 -59 400
#> 19 517 19.0738 F 223 237 30 400
#> 20 552 18.4739 F 232 246 39 400
#> 21 575 17.7611 R 4 18 -189 400
#> 22 646 17.5586 R 209 223 16 400
#> match
#> <DNAStringSet>
#> 1 AGGTCACCCTGGCCC
#> 2 AGGTCACCGTGACCC
#> 3 AGGTGACCCTGACCT
#> 4 GGGTCACACTGTCCT
#> 5 AGGTCACAATGACCT
#> ... ...
#> 18 AGGTCACCCTGACCG
#> 19 GGGTCAGCATGACCT
#> 20 AGGACACACTGACCT
#> 21 AGGTCACCCTAACCT
#> 22 AGGTTAGCCTGACCT
## Apply multiple PWMs as a list
getPwmMatches(ex_pfm, ar_er_seq, best_only = TRUE)
#> $ESR1
#> DataFrame with 22 rows and 8 columns
#> seq score direction start end from_centre seq_width
#> <integer> <numeric> <factor> <integer> <integer> <numeric> <integer>
#> 1 29 18.0880 F 193 207 0 400
#> 2 34 20.8412 R 321 335 128 400
#> 3 60 17.8088 R 154 168 -39 400
#> 4 62 17.5548 R 206 220 13 400
#> 5 98 20.2850 F 13 27 -180 400
#> ... ... ... ... ... ... ... ...
#> 18 478 18.9927 R 134 148 -59 400
#> 19 517 19.0738 F 223 237 30 400
#> 20 552 18.4739 F 232 246 39 400
#> 21 575 17.7611 R 4 18 -189 400
#> 22 646 17.5586 R 209 223 16 400
#> match
#> <DNAStringSet>
#> 1 AGGTCACCCTGGCCC
#> 2 AGGTCACCGTGACCC
#> 3 AGGTGACCCTGACCT
#> 4 GGGTCACACTGTCCT
#> 5 AGGTCACAATGACCT
#> ... ...
#> 18 AGGTCACCCTGACCG
#> 19 GGGTCAGCATGACCT
#> 20 AGGACACACTGACCT
#> 21 AGGTCACCCTAACCT
#> 22 AGGTTAGCCTGACCT
#>
#> $ANDR
#> DataFrame with 8 rows and 8 columns
#> seq score direction start end from_centre seq_width
#> <integer> <numeric> <factor> <integer> <integer> <numeric> <integer>
#> 1 27 18.9055 F 58 75 -133.5 400
#> 2 110 20.5599 F 235 252 43.5 400
#> 3 285 18.9230 F 205 222 13.5 400
#> 4 519 18.9718 R 264 281 72.5 400
#> 5 701 20.3572 F 329 346 137.5 400
#> 6 704 20.5870 F 68 85 -123.5 400
#> 7 708 20.9669 F 278 295 86.5 400
#> 8 833 23.0102 R 167 184 -24.5 400
#> match
#> <DNAStringSet>
#> 1 TGTTCTTTTTTGTTGATT
#> 2 TGTCCTTTTCTGTTTATT
#> 3 TGTTCCTCTCTGTTTACC
#> 4 TGTTCAGCTTTGTTTGCT
#> 5 TGTTCTTTTGTATTTGCT
#> 6 TGTTCTTCTATGTTTATT
#> 7 TGTTCTTTATTATTTGCT
#> 8 TGTTCTTTTTTGTTTGTT
#>
#> $FOXA1
#> DataFrame with 107 rows and 8 columns
#> seq score direction start end from_centre seq_width
#> <integer> <numeric> <factor> <integer> <integer> <numeric> <integer>
#> 1 12 14.0694 F 199 210 4.5 400
#> 2 16 14.4605 F 177 188 -17.5 400
#> 3 17 15.1441 F 341 352 146.5 400
#> 4 18 14.4417 F 301 312 106.5 400
#> 5 21 14.6369 R 206 217 11.5 400
#> ... ... ... ... ... ... ... ...
#> 103 793 14.3562 F 194 205 -0.5 400
#> 104 816 14.1175 R 63 74 -131.5 400
#> 105 817 14.9185 R 291 302 96.5 400
#> 106 826 14.5836 F 261 272 66.5 400
#> 107 844 14.3461 F 40 51 -154.5 400
#> match
#> <DNAStringSet>
#> 1 TGTTTGCTTTTG
#> 2 TGTTTACTTTCC
#> 3 TGTTTATTTAGG
#> 4 TGTTTATTCTGG
#> 5 TGTTTACTCAAC
#> ... ...
#> 103 TGTTTACTTTAA
#> 104 TGTTTATTTTAG
#> 105 TGTTTACACAGT
#> 106 TATTTACTTTAG
#> 107 TGTTTACTTTCT
#>
#> $ZN143
#> DataFrame with 15 rows and 8 columns
#> seq score direction start end from_centre seq_width
#> <integer> <numeric> <factor> <integer> <integer> <numeric> <integer>
#> 1 30 26.8427 F 166 187 -23.5 400
#> 2 67 29.0591 F 210 231 20.5 400
#> 3 118 29.0063 R 205 226 15.5 400
#> 4 182 28.0840 F 225 246 35.5 400
#> 5 330 27.3485 R 196 217 6.5 400
#> ... ... ... ... ... ... ... ...
#> 11 569 28.1714 R 10 31 -179.5 400
#> 12 750 28.4852 R 151 172 -38.5 400
#> 13 829 26.6710 R 151 172 -38.5 400
#> 14 836 28.9222 R 166 187 -23.5 400
#> 15 837 30.0534 F 216 237 26.5 400
#> match
#> <DNAStringSet>
#> 1 AGCCTGCCGGGAGATGTAGTTC
#> 2 GGCATGCTGGGATTTGTAGTCT
#> 3 TGCCTCCTGGGAAATGTAGTCC
#> 4 TGCATGCTGGGAACTGTAGTCT
#> 5 AGCCTTGTGGGAGTTGTAGTTT
#> ... ...
#> 11 GGCATTTTGGGAGTTGTAGTTT
#> 12 CGCATGCTGGGAATTGTAGTTC
#> 13 TGCCCGCTGGGAACTGTAGTCC
#> 14 TGCATGCTGGGATTTGTAGTCC
#> 15 TGCATGCTGGGAGTTGTAGTCT
#>
#> $ZN281
#> DataFrame with 13 rows and 8 columns
#> seq score direction start end from_centre seq_width
#> <integer> <numeric> <factor> <integer> <integer> <numeric> <integer>
#> 1 109 19.6553 R 369 383 176 400
#> 2 118 20.1871 F 60 74 -133 400
#> 3 122 19.3263 F 95 109 -98 400
#> 4 171 18.5467 R 84 98 -109 400
#> 5 192 19.1012 F 260 274 67 400
#> ... ... ... ... ... ... ... ...
#> 9 456 21.2625 R 343 357 150 400
#> 10 507 18.6243 R 175 189 -18 400
#> 11 668 20.0793 R 168 182 -25 400
#> 12 763 22.6040 R 274 288 81 400
#> 13 764 18.8379 R 310 324 117 400
#> match
#> <DNAStringSet>
#> 1 AGTTGGGGGAGGGGC
#> 2 GGCGGGGGGAGGGGA
#> 3 GAATGGGGGAGGGGC
#> 4 GGATGGGGGAAGGGG
#> 5 GGGAGGGGGCGGGGG
#> ... ...
#> 9 CGGTGGGGGAGGGGG
#> 10 GGGAGGGGGAGGGAG
#> 11 GGGTGGGGGTGGGGG
#> 12 GGGTGGGGGAGGGGG
#> 13 AGTGGGGGGAGGGGA
#>