SMTrackR
2025-06-12
Contents
0.1 Introduction
Single-molecule assays like NOMe-seq, and dSMF are superior to DNase-seq and ATAC-seq as they do not cleave DNA (see schematics below). Thus, they enable quantification of all three i.e., protein-free, Transcription Factor-bound and histone-complex-bound states. But a user-friendly tool to visualize and quantify such states is lacking. SMTrackR, a Bioconductor package visualizes protein-DNA binding states on individual sequenced DNA molecules. SMTrackR queries the single-molecule footprint database we built and hosted at Galaxy Server. It comprises of BigBed files generated from NOMe-seq and dSMF datasets. SMTrackR exploits UCSC REST API to query a BigBed file and plot footprint heatmap categorized in different binding states as well as report their occupancies.
0.2 Installation
# if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("SMTrackR")
# rmarkdown::html_vignette0.3 Loading the package
library(SMTrackR)0.4 Plot Single Molecules from SMF Data
SMTrackR::plotFootprints() will generate the heatmap pdf file stored in <target_dir>/plots/<label>.heatmap.pdf if target_dir option is provided, otherwise the plot will be saved in the tempdir, the fuction reports the path in both the cases.
A Gviz compatible code can also be generated by running the generateGvizCodeforSMF function. See the code chunk below.
SMTrackR::plotFootprints(organism = "mmusculus", model = "8cell", condition = "WT",
genome_assembly = "mm10", type = "SMF", chromosome = "chr5",
start = 113847750, end = 113847780, tr = "8cell",
label = "8cell", fp_cap = 50, remove_dup = TRUE, target_dir = "smf")## Using UCSC API to get JSON dump from BigBed Track ...## Heatmap is saved in the file smf/plots/8cell.heatmap.pdf## ## [1] "smf/plots/8cell.heatmap.pdf"SMTrackR::generateGvizCodeforSMF(organism = "mmusculus", model = "8cell", condition = "WT",
genome_assembly = "mm10", type = "SMF", chromosome = "chr5",
start = 113847750, end = 113847780, tr = "8cell",
label = "8cell", fp_cap = 50, remove_dup = TRUE,
gviz_left = 1000, gviz_right = 1000, target_dir = "smf")## A R code file smf/8cell.gviz.R is written. Please run the command: Rscript smf/8cell.gviz.R to place the heatmap on gene track!0.5 Plot Single Molecules from dSMF Data
To learn more about dSMF data, please read the article Krebs et al., Mol. Cell., 2017. Briefly, it uses two exogenous methyltransferases in tandem to map protein-DNA binding on individual sequenced DNA molecules.
The command below will generate plots/peak229.plot.pdf file.
SMTrackR::plotFootprints(target_dir = "dsmf")## Using UCSC API to get JSON dump from BigBed Track ...## Heatmap is saved in the file dsmf/plots/peak229.heatmap.pdf## ## [1] "dsmf/plots/peak229.heatmap.pdf"0.6 Plot Single Molecules from Nanopore Data
Running the command below will generate a pdf file plots/smac_seq.plot.pdf.
SMTrackR::plotMethylationCallsNanopore(target_dir = "nanopore") ## Using UCSC API to get JSON dump from BigBed Track ...## Heatmap is saved in the file nanopore/plots/smac_seq.heatmap.pdf## 0.7 Listing Available Track
All uptodate tracks are available here and can be listed using the following command.
#SMTrackR::listTracks()0.8 Session Info
sessionInfo()## R Under development (unstable) (2026-03-05 r89546)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.23-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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] SMTrackR_0.99.6 BiocStyle_2.39.0
##
## loaded via a namespace (and not attached):
## [1] SummarizedExperiment_1.41.1 httr2_1.2.2
## [3] rjson_0.2.23 xfun_0.56
## [5] bslib_0.10.0 Biobase_2.71.0
## [7] lattice_0.22-9 vctrs_0.7.1
## [9] tools_4.6.0 bitops_1.0-9
## [11] generics_0.1.4 stats4_4.6.0
## [13] curl_7.0.0 parallel_4.6.0
## [15] tibble_3.3.1 RSQLite_2.4.6
## [17] blob_1.3.0 pkgconfig_2.0.3
## [19] Matrix_1.7-4 dbplyr_2.5.2
## [21] S4Vectors_0.49.0 cigarillo_1.1.0
## [23] lifecycle_1.0.5 compiler_4.6.0
## [25] stringr_1.6.0 Rsamtools_2.27.1
## [27] Biostrings_2.79.5 Seqinfo_1.1.0
## [29] codetools_0.2-20 htmltools_0.5.9
## [31] sass_0.4.10 RCurl_1.98-1.17
## [33] yaml_2.3.12 pillar_1.11.1
## [35] crayon_1.5.3 jquerylib_0.1.4
## [37] BiocParallel_1.45.0 cachem_1.1.0
## [39] DelayedArray_0.37.0 abind_1.4-8
## [41] tidyselect_1.2.1 digest_0.6.39
## [43] stringi_1.8.7 purrr_1.2.1
## [45] dplyr_1.2.0 restfulr_0.0.16
## [47] bookdown_0.46 fastmap_1.2.0
## [49] grid_4.6.0 cli_3.6.5
## [51] SparseArray_1.11.11 magrittr_2.0.4
## [53] S4Arrays_1.11.1 XML_3.99-0.22
## [55] withr_3.0.2 filelock_1.0.3
## [57] rappdirs_0.3.4 bit64_4.6.0-1
## [59] rmarkdown_2.30 XVector_0.51.0
## [61] httr_1.4.8 matrixStats_1.5.0
## [63] bit_4.6.0 otel_0.2.0
## [65] memoise_2.0.1 evaluate_1.0.5
## [67] knitr_1.51 GenomicRanges_1.63.1
## [69] IRanges_2.45.0 BiocIO_1.21.0
## [71] BiocFileCache_3.1.0 rtracklayer_1.71.3
## [73] rlang_1.1.7 glue_1.8.0
## [75] DBI_1.3.0 BiocManager_1.30.27
## [77] BiocGenerics_0.57.0 jsonlite_2.0.0
## [79] R6_2.6.1 MatrixGenerics_1.23.0
## [81] GenomicAlignments_1.47.0