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AAV-mediated gene therapy demonstrates phenotypic rescue in a mouse model of Cockayne syndrome.

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  1. [1] § Methods › RNA-seq ↔ modules/nf-core/dupradar/templates/dupradar.r, lines 65–142 · score 0.80 · generalized linear model, quality control, expressed genes, library, nf core, RNA

Paper

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The authors' code

R · 187 lines · 6.4 KB · MIT · 1 match

  1. #!/usr/bin/env Rscript
  3. # Written by Phil Ewels and released under the MIT license.
  4. # Ported to nf-core/modules with template by Jonathan Manning
  6. #' Parse out options from a string without recourse to optparse
  7. #'
  8. #' @param x Long-form argument list like --opt1 val1 --opt2 val2
  9. #'
  10. #' @return named list of options and values similar to optparse
  12. parse_args <- function(x){
  13. args_list <- unlist(strsplit(x, ' ?--')[[1]])[-1]
  14. args_vals <- lapply(args_list, function(x) scan(text=x, what='character', quiet = TRUE))
  16. # Ensure the option vectors are length 2 (key/ value) to catch empty ones
  17. args_vals <- lapply(args_vals, function(z){ length(z) <- 2; z})
  19. parsed_args <- structure(lapply(args_vals, function(x) x[2]), names = lapply(args_vals, function(x) x[1]))
  20. parsed_args[! is.na(parsed_args)]
  21. }
  23. ################################################
  24. ################################################
  25. ## Pull in module inputs ##
  26. ################################################
  27. ################################################
  29. input_bam <- '$bam'
  30. output_prefix = ifelse('$task.ext.prefix' == 'null', '$meta.id', '$task.ext.prefix')
  31. annotation_gtf <- '$gtf'
  32. threads <- $task.cpus
  33. args_opt <- parse_args('$task.ext.args')
  34. feature_type <- ifelse('feature_type' %in% names(args_opt), args_opt[['feature_type']], 'exon')
  36. stranded <- 0
  37. if ('${meta.strandedness}' == 'forward') {
  38. stranded <- 1
  39. } else if ('${meta.strandedness}' == 'reverse') {
  40. stranded <- 2
  41. }
  43. paired_end <- TRUE
  44. if ('${meta.single_end}' == 'true'){
  45. paired_end <- FALSE
  46. }
  48. # Debug messages (stderr)
  49. message("Input bam : ", input_bam)
  50. message("Input gtf : ", annotation_gtf)
  51. message("Strandness : ", c("unstranded", "forward", "reverse")[stranded+1])
  52. message("paired/single : ", ifelse(paired_end, 'paired', 'single'))
  53. message("feature type : ", ifelse(paired_end, 'paired', 'single'))
  54. message("Nb threads : ", threads)
  55. message("Output basename: ", output_prefix)
  57. # Load / install packages
  58. library("dupRadar")
  59. library("parallel")
  61. # Duplicate stats
  62. dm <- analyzeDuprates(input_bam, annotation_gtf, stranded, paired_end, threads, GTF.featureType = feature_type, verbose = TRUE)
  63. write.table(dm, file=paste(output_prefix, "_dupMatrix.txt", sep=""), quote=F, row.name=F, sep="\t")
  65. # 2D density scatter plot
  66. pdf(paste0(output_prefix, "_duprateExpDens.pdf"))
  67. duprateExpDensPlot(DupMat=dm)
  68. title("Density scatter plot")
  69. mtext(output_prefix, side=3)
  70. dev.off()
  71. fit <- duprateExpFit(DupMat=dm)
  72. cat(
  73. paste("- dupRadar Int (duprate at low read counts):", fit\$intercept),
  74. paste("- dupRadar Sl (progression of the duplication rate):", fit\$slope),
  75. fill=TRUE, labels=output_prefix,
  76. file=paste0(output_prefix, "_intercept_slope.txt"), append=FALSE
  77. )
  79. # Create a multiqc file dupInt
  80. sample_name <- gsub("Aligned.sortedByCoord.out.markDups", "", output_prefix)
  81. line="#id: DupInt
  82. #plot_type: 'generalstats'
  83. #pconfig:
  84. # dupRadar_intercept:
  85. # title: 'dupInt'
  86. # namespace: 'DupRadar'
  87. # description: 'Intercept value from DupRadar'
  88. # max: 100
  89. # min: 0
  90. # scale: 'RdYlGn-rev'
  91. Sample dupRadar_intercept"
  93. write(line,file=paste0(output_prefix, "_dup_intercept_mqc.txt"),append=TRUE)
  94. write(paste(sample_name, fit\$intercept),file=paste0(output_prefix, "_dup_intercept_mqc.txt"),append=TRUE)
  96. # Get numbers from dupRadar GLM
  97. curve_x <- sort(log10(dm\$RPK))
  98. curve_y = 100*predict(fit\$glm, data.frame(x=curve_x), type="response")
  99. # Remove all of the infinite values
  100. infs = which(curve_x %in% c(-Inf,Inf))
  101. curve_x = curve_x[-infs]
  102. curve_y = curve_y[-infs]
  103. # Reduce number of data points
  104. curve_x <- curve_x[seq(1, length(curve_x), 10)]
  105. curve_y <- curve_y[seq(1, length(curve_y), 10)]
  106. # Convert x values back to real counts
  107. curve_x = 10^curve_x
  108. # Write to file
  109. line="#id: dupradar
  110. #plot_type: 'linegraph'
  111. #section_name: 'DupRadar'
  112. #section_href: 'bioconductor.org/packages/release/bioc/html/dupRadar.html'
  113. #description: \"provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication.
  114. # This plot shows the general linear models - a summary of the gene duplication distributions. \"
  115. #pconfig:
  116. # title: 'DupRadar General Linear Model'
  117. # xlog: True
  118. # xlab: 'expression (reads/kbp)'
  119. # ylab: '% duplicate reads'
  120. # ymax: 100
  121. # ymin: 0
  122. # tt_label: '<b>{point.x:.1f} reads/kbp</b>: {point.y:,.2f}% duplicates'
  123. # x_lines:
  124. # - color: 'green'
  125. # dash: 'LongDash'
  126. # label:
  127. # text: '0.5 RPKM'
  128. # value: 0.5
  129. # width: 1
  130. # - color: 'red'
  131. # dash: 'LongDash'
  132. # label:
  133. # text: '1 read/bp'
  134. # value: 1000
  135. # width: 1"
  137. write(line,file=paste0(output_prefix, "_duprateExpDensCurve_mqc.txt"),append=TRUE)
  138. write.table(
  139. cbind(curve_x, curve_y),
  140. file=paste0(output_prefix, "_duprateExpDensCurve_mqc.txt"),
  141. quote=FALSE, row.names=FALSE, col.names=FALSE, append=TRUE,
  142. )
  144. # Distribution of expression box plot
  145. pdf(paste0(output_prefix, "_duprateExpBoxplot.pdf"))
  146. duprateExpBoxplot(DupMat=dm)
  147. title("Percent Duplication by Expression")
  148. mtext(output_prefix, side=3)
  149. dev.off()
  151. # Distribution of RPK values per gene
  152. pdf(paste0(output_prefix, "_expressionHist.pdf"))
  153. expressionHist(DupMat=dm)
  154. title("Distribution of RPK values per gene")
  155. mtext(output_prefix, side=3)
  156. dev.off()
  158. ################################################
  159. ################################################
  160. ## R SESSION INFO ##
  161. ################################################
  162. ################################################
  164. sink(paste(output_prefix, "R_sessionInfo.log", sep = '.'))
  165. print(sessionInfo())
  166. sink()
  168. ################################################
  169. ################################################
  170. ## VERSIONS FILE ##
  171. ################################################
  172. ################################################
  174. r.version <- strsplit(version[['version.string']], ' ')[[1]][3]
  175. dupradar.version <- as.character(packageVersion('dupRadar'))
  177. writeLines(
  178. c(
  179. '"${task.process}":',
  180. paste(' bioconductor-dupradar:', dupradar.version)
  181. ),
  182. 'versions.yml')
  184. ################################################
  185. ################################################
  186. ################################################
  187. ################################################

dupradar.r, under MIT · at the source

Overview

Authors: Ana Rita Batista1,2, Aine C. Scholand1,2, William S. Callahan1,2, McKenna K. Watson1,2, Cassandra M. Sion1,2, Tyler Mola1,2, Kennedy O’Hara1,2, Simon A. Wentworth1,2,3, William S. Sena-Esteves1,2, Oliver D. King2, Robert M. King4, Miguel Sena-Esteves1,2
ORCID iDs: Aine C. Scholand, Robert M. King
  1. Department of Genetic and Cellular Medicine
  2. Department of Neurology
  3. MD/PhD program, Morningside Graduate School of Biomedical Sciences, T.H. Chan School of Medicine, and
  4. Department of Radiology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
Institutions: University of Massachusetts Chan Medical School (United States)
Journal: The Journal of clinical investigation, volume 136, issue 18, article e196689
Dates: received 11 June 2025; accepted 20 July 2026; published online 30 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1172/jci196689 · PMID 42531030 · PMCID PMC13574149 · OpenAlex W7171856869
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism)
Methods: Statistics, fMRI & imaging
Keywords: Genetics, Neuroscience, DNA repair, Gene therapy, Genetic diseases
MeSH: Cockayne Syndrome*, Dependovirus*, Genetic Therapy*, Genetic Vectors*, Animals, Disease Models, Animal, Gene Therapy Agents, Humans, Male, Mice, Mice, Knockout, Xeroderma Pigmentosum Group A Protein (* major topic)
Topic: DNA Repair Mechanisms (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Riaan Research Initiative (RRI-1212)
Citations: not cited yet (Europe PMC); 84 references in the paper

Abstract

Cockayne syndrome (CS) is an autosomal recessive, progressive developmental and neurodegenerative disease. Approximately 30% of cases are caused by mutations in the ERCC8/CSA gene. Patients with CS present with cutaneous photosensitivity, growth failure, shorter life span, and a progressive degeneration of the central nervous system. Loss-of-function mutations in CSA result in deficiencies in transcription-coupled nucleotide excision repair. Currently, no therapies are available for these patients. Adeno-associated virus–mediated (AAV-mediated) gene therapy offers an opportunity to address this unmet need. We designed an AAV vector encoding human CSA under a ubiquitous promoter. We tested the therapeutic efficacy of this AAV9-CSA vector by neonatal intracerebroventricular injection in the Csa–/– Xpa–/– mouse model. Treatment with AAV9-CSA resulted in a significant increase in life span, and broad distribution of human CSA in the brain and heart, without evidence of vector-related toxicity. Despite clear therapeutic benefit, we also observed neuroradiological abnormalities, and neuropathologic alterations, including hypomyelination, astrocytosis, and microgliosis, as well as likely life-limiting transcriptomic alterations in liver at endpoint. Nonetheless, the success of these experiments paves the way for clinical translation of an AAV gene therapy for patients with CS into humans.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

Zenodo 20072251

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: DESeq2 (1 file), ggplot2 (1 file), Nextflow (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
15 files
At the source: zenodo.org/records/20072251

The paper's code and data availability statement is in the Data section.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 13 scripts, each with its path and the digest of its content;
  • 1 match 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

The RNA-seq data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (GEO) (84) and are accessible through GEO Series accession number GSE336535. All other raw data and MATLAB scripts are available upon request. Values for all data points in graphs are reported in the Supporting Data Values file.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 12 MeSH terms, 1 funder, 82 references.

Cite

This paper

Batista, A. R., Scholand, A. C., Callahan, W. S., Watson, M. K., Sion, C. M., Mola, T., O’Hara, K., Wentworth, S. A., Sena-Esteves, W. S., King, O. D., King, R. M., & Sena-Esteves, M. (2026). AAV-mediated gene therapy demonstrates phenotypic rescue in a mouse model of Cockayne syndrome. The Journal of clinical investigation, 136(18), e196689. https://doi.org/10.1172/jci196689

BibTeX

@article{batista2026aav,
author = {Batista, Ana Rita and Scholand, Aine C. and Callahan, William S. and Watson, McKenna K. and Sion, Cassandra M. and Mola, Tyler and O’Hara, Kennedy and Wentworth, Simon A. and Sena-Esteves, William S. and King, Oliver D. and King, Robert M. and Sena-Esteves, Miguel},
title = {{AAV-mediated gene therapy demonstrates phenotypic rescue in a mouse model of Cockayne syndrome}},
journal = {The Journal of clinical investigation},
year = {2026},
month = jul,
volume = {136},
number = {18},
pages = {e196689},
publisher = {American Society for Clinical Investigation},
issn = {0021-9738},
doi = {10.1172/jci196689},
url = {https://doi.org/10.1172/jci196689},
pmid = {42531030},
pmcid = {PMC13574149}
}

RIS

TY - JOUR
AU - Batista, Ana Rita
AU - Scholand, Aine C.
AU - Callahan, William S.
AU - Watson, McKenna K.
AU - Sion, Cassandra M.
AU - Mola, Tyler
AU - O’Hara, Kennedy
AU - Wentworth, Simon A.
AU - Sena-Esteves, William S.
AU - King, Oliver D.
AU - King, Robert M.
AU - Sena-Esteves, Miguel
TI - AAV-mediated gene therapy demonstrates phenotypic rescue in a mouse model of Cockayne syndrome
T2 - The Journal of clinical investigation
J2 - J Clin Invest
PY - 2026
DA - 2026/07/30
VL - 136
IS - 18
SP - e196689
SN - 0021-9738
PB - American Society for Clinical Investigation
DO - 10.1172/jci196689
UR - https://doi.org/10.1172/jci196689
LA - en
ER -

CSL-JSON

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