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Multi-platform profiling reveals host- and cell -type-specific pseudorabies virus gene expression.

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Paper

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The authors' code

R · 162 lines · 5.9 KB · MIT

  3. cover_dt_weighted <- function(dt,
  4. weight_col = NULL,
  5. by = c("seqnames", "strand", "sample"),
  6. return = c("rle", "pos"),
  7. nworkers = 1L,
  8. # zero-fill options
  9. include_zero = FALSE,
  10. contigs_dt = NULL,
  11. contig_seq_col = "seqnames",
  12. contig_start_col = "start",
  13. contig_end_col = "end") {
  14. stopifnot(all(c("seqnames","strand","start","end","sample") %in% names(dt)))
  15. return <- match.arg(return)
  16. setDT(dt)
  18. # Coerce to ints & numeric weight
  19. dt[, `:=`(start = as.integer(start), end = as.integer(end))]
  20. if (!is.null(weight_col)) {
  21. stopifnot(weight_col %in% names(dt))
  22. set(dt, j = weight_col, value = as.numeric(dt[[weight_col]]))
  23. }
  24. if (is.null(weight_col)) {
  25. dt[, `__w__` := 1.0]
  26. on.exit(dt[, `__w__` := NULL], add = TRUE)
  27. weight_col <- "__w__"
  28. }
  30. # Drop 0 / NA / non-finite weights early
  31. dt <- dt[is.finite(get(weight_col)) & get(weight_col) != 0]
  33. # Safe +1 with overflow guard
  34. add1 <- function(x) {
  35. y <- x + 1L
  36. y[is.na(x)] <- NA_integer_
  37. y[ y < x ] <- .Machine$integer.max
  38. y
  39. }
  41. make_runs <- function(x, bycols, wcol) {
  42. # Weighted events per group: +w at start, -w at end+1
  43. ev <- x[, {
  44. w <- get(wcol)
  45. .(pos = c(start, add1(end)),
  46. delta = c(w, -w))
  47. }, by = bycols]
  49. # Combine same-position events
  50. ev <- ev[, .(delta = sum(delta)), by = c(bycols, "pos")]
  52. # Order by group columns and position
  53. do.call(setorder, c(list(ev), as.list(c(bycols, "pos"))))
  55. # Weighted coverage via cumsum of deltas per group
  56. ev[, cov := cumsum(delta), by = bycols]
  57. ev[, next_pos := shift(pos, type = "lead"), by = bycols]
  59. runs <- ev[!is.na(next_pos) & cov > 0,
  60. .(start = pos, end = next_pos - 1L, count = cov),
  61. by = bycols]
  62. runs[]
  63. }
  65. # Compute positive-coverage runs
  66. if (nrow(dt) == 0L) {
  67. runs <- data.table(matrix(ncol = length(by) + 3L, nrow = 0L))
  68. setnames(runs, c(by, "start", "end", "count"))
  69. } else if (nworkers > 1L) {
  70. requireNamespace("future.apply", quietly = TRUE)
  71. requireNamespace("future", quietly = TRUE)
  72. split_dt <- split(dt, by = by, drop = TRUE, keep.by = TRUE)
  74. oplan <- NULL
  75. if (!inherits(future::plan(), "multiprocess")) {
  76. oplan <- future::plan(future::multisession, workers = nworkers)
  77. on.exit({ if (!is.null(oplan)) future::plan(oplan) }, add = TRUE)
  78. }
  80. parts <- future.apply::future_lapply(
  81. split_dt, make_runs, bycols = by, wcol = weight_col, future.seed = TRUE
  82. )
  83. runs <- rbindlist(parts, use.names = TRUE)
  84. } else {
  85. runs <- make_runs(dt, by, weight_col)
  86. }
  88. # === Zero-fill RLE based on contig spans ===
  89. if (isTRUE(include_zero)) {
  90. stopifnot(!is.null(contigs_dt),
  91. all(c(contig_seq_col, contig_start_col, contig_end_col) %in% names(contigs_dt)))
  93. g0 <- unique(as.data.table(contigs_dt)[,
  94. .(seqnames = get(contig_seq_col),
  95. g_start = as.integer(get(contig_start_col)),
  96. g_end = as.integer(get(contig_end_col)))
  97. ])
  99. # Expand contigs across any missing by-columns using dt's uniques
  100. comb_list <- list(seqnames = unique(g0$seqnames))
  101. for (col in setdiff(by, "seqnames")) {
  102. if (col %in% names(contigs_dt)) comb_list[[col]] <- unique(contigs_dt[[col]])
  103. else comb_list[[col]] <- unique(dt[[col]])
  104. }
  105. comb <- do.call(CJ, c(comb_list, list(unique = TRUE)))
  106. contigs_exp <- g0[comb, on = "seqnames", nomatch = 0L]
  108. # Groups with no positive runs -> full-length zero run
  109. runs_keys <- unique(runs[, ..by])
  110. missing_groups <- contigs_exp[!runs_keys, on = by]
  111. zero_missing <- if (nrow(missing_groups)) {
  112. missing_groups[, .(start = g_start, end = g_end, count = 0.0), by = by]
  113. } else data.table(matrix(ncol = length(by) + 3L, nrow = 0L))
  114. if (nrow(zero_missing)) setnames(zero_missing, c(by, "start", "end", "count"))
  116. # For groups with positives, add leading/trailing and between-run zeros
  117. if (nrow(runs)) {
  118. runs2 <- contigs_exp[runs, on = by]
  119. # g_start/g_end now available per group
  120. zeros_between <- runs2[, {
  121. sd <- copy(.SD)
  122. setorder(sd, start, end)
  123. gs <- g_start[1L]; ge <- g_end[1L]
  124. out <- vector("list", 3L)
  125. k <- 0L
  126. if (nrow(sd) && sd$start[1L] > gs) { k <- k + 1L; out[[k]] <- data.table(start = gs, end = sd$start[1L] - 1L, count = 0.0) }
  127. if (nrow(sd) > 1L) {
  128. gaps_s <- sd$end[-.N] + 1L
  129. gaps_e <- sd$start[-1L] - 1L
  130. sel <- gaps_s <= gaps_e
  131. if (any(sel)) { k <- k + 1L; out[[k]] <- data.table(start = gaps_s[sel], end = gaps_e[sel], count = 0.0) }
  132. }
  133. if (nrow(sd) && sd$end[.N] < ge) { k <- k + 1L; out[[k]] <- data.table(start = sd$end[.N] + 1L, end = ge, count = 0.0) }
  134. if (k) rbindlist(out[1:k]) else data.table(start = integer(), end = integer(), count = numeric())
  135. }, by = by]
  137. runs <- rbindlist(list(runs, zeros_between, zero_missing), use.names = TRUE, fill = TRUE)
  138. } else {
  139. runs <- zero_missing
  140. }
  142. if (nrow(runs)) do.call(setorder, c(list(runs), as.list(c(by, "start", "end"))))
  143. }
  146. #
  147. runs <- runs[, .(seqnames, strand, sample, start, end, count)]
  149. #
  150. if (return == "rle") {
  151. setcolorder(runs, c(by, setdiff(names(runs), by)))
  152. return(runs[])
  153. }
  155. # Per-position expansion (row-wise, length-safe). This will also include zeros if include_zero=TRUE.
  156. runs[, {
  157. pos_list <- Map(seq.int, start, end)
  158. .(pos = unlist(pos_list, use.names = FALSE),
  159. count = rep.int(count, lengths(pos_list)))
  160. }, by = by]
  162. }

ExDT2cov.R at commit aeb8421, under MIT · at the source

Overview

Authors: Balázs Kakuk1, Zsolt Csabai1, Zoltán Deim2, Gábor Torma1, Ádám Fülöp1, Gergely Ármin Nagy1, Virág Éva Dani1, Dóra Tombácz1, Zsolt Boldogkői1
  1. Department of Medical Biology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
  2. Department of Physiology, Anatomy and Neuroscience, Faculty of Science and Informatics, University of Szeged, 6726 Szeged, Hungary
Institutions: University of Szeged (Hungary)
Journal: Scientific reports, volume 16, issue 1, article 15297
Dates: received 17 December 2025; accepted 23 March 2026; published online 1 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-45990-4 · PMID 41922589 · PMCID PMC13181115 · OpenAlex W7147096085
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), rat (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics
Keywords: Computational biology and bioinformatics, Genetics, Molecular biology
MeSH: Gene Expression Profiling*, Gene Expression Regulation, Viral*, Herpesvirus 1, Suid*, Host-Pathogen Interactions*, Pseudorabies*, Animals, Cell Line, Rats, Swine, Viral Proteins (* major topic)
Topic: Herpesvirus Infections and Treatments (Epidemiology, Medicine), according to OpenAlex
Funding: University of Szeged
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Pseudorabies virus (PRV) is an alphaherpesvirus that follows a conserved immediate-early → early → late transcriptional cascade, yet how this program adapts to diverse cell types is unclear. We profiled PRV transcription in four permissive cell lines—two epithelial (porcine kidney PK-15 and rat kidney NRK), one glial (rat glioma C6), and one neuron-like (rat PC-12)—at six time points (1–12 h post-infection). Host-dependent differences peaked early in infection, particularly for the regulators ie180, ep0, and us1. ie180 showed strong species bias, with high expression in PK-15 but minimal in rodent lines, whereas ep0 and us1 varied quantitatively by cell type, with C6 and PC-12 showing marked early us1 activation. Combining long-read direct cDNA and direct RNA sequencing with 5′-capped CAGE-seq resolved viral transcription boundaries and identified 94 previously unannotated transcripts, including 5′ UTR isoforms, polygenic RNAs, and noncoding transcripts. Differential transcript usage analysis revealed extensive isoform remodeling across conditions, with late infection showing shifts from long or polygenic isoforms toward shorter forms. Together, these results provide the first multi-host, isoform-resolved temporal atlas of PRV transcription and show that the canonical cascade is conserved yet quantitatively tuned by host species and cell-type background.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-45990-4.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

Balays/Rlyeh

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: aeb8421bc29070fa130d6266c927d7e1c5df64c6, 7 February 2026
Languages: R (42), Shell (1)
Size: 49 files, 43 scripts
Software Heritage: not archived
Found in: the text, “Downstream bioinformatics analysis”
Holds: license file, 1 notebook
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (18 files), tidyverse (12 files), cowplot (2 files), ggplot2 (1 file), igraph (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
44 files
At the source: github.com/Balays/Rlyeh

Balays/VALAR-PRV-MDBIO-4cell

License: none: the authors keep all their rights
State: the link is dead, verified on 28 September 2026
Evidence: found in the paper
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link is dead
  • 28 September 2026: the link is dead
At the source: github.com/Balays/VALAR-PRV-MDBIO-4cell

Code availability

All R scripts that were used for the downstream analysis are available at the GitHub repository: https://github.com/Balays/VALAR-PRV-MDBIO-4cell

Reproduced under the paper's license (CC BY), 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;
  • 43 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

No dataset and no data link were found in the paper.

Data availability

The fastq files from all sequencing were uploaded to European Nucleotide Archive (ENA), under the project id PRJEB60055.

Reproduced under the paper's license (CC BY), 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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 3 keywords, 10 MeSH terms, 1 funder, 51 references.

Cite

This paper

Kakuk, B., Csabai, Z., Deim, Z., Torma, G., Fülöp, Á., Nagy, G. Á., Dani, V. É., Tombácz, D., & Boldogkői, Z. (2026). Multi-platform profiling reveals host- and cell -type-specific pseudorabies virus gene expression. Scientific reports, 16(1), 15297. https://doi.org/10.1038/s41598-026-45990-4

BibTeX

@article{kakuk2026multi,
author = {Kakuk, Balázs and Csabai, Zsolt and Deim, Zoltán and Torma, Gábor and Fülöp, Ádám and Nagy, Gergely Ármin and Dani, Virág Éva and Tombácz, Dóra and Boldogkői, Zsolt},
title = {{Multi-platform profiling reveals host- and cell -type-specific pseudorabies virus gene expression}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {15297},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-45990-4},
url = {https://doi.org/10.1038/s41598-026-45990-4},
pmid = {41922589},
pmcid = {PMC13181115}
}

RIS

TY - JOUR
AU - Kakuk, Balázs
AU - Csabai, Zsolt
AU - Deim, Zoltán
AU - Torma, Gábor
AU - Fülöp, Ádám
AU - Nagy, Gergely Ármin
AU - Dani, Virág Éva
AU - Tombácz, Dóra
AU - Boldogkői, Zsolt
TI - Multi-platform profiling reveals host- and cell -type-specific pseudorabies virus gene expression
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/01
VL - 16
IS - 1
SP - 15297
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-45990-4
UR - https://doi.org/10.1038/s41598-026-45990-4
LA - en
ER -

CSL-JSON

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"container-title": "Scientific reports",
"author": [
{
"family": "Kakuk",
"given": "Balázs"
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"family": "Csabai",
"given": "Zsolt"
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"given": "Zoltán"
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{
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{
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"given": "Virág Éva"
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"PMCID": "PMC13181115",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
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"language": "en",
"issued": {
"date-parts": [
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}

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