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RegRegSEA: a web server for regulatory region set enrichment analysis of epigenomic data.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Results › Case study 1: re-analysis of down syndrome brain methylome ↔ R/plotting.R, lines 134–236 · score 0.69 · absolute NES, positive NES, Top enriched, enrichment scores, Bars, enhancers
  2. [2] § Results › The RegRegSEA web server › Output and visualizations ↔ R/plotting.R, lines 134–236 · score 0.69 · positive NES, top enriched, enrichment score, bars, cutoff, FDR

Paper

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The authors' code

R · 236 lines · 7.9 KB · MIT · 2 matches

  1. #' Plot Running Enrichment Score
  2. #'
  3. #' Creates a two-panel plot showing the running enrichment score and
  4. #' ranked statistic for a specific region set.
  5. #'
  6. #' @param ranking Named and sorted vector of genomic regions and their ranking score
  7. #' @param region_sets List of region sets (from mapGRangesToRegionSets)
  8. #' @param region_set_name Name of the region set to plot
  9. #' @param title Plot title (optional, defaults to region set name)
  10. #' @param color Color for enrichment score curve (default: "grey40")
  11. #' @return ggplot object with running enrichment score plot
  12. #' @export
  13. #' @importFrom fgsea plotEnrichmentData
  14. #' @importFrom ggplot2 ggplot aes geom_line geom_segment geom_hline geom_tile
  15. #' @importFrom ggplot2 labs theme_classic theme element_blank element_text
  16. #' @importFrom ggplot2 coord_cartesian scale_y_continuous scale_fill_gradient2
  17. #' @importFrom ggplot2 scale_x_continuous guides guide_colorbar margin unit
  18. #' @importFrom patchwork plot_layout
  19. #' @importFrom scales pretty_breaks
  20. #' @examples
  21. #' \dontrun{
  22. #' # Plot running enrichment score for specific region set
  23. #' p <- plotRunningES(ranking, region_sets, "encode_tfbs_CTCF_hg19.bed")
  24. #' print(p)
  25. #' }
  26. plotRunningES <- function(ranking, region_sets, region_set_name,
  27. title = NULL, color = "grey40") {
  29. # Input validation
  30. if (!region_set_name %in% names(region_sets)) {
  31. stop(sprintf("Region set '%s' not found in region_sets", region_set_name))
  32. }
  34. # Get the specific region set
  35. pathway <- region_sets[[region_set_name]]
  37. # Generate plot data using fgsea's function
  38. plot_data <- plotEnrichmentData(
  39. pathway = pathway,
  40. stats = ranking,
  41. gseaParam = 1
  42. )
  44. # Set title
  45. if (is.null(title)) {
  46. title <- region_set_name
  47. }
  49. # Calculate plot limits
  50. y_min <- min(plot_data$curve$ES) - abs(min(plot_data$curve$ES)) * 0.1 - 0.07
  51. y_max <- max(plot_data$curve$ES) + abs(max(plot_data$curve$ES)) * 0.1
  53. # Calculate statistic limits for consistent color scaling
  54. stat_limits <- c(min(ranking), max(ranking))
  56. # Main enrichment score plot
  57. main_plot <- ggplot() +
  58. # Enrichment score curve
  59. geom_line(data = plot_data$curve,
  60. aes(x = rank, y = ES),
  61. color = color, size = 1.4) +
  63. # Gene hit ticks at bottom
  64. geom_segment(data = plot_data$ticks,
  65. aes(x = rank,
  66. y = y_min - (y_max - y_min) * 0.04,
  67. xend = rank,
  68. yend = y_min + (y_max - y_min) * 0.05),
  69. color = "black", size = 0.3) +
  71. # Reference line at zero
  72. geom_hline(yintercept = 0, color = "black",
  73. size = 0.5, linetype = "dashed", alpha = 0.7) +
  75. # Styling
  76. labs(y = "Enrichment Score", x = NULL, title = title) +
  77. theme_classic() +
  78. theme(
  79. axis.text.x = element_blank(),
  80. axis.ticks.x = element_blank(),
  81. plot.margin = margin(5, 5, 0, 5),
  82. panel.grid = element_blank(),
  83. plot.title = element_text(size = 16, hjust = 0.5),
  84. axis.title.y = element_text(size = 14),
  85. axis.text.y = element_text(size = 12)
  86. ) +
  87. coord_cartesian(ylim = c(y_min, y_max)) +
  88. scale_y_continuous(breaks = pretty_breaks(n = 5))
  90. # Bottom panel - ranked statistics heatmap
  91. # Create bins for smoother visualization
  92. n_bins <- min(100, length(ranking))
  93. bin_indices <- round(seq(1, length(ranking), length.out = n_bins))
  94. stats_binned <- data.frame(
  95. rank = bin_indices,
  96. stat = ranking[bin_indices]
  97. )
  99. stats_heatmap <- ggplot(stats_binned, aes(x = rank, y = 1)) +
  100. geom_tile(aes(fill = stat), height = 1) +
  101. scale_fill_gradient2(
  102. low = "blue",
  103. mid = "white",
  104. high = "red",
  105. midpoint = 0,
  106. limits = stat_limits,
  107. name = "Ranked\nStatistic"
  108. ) +
  109. theme_classic() +
  110. theme(
  111. axis.title.y = element_blank(),
  112. axis.text.y = element_blank(),
  113. axis.ticks.y = element_blank(),
  114. axis.line.y = element_blank(),
  115. plot.margin = margin(0, 5, 5, 5),
  116. legend.position = "right",
  117. legend.key.size = unit(0.4, "cm"),
  118. axis.title.x = element_text(size = 14),
  119. axis.text.x = element_text(size = 12)
  120. ) +
  121. labs(x = "Rank in Ordered Regions") +
  122. scale_x_continuous(breaks = pretty_breaks(n = 6)) +
  123. guides(fill = guide_colorbar(barwidth = 0.8, barheight = 3))
  125. # Combine panels with appropriate height ratio
  126. final_plot <- main_plot / stats_heatmap +
  127. plot_layout(heights = c(7, 1))
  129. return(final_plot)
  130. }
  134. #' Plot Top Enriched Region Sets
  135. #'
  136. #' Filters and plots the top significantly enriched region sets from enrichment results.
  137. #'
  138. #' @param results A data frame containing enrichment results (analysis$results). Must contain columns:
  139. #' \code{padj}, \code{antibody}, \code{NES}, and \code{description}.
  140. #' @param n Integer. The number of top region sets to display. Default is 20.
  141. #' @param filter_by_TF Logical. If \code{TRUE}, selects only the single top region set
  142. #' (by absolute NES) for each unique TF/antibody and plots TF/antibody names instead of region set names.
  143. #' Default is \code{FALSE}.
  144. #' @param FDR_cutoff Numeric. The adjusted p-value cutoff for significance.
  145. #' Default is 0.05.
  146. #' @param only_pos Logical. If \code{TRUE}, filters for only positive Normalized
  147. #' Enrichment Scores (NES > 0). Default is \code{FALSE}.
  148. #' @return A \code{ggplot} object representing the bar plot of normalized enrichment scores.
  149. #' @export
  150. #' @importFrom dplyr %>% filter group_by slice_max ungroup arrange desc slice_head mutate
  151. #' @importFrom ggplot2 ggplot aes geom_bar coord_flip scale_fill_manual labs theme_minimal theme element_text
  152. #' @importFrom stringr str_wrap
  153. #' @importFrom rlang .data
  154. #' @examples
  155. #' # Mock data
  156. #' df <- data.frame(
  157. #' regionSet = c("Region_Set_1", "Region_Set_2", "Region_Set_3"),
  158. #' antibody = c("TF_A", "TF_B", "TF_A"),
  159. #' NES = c(2.5, -1.8, 1.2),
  160. #' padj = c(0.001, 0.04, 0.001),
  161. #' description = c("Promoter Region A", "Enhancer Region B", "Promoter Region C")
  162. #' )
  163. #'
  164. #' # Plot top 10 sets
  165. #' plotTopRegionSets(df, n = 10)
  166. #'
  167. plotTopRegionSets <- function(results, n = 20, filter_by_TF = FALSE, FDR_cutoff = 0.05, only_pos = FALSE) {
  169. # Filter for significant region sets
  170. results <- results %>%
  171. filter(.data$padj < FDR_cutoff)
  173. if (dim(results)[1] == 0) {
  174. stop(sprintf("No significantly enriched region sets found at FDR_cutoff = '%s'", FDR_cutoff))
  175. }
  177. # Filter for the top set for each TF/antibody (Optional)
  178. if (filter_by_TF) {
  179. results <- results %>%
  180. group_by(.data$antibody) %>%
  181. slice_max(order_by = abs(.data$NES), n = 1, with_ties = FALSE) %>%
  182. ungroup()
  183. }
  185. # Filter for only positive NES values (Optional)
  186. if (only_pos) {
  187. results <- results %>%
  188. filter(.data$NES > 0)
  189. }
  191. # Filter top n region sets and arrange for plotting
  192. results <- results %>%
  193. arrange(desc(abs(.data$NES))) %>%
  194. slice_head(n = n) %>%
  195. arrange(.data$NES)
  197. # If filtering by TF, use TF names for plot labels
  198. if (filter_by_TF) {
  199. results <- results %>%
  200. mutate(plot_label = .data$antibody)
  201. } else {
  202. results <- results %>%
  203. mutate(plot_label = str_wrap(.data$regionSet, width = 40))
  204. }
  206. # Set factor levels to match the current order and add sign
  207. results <- results %>%
  208. mutate(
  209. plot_label = factor(plot_label, levels = unique(plot_label)),
  210. NES_sign = ifelse(.data$NES > 0, "pos. NES", "neg. NES")
  211. )
  213. # Plotting
  214. plot <- ggplot(results, aes(x = .data$plot_label, y = .data$NES, fill = .data$NES_sign)) +
  215. geom_bar(stat = "identity", width = 0.7) +
  216. coord_flip() +
  217. scale_fill_manual(
  218. values = c("pos. NES" = "#E57373", "neg. NES" = "#64B5F6"),
  219. drop = FALSE
  220. ) +
  221. labs(
  222. x = "",
  223. y = "Normalized Enrichment Score (NES)",
  224. fill = ""
  225. ) +
  226. theme_minimal() +
  227. theme(
  228. axis.text.y = element_text(size = 12, face = "bold"),
  229. axis.text.x = element_text(size = 11),
  230. axis.title = element_text(size = 12),
  231. legend.text = element_text(size = 11),
  232. legend.position = "top"
  233. )
  235. return(plot)
  236. }

plotting.R at commit a874a19, under MIT · at the source

Overview

Authors: Tobias Wolff1, Friederike Grandke1,2, Misbah Sayeeda1, Pascal Hirsch1, Matthias Flotho1,2, Andreas Keller1,2,3
ORCID iDs: Tobias Wolff, Friederike Grandke, Misbah Sayeeda, Pascal Hirsch, Matthias Flotho, Andreas Keller
  1. Chair for Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
  2. Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Saarland University Campus, 66123 Saarbrücken, Germany
  3. PharmaScienceHub (PSH), Saarland University Campus, 66123 Saarbrücken, Germany
Institutions: Saarland University (Germany); Helmholtz Institute for Pharmaceutical Research Saarland (Germany)
Journal: Nucleic acids research, volume 54, issue W1, pages W117-W124
Dates: received 25 March 2026; accepted 23 April 2026; published online 11 May 2026; in print July 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/nar/gkag454 · PMID 42109173 · PMCID PMC13355058 · OpenAlex W7160847415
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), methods / tools (subfield)
Methods: Statistics
MeSH: Epigenomics*, Regulatory Sequences, Nucleic Acid*, Software*, Animals, Binding Sites, Chromatin, DNA Methylation, Humans, Internet, Mice, Transcription Factors (* major topic)
Topic: Genomics and Chromatin Dynamics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Horizon Europe programme (101057548-EPIVINF); DFG (469073465); Saarland University
Citations: not cited yet (Europe PMC); 34 references in the paper

Abstract

Interpreting genome-wide epigenomic experiments, such as DNA methylation profiling and chromatin accessibility assays, requires tools that can identify which regulatory programs underlie coordinated changes across genomic regions. Without this regulatory context, lists of differential regions remain largely descriptive and difficult to interpret mechanistically. Existing approaches either apply hard significance cutoffs that discard moderate but biologically meaningful signals, or rely on gene-centric annotations that neglect enhancers and intergenic space, introducing bias into the interpretation. RegRegSEA addresses both shortcomings by adapting the Gene Set Enrichment Analysis framework directly to genomic coordinates. The server accepts a standard differential analysis table, ranks all tested intervals by a signed statistic, and computes enrichment scores against curated regulatory databases including transcription factor binding site collections. Results are returned as an interactive, publication-ready report featuring dynamic visualizations of enrichment profiles and regulatory annotations, along with downloadable leading-edge regions for downstream analyses. We demonstrate the utility of this approach through re-analysis of Down syndrome brain methylation data and chromatin accessibility in ageing mouse liver. The server is freely available at https://web.ccb.uni-saarland.de/regregsea/ and open to all users with no login required.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

CCB-SB/RegRegSEA

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a874a198aaf5a75d6be6371b8bac03eeb4bee4d0, 20 April 2026
Languages: R (3)
Size: 20 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (DESCRIPTION), documentation, 1 notebook
Not found: CITATION.cff, tests, continuous integration
Tools: tidyverse (2 files), data.table (1 file), ggplot2 (1 file), patchwork (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/CCB-SB/RegRegSEA

Zenodo 19731482

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files), data.table (1 file), ggplot2 (1 file), patchwork (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
5 files
At the source: doi.org/10.5281/zenodo.19731482

Code availability

The backend analysis code of the web server is written in R and available at https://github.com/CCB-SB/RegRegSEA and https://doi.org/10.5281/zenodo.19731482.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

No new data were generated or analysed in this study. The RegRegSEA web server is freely available at https://web.ccb.uni-saarland.de/regregsea/.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 11 MeSH terms, 3 funders, 34 references.

Cite

This paper

Wolff, T., Grandke, F., Sayeeda, M., Hirsch, P., Flotho, M., & Keller, A. (2026). RegRegSEA: a web server for regulatory region set enrichment analysis of epigenomic data. Nucleic acids research, 54(W1), W117-W124. https://doi.org/10.1093/nar/gkag454

BibTeX

@article{wolff2026regregsea,
author = {Wolff, Tobias and Grandke, Friederike and Sayeeda, Misbah and Hirsch, Pascal and Flotho, Matthias and Keller, Andreas},
title = {{RegRegSEA: a web server for regulatory region set enrichment analysis of epigenomic data}},
journal = {Nucleic acids research},
year = {2026},
month = jul,
volume = {54},
number = {W1},
pages = {W117--W124},
publisher = {Oxford University Press},
issn = {0305-1048},
doi = {10.1093/nar/gkag454},
url = {https://doi.org/10.1093/nar/gkag454},
pmid = {42109173},
pmcid = {PMC13355058}
}

RIS

TY - JOUR
AU - Wolff, Tobias
AU - Grandke, Friederike
AU - Sayeeda, Misbah
AU - Hirsch, Pascal
AU - Flotho, Matthias
AU - Keller, Andreas
TI - RegRegSEA: a web server for regulatory region set enrichment analysis of epigenomic data
T2 - Nucleic acids research
J2 - Nucleic Acids Res
PY - 2026
DA - 2026/07/01
VL - 54
IS - W1
SP - W117
EP - W124
SN - 0305-1048
PB - Oxford University Press
DO - 10.1093/nar/gkag454
UR - https://doi.org/10.1093/nar/gkag454
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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