Sequence-based TF affinity scoring can be conducted with the FIMO suite, see @Sonawane2017. We have serialized an object with references to FIMO outputs for 16 TFs.
## GenomicFiles object with 1 ranges and 16 files:
## files: M0635_1.02sort.bed.gz, M3433_1.02sort.bed.gz, ..., M6159_1.02sort.bed.gz, M6497_1.02sort.bed.gz
## detail: use files(), rowRanges(), colData(), ...While the token bed is used in the filenames, the files are
not actually bed format!
scanTabixWe can use reduceByRange to import selected scans.
if (.Platform$OS.type != "windows") {
si = TFutils::seqinfo_hg19_chr17
myg = GRanges("chr17", IRanges(38.07e6,38.09e6), seqinfo=si)
colnames(fimo16) = fimo16$HGNC
lk2 = GenomicFiles::reduceByRange(fimo16[, c("POU2F1", "VDR")],
MAP=function(r,f) scanTabix(f, param=r))
str(lk2)
}## List of 1
## $ :List of 2
## ..$ POU2F1:List of 1
## .. ..$ chr17:38077000-38084000: chr [1:12] "chr17\t38077313\t38077331\tchr17:38077313-38077331\t2.15306\t+\t0.000352" "chr17\t38078549\t38078567\tchr17:38078549-38078567\t0.102041\t-\t0.000634" "chr17\t38078556\t38078574\tchr17:38078556-38078574\t-0.0408163\t-\t0.00066" "chr17\t38080045\t38080063\tchr17:38080045-38080063\t1.66327\t-\t0.000407" ...
## ..$ VDR :List of 1
## .. ..$ chr17:38077000-38084000: chr [1:18] "chr17\t38077445\t38077460\tchr17:38077445-38077460\t-1.9899\t-\t0.000666" "chr17\t38078536\t38078551\tchr17:38078536-38078551\t-2.66667\t-\t0.0008" "chr17\t38078574\t38078589\tchr17:38078574-38078589\t1.65657\t+\t0.000235" "chr17\t38078796\t38078811\tchr17:38078796-38078811\t2.07071\t-\t0.000207" ...This result can be massaged into a GRanges or other desirable structure.
fimo_granges takes care of this.
#fimo_ranges = function(gf, query) { # prototypical code
# rowRanges(gf) = query
# ans = GenomicFiles::reduceByRange(gf, MAP=function(r,f) scanTabix(f, param=r))
# ans = unlist(ans, recursive=FALSE) # drop top list structure
# tabs = lapply(ans, lapply, function(x) {
# con = textConnection(x)
# on.exit(close(con))
# dtf = read.delim(con, h=FALSE, stringsAsFactors=FALSE, sep="\t")
# colnames(dtf) = c("chr", "start", "end", "rname", "score", "dir", "pval")
# ans = with(dtf, GRanges(seqnames=chr, IRanges(start, end),
# rname=rname, score=score, dir=dir, pval=pval))
# ans
# })
# GRangesList(unlist(tabs, recursive=FALSE))
#}
if (.Platform$OS.type != "windows") {
rr = fimo_granges(fimo16[, c("POU2F1", "VDR")], myg)
rr
}## $POU2F1
## $POU2F1$`chr17:38070000-38090000`
## GRanges object with 76 ranges and 4 metadata columns:
## seqnames ranges strand | rname score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr17 38070239-38070257 * | chr17:38070239-38070.. -1.5408200
## [2] chr17 38070579-38070597 * | chr17:38070579-38070.. -0.9693880
## [3] chr17 38070851-38070869 * | chr17:38070851-38070.. 0.1224490
## [4] chr17 38071025-38071043 * | chr17:38071025-38071.. 0.0918367
## [5] chr17 38071253-38071271 * | chr17:38071253-38071.. 3.6734700
## ... ... ... ... . ... ...
## [72] chr17 38088602-38088620 * | chr17:38088602-38088.. 4.06122
## [73] chr17 38088637-38088655 * | chr17:38088637-38088.. 11.69390
## [74] chr17 38089141-38089159 * | chr17:38089141-38089.. 13.18370
## [75] chr17 38089439-38089457 * | chr17:38089439-38089.. -1.35714
## [76] chr17 38089822-38089840 * | chr17:38089822-38089.. 3.67347
## dir pval
## <character> <numeric>
## [1] + 0.000989
## [2] - 0.000849
## [3] - 0.000631
## [4] - 0.000636
## [5] + 0.000222
## ... ... ...
## [72] + 1.98e-04
## [73] - 1.32e-05
## [74] - 7.09e-06
## [75] - 9.42e-04
## [76] - 2.22e-04
## -------
## seqinfo: 1 sequence from hg19 genome
##
##
## $VDR
## $VDR$`chr17:38070000-38090000`
## GRanges object with 40 ranges and 4 metadata columns:
## seqnames ranges strand | rname score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr17 38070016-38070031 * | chr17:38070016-38070.. 0.0505051
## [2] chr17 38070387-38070402 * | chr17:38070387-38070.. -3.3838400
## [3] chr17 38070925-38070940 * | chr17:38070925-38070.. 5.7171700
## [4] chr17 38071183-38071198 * | chr17:38071183-38071.. -0.6767680
## [5] chr17 38072289-38072304 * | chr17:38072289-38072.. -0.3333330
## ... ... ... ... . ... ...
## [36] chr17 38086915-38086930 * | chr17:38086915-38086.. -0.353535
## [37] chr17 38087304-38087319 * | chr17:38087304-38087.. 0.101010
## [38] chr17 38087866-38087881 * | chr17:38087866-38087.. 5.343430
## [39] chr17 38088893-38088908 * | chr17:38088893-38088.. -0.111111
## [40] chr17 38089214-38089229 * | chr17:38089214-38089.. 1.101010
## dir pval
## <character> <numeric>
## [1] + 3.77e-04
## [2] + 9.66e-04
## [3] - 6.44e-05
## [4] + 4.63e-04
## [5] - 4.21e-04
## ... ... ...
## [36] - 0.000423
## [37] - 0.000371
## [38] + 0.000073
## [39] - 0.000395
## [40] - 0.000277
## -------
## seqinfo: 1 sequence from hg19 genome## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] UpSetR_1.4.0 magrittr_2.0.3
## [3] dplyr_1.1.4 gwascat_2.40.0
## [5] GSEABase_1.70.0 graph_1.86.0
## [7] annotate_1.86.0 XML_3.99-0.18
## [9] png_0.1-8 ggplot2_3.5.2
## [11] knitr_1.50 data.table_1.17.0
## [13] GO.db_3.21.0 GenomicFiles_1.44.0
## [15] rtracklayer_1.68.0 Rsamtools_2.24.0
## [17] Biostrings_2.76.0 XVector_0.48.0
## [19] BiocParallel_1.42.0 SummarizedExperiment_1.38.0
## [21] GenomicRanges_1.60.0 GenomeInfoDb_1.44.0
## [23] MatrixGenerics_1.20.0 matrixStats_1.5.0
## [25] org.Hs.eg.db_3.21.0 AnnotationDbi_1.70.0
## [27] IRanges_2.42.0 S4Vectors_0.46.0
## [29] Biobase_2.68.0 BiocGenerics_0.54.0
## [31] generics_0.1.3 TFutils_1.28.0
## [33] BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9 gridExtra_2.3
## [4] readxl_1.4.5 rlang_1.1.6 compiler_4.5.0
## [7] RSQLite_2.3.9 GenomicFeatures_1.60.0 vctrs_0.6.5
## [10] pkgconfig_2.0.3 crayon_1.5.3 fastmap_1.2.0
## [13] dbplyr_2.5.0 labeling_0.4.3 promises_1.3.2
## [16] rmarkdown_2.29 UCSC.utils_1.4.0 bit_4.6.0
## [19] xfun_0.52 zlibbioc_1.54.0 cachem_1.1.0
## [22] jsonlite_2.0.0 blob_1.2.4 later_1.4.2
## [25] DelayedArray_0.34.0 parallel_4.5.0 R6_2.6.1
## [28] VariantAnnotation_1.54.0 bslib_0.9.0 jquerylib_0.1.4
## [31] cellranger_1.1.0 bookdown_0.43 Rcpp_1.0.14
## [34] splines_4.5.0 httpuv_1.6.15 Matrix_1.7-3
## [37] tidyselect_1.2.1 abind_1.4-8 yaml_2.3.10
## [40] codetools_0.2-20 miniUI_0.1.1.1 curl_6.2.2
## [43] plyr_1.8.9 lattice_0.22-7 tibble_3.2.1
## [46] withr_3.0.2 shiny_1.10.0 KEGGREST_1.48.0
## [49] evaluate_1.0.3 survival_3.8-3 BiocFileCache_2.16.0
## [52] snpStats_1.58.0 pillar_1.10.2 BiocManager_1.30.25
## [55] filelock_1.0.3 RCurl_1.98-1.17 munsell_0.5.1
## [58] scales_1.3.0 xtable_1.8-4 glue_1.8.0
## [61] tools_4.5.0 BiocIO_1.18.0 BSgenome_1.76.0
## [64] GenomicAlignments_1.44.0 colorspace_2.1-1 GenomeInfoDbData_1.2.14
## [67] restfulr_0.0.15 cli_3.6.4 S4Arrays_1.8.0
## [70] gtable_0.3.6 sass_0.4.10 digest_0.6.37
## [73] SparseArray_1.8.0 farver_2.1.2 rjson_0.2.23
## [76] memoise_2.0.1 htmltools_0.5.8.1 lifecycle_1.0.4
## [79] httr_1.4.7 mime_0.13 bit64_4.6.0-1