OSCR

Developmental stage dominates cell-type identity and reveals a chromatin regulatory function for Rad50 in Drosophila.

Code ↔ Paper

8 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 8 matches
  1. [1] § Materials and methods › LC–MS/MS ↔ 260511_lcmsms_analysis.Rmd, lines 69–117 · score 0.90 · endoplasmic reticulum, LC MS, clathrin, endomembrane, endosome, golgi
  2. [2] § Results › Developmental stage is the main feature of cell identity ↔ 250411_RNAseq_gliaHemocytesNeurons.Rmd, lines 207–276 · score 0.81 · Euclidean distances, hierarchical clustering, variance explained, HC, PC1, PC2
  3. [3] § Materials and methods › RT-qPCR ↔ 251103_RTqPCR_analysis_DNArepair.Rmd, lines 12–50 · score 0.74 · standard deviation, technical replicate, biological replicate, duplicate, Ct, PCR
  4. [4] § Results › Developmental stage is the main feature of cell identity ↔ 250411_RNAseq_gliaHemocytesNeurons.Rmd, lines 207–276 · score 0.66 · Euclidean distances, Hierarchical clustering, PC1, PC2, hemocytes, PCA
  5. [5] § Materials and methods › RNA-seq analysis ↔ 250411_RNAseq_gliaHemocytesNeurons.Rmd, lines 67–92 · score 0.61 · stabilization transformed, cellType, glia, neurons, hemocytes, padj
  6. [6] § Results › Rad50 interacts with a broad spectrum of chromatin associated proteins ↔ 260511_lcmsms_analysis.Rmd, lines 119–158 · score 0.61 · PolA1, PolA2, Prim1, Prim2, Mre11, alpha
  7. [7] § Results › Stage-specific transcriptional features in embryonic and larval cells ↔ 250411_RNAseq_gliaHemocytesNeurons.Rmd, lines 94–134 · score 0.55 · Log2FC, gene selection, DESeq2, glia, neurons, hemocytes
  8. [8] § Results › Rad50 interacts with a broad spectrum of chromatin associated proteins ↔ 260511_lcmsms_analysis.Rmd, lines 160–218 · score 0.52 · Reactome pathway, Co IP LC, LC MS, log2, enrichment, Rad50

Paper

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The authors' code

R Markdown · 309 lines · 12 KB · no license · 4 matches

  1. ---
  2. title: "RNA-seq glia/hemocytes/neurons analysis"
  3. author: "Thomas Boutet"
  4. date: "2/23/2025"
  5. output: html_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  10. ```
  12. # Library
  13. ```{r library}
  14. library(DESeq2)
  15. library(dplyr)
  16. library(ggplot2)
  17. library(ggdendro)
  18. library(gridExtra)
  19. library(VennDiagram)
  20. library(pheatmap)
  21. library(RColorBrewer)
  22. library(ggsci)
  23. library(ggplotify)
  24. library(cowplot)
  25. library(ggpubr)
  26. library(tibble)
  27. library(tidyr)
  28. library(stringr)
  29. ```
  31. # Setup working dir
  32. ```{r setup dir}
  33. pathToProj <- "~/01_transcriptome_stage"
  34. figureDir <- file.path(pathToProj, "figure")
  35. geneSelection <- file.path(pathToProj, "geneSelection")
  36. ```
  38. # Setup count data
  39. ```{r}
  40. # get countmatrix
  41. countMatrix <- read.table("/01_transcriptome_stage/hisat2_L3E16_stranded.csv", sep = ";", header = TRUE)
  43. # create the count file and the metadata for DESeq2. Include batch
  44. metaData <- matrix(c("hemo_e16_rep1", "hemo_e16_rep2", "hemo_e16_rep3", "hemo_l3_rep1", "hemo_l3_rep2", "hemo_l3_rep3",
  45. "glia_e16_rep1", "glia_e16_rep2", "glia_e16_rep3", "glia_l3_rep1", "glia_l3_rep2", "glia_l3_rep3",
  46. "neuron_e16_rep1", "neuron_e16_rep2", "neuron_e16_rep3", "neuron_l3_rep1", "neuron_l3_rep2", "neuron_l3_rep3",
  47. # Cell type
  48. "hemo", "hemo", "hemo", "hemo", "hemo", "hemo",
  49. "glia", "glia", "glia", "glia", "glia", "glia",
  50. "neurons", "neurons", "neurons", "neurons", "neurons", "neurons",
  51. # Stage
  52. "e16", "e16", "e16", "l3", "l3", "l3",
  53. "e16", "e16", "e16", "l3", "l3", "l3",
  54. "e16", "e16", "e16", "l3", "l3", "l3"),
  55. ncol = 3, byrow = FALSE)
  57. metaData <- as.data.frame(metaData)
  58. colnames(metaData) <- c("sample", "cellType", "stage")
  60. countData <- dplyr::select(countMatrix, c(1, (3:20)))
  61. countData <- as.data.frame(countData)
  63. rownames(countData) <- countData$Geneid
  64. countData <- countData[, -1]
  65. ```
  67. # perform DESeq2 for stage analysis
  68. ```{r}
  69. # modelize the statistical analysis. We use cell-type and stage as main effect and then we look at the interaction term celtype vs stage
  70. deseq2Obj <- DESeqDataSetFromMatrix(countData = countData,
  71. colData = metaData,
  72. design = ~ cellType + stage + cellType:stage)
  74. # perform deseq2
  75. dds <- DESeq(deseq2Obj)
  76. # Store the results
  77. res <- results(dds)
  79. # Order the results by p-value
  80. res <- res[order(res$padj), ]
  82. # get coef names
  83. resultsNames(dds)
  84. summary(res)
  86. # if needed, here are normalized counts extracted
  87. normlzd_dds <- counts(dds, normalized=T)
  89. # varaiance stabilization transformation: prepare data for clustering
  90. vsd <- vst(dds, blind=T)
  91. rsd <- rlog(dds, blind=T)
  92. ```
  94. # perform analysis and plot graphs
  95. ```{r}
  96. # --- 1. Log2FC Shrinkage for All Coefficients ---
  97. # Get all coefficient names from deseq2 analysis
  98. coefNames <- resultsNames(dds)[-1]
  99. # Apply shrinkage using apeglm to each coefficient
  100. shrunkenResults <- lapply(coefNames, function(coef) {
  101. lfcShrink(dds, coef = coef, type = "apeglm")
  102. })
  103. names(shrunkenResults) <- coefNames
  105. # --- 2. Candidate Gene Selection ---
  106. # (a) For Glia Stage Contrast (L3 vs E16): use shrunken result
  107. res_stage_shrunk <- shrunkenResults[["stage_l3_vs_e16"]]
  108. cand_glia_df <- subset(as.data.frame(res_stage_shrunk), padj < 0.01 & abs(log2FoldChange) > 1)
  109. cand_glia <- rownames(cand_glia_df)
  110. up_glia <- sum(cand_glia_df$log2FoldChange > 0)
  111. down_glia <- sum(cand_glia_df$log2FoldChange < 0)
  113. # (b) For Interaction Effects, use shrunken results for each interaction. No interaction is verified if i. no difference log2FC<|1| or ii. if no significant padj>0.01
  114. res_int_hemo_shrunk <- shrunkenResults[["cellTypehemo.stagel3"]]
  115. res_int_neurons_shrunk <- shrunkenResults[["cellTypeneurons.stagel3"]]
  116. sig_int_hemo_df <- subset(as.data.frame(res_int_hemo_shrunk), (padj > 0.01 | abs(log2FoldChange) < 1))
  117. sig_int_neurons_df <- subset(as.data.frame(res_int_neurons_shrunk), (padj > 0.01 | abs(log2FoldChange) < 1))
  118. sig_int_hemo <- rownames(sig_int_hemo_df)
  119. sig_int_neurons <- rownames(sig_int_neurons_df)
  120. sig_interaction_genes <- unique(intersect(sig_int_hemo, sig_int_neurons))
  122. # (c) Final Candidate (Consistent Stage-Specific Genes) --> intersection between the stage contrast and the interaction terms
  123. final_stage_specific <- intersect(cand_glia, sig_interaction_genes)
  124. final_df <- cand_glia_df[rownames(cand_glia_df) %in% final_stage_specific, ]
  125. up_final <- sum(final_df$log2FoldChange > 0)
  126. down_final <- sum(final_df$log2FoldChange < 0)
  128. # Export Candidate Lists or GO analysis
  129. final_L3_candidates <- rownames(final_df)[final_df$log2FoldChange > 0]
  130. final_E16_candidates <- rownames(final_df)[final_df$log2FoldChange < 0]
  131. output_candidates_L3 <- file.path("/01_transcriptome_stage/geneSelection/Final_Candidate_Genes_Up_in_L3.txt")
  132. write.table(final_L3_candidates, file = output_candidates_L3, quote = FALSE, row.names = FALSE, col.names = FALSE)
  133. output_candidates_E16 <- file.path("/01_transcriptome_stage/geneSelection/Final_Candidate_Genes_Up_in_E16.txt")
  134. write.table(final_E16_candidates, file = output_candidates_E16, quote = FALSE, row.names = FALSE, col.names = FALSE)
  137. # --- 3. Background Extraction for GO Analysis ---
  138. # Define the background universe as: TAKES OUT ALL DETECTED GENES - COUTNS > 0
  139. keep <- rowSums(counts(dds)) > 0
  140. dds2 <- dds[keep, ]
  141. filtered_genes <- rownames(dds2)
  142. # Write them to a text file
  143. write.table(filtered_genes,
  144. file = file.path("/01_transcriptome_stage/geneSelection/filtered_genes_background.txt"),
  145. quote = FALSE,
  146. row.names = FALSE,
  147. col.names = FALSE)
  149. # --- 4. GO Analysis Barplots ---
  150. # define path to find GO analysis
  151. go_file_E16 <- file.path("/01_transcriptome_stage/GO_gene_selection/GO_upE16.csv")
  152. go_file_L3 <- file.path("/01_transcriptome_stage/GO_gene_selection/GO_upL3.csv")
  154. # Read GO result files
  155. go_upE16 <- read.delim(go_file_E16, header = TRUE, sep = ",", stringsAsFactors = FALSE)
  156. go_upL3 <- read.delim(go_file_L3, header = TRUE, sep = ",", stringsAsFactors = FALSE)
  158. # Sort by bonferroni corrected pvalue and take top10 for each stage
  159. go_upE16_sig <- go_upE16[order(go_upE16$Bonferroni), ][1:10, ]
  160. go_upL3_sig <- go_upL3[order(go_upL3$Bonferroni), ][1:10, ]
  162. # Prepare for plotting
  163. go_upE16_sig <- go_upE16_sig %>%
  164. arrange(-Bonferroni) %>% # arrange by significance
  165. mutate(Gene.Set.Name = factor(Gene.Set.Name, levels = Gene.Set.Name))
  167. go_upL3_sig <- go_upL3_sig %>%
  168. arrange(-Bonferroni) %>% # arrange by significance
  169. mutate(Gene.Set.Name = factor(Gene.Set.Name, levels = Gene.Set.Name))
  171. # plot GO term
  172. figure1F_E16 <- ggplot(go_upE16_sig,
  173. aes(x = Gene.Set.Name,
  174. y = log2.fold,
  175. fill = Bonferroni)) +
  176. geom_bar(stat = "identity", width = 0.5, size = 3) +
  177. geom_text(aes(label = paste0(Count.Overlap.Gene, "/", Gene.Set.Size)),
  178. hjust = 1.1, size = 3) +
  179. coord_flip() +
  180. scale_x_discrete(labels = function(x) str_wrap(x, width = 30)) +
  181. scale_fill_gradient(low = "#E64B35FF",
  182. high = "#4DBBD5FF") +
  183. labs(title = "GO Terms for genes upregulated in E16",
  184. x = "GO Term",
  185. y = "Log2 Fold Enrichment",
  186. fill = "Bonferroni p-value") +
  187. theme_minimal()
  189. figure1F_L3 <- ggplot(go_upL3_sig,
  190. aes(x = Gene.Set.Name,
  191. y = log2.fold,
  192. fill = Bonferroni)) +
  193. geom_bar(stat = "identity", width = 0.5, size = 3) +
  194. geom_text(aes(label = paste0(Count.Overlap.Gene, "/", Gene.Set.Size)),
  195. hjust = 1.1, size = 3) +
  196. coord_flip() +
  197. scale_x_discrete(labels = function(x) str_wrap(x, width = 30)) +
  198. scale_fill_gradient(low = "#E64B35FF",
  199. high = "#4DBBD5FF") +
  200. labs(title = "GO Terms for genes upregulated in L3",
  201. x = "GO Term",
  202. y = "Log2 Fold Enrichment",
  203. fill = "Bonferroni p-value") +
  204. theme_minimal()
  207. # --- 5. ploting rest of figures ---
  208. # Define candidate colors using the NPG palette from ggsci:
  209. candidate_colors <- pal_npg()(3)
  212. # Get PCA and percentage of variation explained
  213. pcaData <- plotPCA(vsd, intgroup = c("cellType", "stage"), returnData = TRUE)
  214. percentVar <- round(100 * attr(pcaData, "percentVar"), 1)
  216. # Create PCA plot with percentage of variation explained on the axes
  217. figure1A <- ggplot(pcaData, aes(x = PC1, y = PC2, color = cellType, shape = stage)) +
  218. geom_point(size = 3) +
  219. scale_color_manual(values = c("glia" = candidate_colors[1],
  220. "hemo" = candidate_colors[2],
  221. "neurons" = candidate_colors[3])) +
  222. ggtitle("PCA: Samples by Cell Type & Stage") +
  223. xlab(paste0("PC1: ", percentVar[1], "% variance explained")) +
  224. ylab(paste0("PC2: ", percentVar[2], "% variance explained")) +
  225. theme_minimal()
  228. # Create hierarchical clustering of euclidean distances
  229. sampleDists <- dist(t(assay(vsd)))
  230. hc <- hclust(sampleDists, method = "ward.D2")
  231. dendro_data <- dendro_data(as.dendrogram(hc))
  233. figure1B <- ggplot() +
  234. geom_segment(data = segment(dendro_data),
  235. aes(x = x, y = y, xend = xend, yend = yend)) +
  236. geom_text(data = dendro_data$labels,
  237. aes(x = x, y = y, label = label),
  238. angle = 90, hjust = 1, size = 2.5) +
  239. theme_minimal() +
  240. coord_cartesian(clip = "off") + # allow drawing outside the plot area
  241. theme(plot.margin = unit(c(1, 4, 4, 1), "lines"), # increase bottom margin
  242. axis.text.x = element_blank(),
  243. axis.ticks.x = element_blank(),
  244. plot.title = element_text(hjust = 0.5)) +
  245. labs(title = "Hierarchical Clustering Dendrogram", x = "", y = "Height")
  247. ## Venn Diagram and Heatmap of Final Candidates
  248. figure1D <- venn.diagram(
  249. x = list("Glia Candidates" = cand_glia,
  250. "Hemocytes Interaction" = sig_int_hemo,
  251. "Neurons Interaction" = sig_int_neurons),
  252. filename = NULL,
  253. fill = c(adjustcolor(candidate_colors[1], alpha.f = 0.5),
  254. adjustcolor(candidate_colors[2], alpha.f = 0.5),
  255. adjustcolor(candidate_colors[3], alpha.f = 0.5)),
  256. col = c(NA, NA, NA), # Remove borders
  257. cex = 1,
  258. fontfamily = "sans", # Font for numbers in the circles
  259. cat.cex = 1,
  260. cat.fontfamily = "sans", # Font for the category labels
  261. margin = 0.05,
  262. main = "Stage-specific genes",
  263. main.cex = 1.0,
  264. main.fontfamily = "sans" # Font for the title
  265. )
  267. # plot heatmap of stage-specific genes
  268. figure1E <- pheatmap(assay(vsd)[final_stage_specific, ],
  269. scale = "row",
  270. clustering_distance_rows = "correlation",
  271. clustering_distance_cols = "euclidean",
  272. show_rownames = FALSE,
  273. cellwidth = 15,
  274. main = "Heatmap of z-score of cell-type independent\nstage specific-genes",
  275. color = colorRampPalette(rev(brewer.pal(9, "RdBu")))(255))
  276. ```
  278. # plot figures
  279. ```{r}
  280. # --- Save Individual Plots as PDFs ---
  281. # Save PCA Plot
  282. output_pca <- file.path("/01_transcriptome_stage/figure/Fig1A_PCA_Plot.pdf")
  283. ggsave(output_pca, figure1A, width = 5, height = 3)
  285. # Save Sample Distance Heatmap (dendrogram)
  286. output_dendro <- file.path("/01_transcriptome_stage/figure/Fig1B_Dendrogram.pdf")
  287. pdf(output_dendro, width = 5, height = 3)
  288. grid.draw(figure1B)
  289. dev.off()
  291. # Save Venn Diagram
  292. output_venn <- file.path("/01_transcriptome_stage/figure/Fig1D_Venn_Diagram.pdf")
  293. pdf(output_venn, width = 3, height = 3)
  294. grid.draw(figure1D)
  295. dev.off()
  297. # Save Heatmap Stage
  298. output_heatmap <- file.path("/01_transcriptome_stage/figure/Fig1E_Stage_Heatmap.pdf")
  299. pdf(output_heatmap, width = 7, height = 7)
  300. grid.draw(figure1E)
  301. dev.off()
  303. # Save GO panel
  304. GO_panel <- plot_grid(figure1F_E16, figure1F_L3, ncol = 1, align = "v", rel_heights = c(1, 1))
  305. output_go_E16_L3 <- file.path("/01_transcriptome_stage/figure/GO_panel_E16_L3.pdf")
  306. ggsave(output_go_E16_L3, GO_panel, width = 6, height = 5)
  309. ```

250411_RNAseq_gliaHemocytesNeurons.Rmd at commit 90ba453, no license · at the source

Overview

Authors: Thomas Boutet1,2,3,4,5, Rosy Sakr1,2,3,4, Marta Marzullo6,7, Manisha Goyal8,9, Pierre B Cattenoz1,2,3,4, Laura Ciapponi6, Tina Mukherjee9, Angela Giangrande1,2,3,4
ORCID iDs: Marta Marzullo, Manisha Goyal, Laura Ciapponi, Angela Giangrande
  1. Institut de Génétique et de Biologie Moléculaire et Cellulaire, 67400, Illkirch-Grafenstaden, France
  2. Centre National de la Recherche Scientifique, 67400, Illkirch-Graffenstaden UMR7104, France
  3. Institut National de la Santé et de la Recherche Médicale, 67400, llkirch-Graffenstaden U1258, France
  4. Université de Strasbourg, 67000, Strasbourg, France
  5. Institut de Biologie Moléculaire et Cellulaire, UPR9022, 67084, Strasbourg, France
  6. Department of Biology and Biotechnologies, Sapienza University of Rome, Rome 00185, Italy
  7. IBPM CNR c/o Department of Biology and Biotechnology, Sapienza University of Rome, Rome 00185, Italy
  8. The University of Trans-Disciplinary Health Sciences & Technology (TDU), Bengaluru, Karnataka 560064, India
  9. Institute for Stem Cell Science and Regenerative Medicine (inStem), GKVK, Bellary Road, Bangalore 560065, India
Institutions: Centre National de la Recherche Scientifique (France); Inserm (France); Institut de génétique et de biologie moléculaire et cellulaire (France); Modèles Insectes de l'Immunité Innée (France); Institut de Biologie Moléculaire et Cellulaire (France); Université de Strasbourg (France); Institute of Molecular Biology and Pathology (Italy); Sapienza University of Rome (Italy); University of Trans-Disciplinary Health Sciences and Technology (India); Institute for Stem Cell Biology and Regenerative Medicine (India)
Journal: Nucleic acids research, volume 54, issue 7, article gkag294
Dates: received 18 December 2025; accepted 5 March 2026; published online 13 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/nar/gkag294 · PMID 41978263 · PMCID PMC13076223 · OpenAlex W7154287524
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: drosophila (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions
MeSH: Chromatin*, DNA-Binding Proteins*, Drosophila*, Drosophila melanogaster*, Drosophila Proteins*, Animals, DNA Repair, Gene Expression Regulation, Developmental, Hemocytes, Larva, Neuroglia, Neurons (* major topic)
Topic: Developmental Biology and Gene Regulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: ARC; CNRS; USIAS; ANR labex. L. Ciapponi's lab Sapienza University (RM120172B7D32C04); DST-Core Reasearch; Inserm; University of Strasbourg; Fondation pour la Recherche Médicale (FDT2020010107630); ANR; NIH HHS (P40 OD018537); Hôpital de Strasbourg; CEFIPRA; Ligue Régionale contre le Cancer; UDS
Citations: cited by 1 paper (Europe PMC); 116 references in the paper

Abstract

Cell types are fundamental units of metazoans, however, their definition remains a long-standing challenge. We here use high-throughput assays allowing for unprecedented resolution to analyze and compare the transcriptional landscapes of related and unrelated Drosophila cell types at larval and embryonic stages. Unexpectedly, all cell types share a stage-specific signature that is even stronger than the cell-specific one. Despite having distinct developmental origins and functions, neurons, glia, and hemocytes are more transcriptionally similar to one another within the same developmental stage than they are to the same cell type at different stages. This stage-specific signature is enriched for DNA repair genes at larval stage, particularly the MRN complex (Mre11–Rad50–Nbs). Loss of Rad50 disrupts histone modification patterns and causes inappropriate reactivation of embryonic gene expression programs in larval central nervous system (CNS), as revealed by transcriptomic and chromatin accessibility analyses. The identification of cell-specific and stage-specific signatures highlights a new dimension in the definition of cell identity and suggests a role for Rad50 in maintaining developmentally appropriate chromatin states.

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

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ThomasBLMN/260611_transcriptomePaper

License: none: the authors keep all their rights
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Commit: 90ba453cd332fccdbf8be587396cba5756bcfabb, 19 December 2025
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Size: 9 files, 4 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 4 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), tidyverse (4 files), cowplot (2 files), DESeq2 (2 files), ggpubr (2 files), patchwork (1 file), pheatmap (1 file), reshape2 (1 file), rstatix (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
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At the source: github.com/ThomasBLMN/260611_transcriptomePaper

Zenodo 18978512

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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), tidyverse (4 files), cowplot (2 files), DESeq2 (2 files), ggpubr (2 files), patchwork (1 file), pheatmap (1 file), reshape2 (1 file), rstatix (1 file)
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At the source: doi.org/10.5281/zenodo.18978512

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Data

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Data availability

Sequencing data have been deposited at EBI (E-MTAB-13490, E-MTAB-8702 for RNA-seq, E-MTAB-16072 for ATAC-seq) and are publicly available as of the date of publication. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD056082 and 10.6019/PXD056082. Scripts for figures and genomic analyses are provided on https://github.com/ThomasBLMN/260611_transcriptomePaper and https://doi.org/10.5281/zenodo.18978512.

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Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 12 MeSH terms, 14 funders, 116 references.

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Boutet, T., Sakr, R., Marzullo, M., Goyal, M., Cattenoz, P. B., Ciapponi, L., Mukherjee, T., & Giangrande, A. (2026). Developmental stage dominates cell-type identity and reveals a chromatin regulatory function for Rad50 in Drosophila. Nucleic acids research, 54(7), gkag294. https://doi.org/10.1093/nar/gkag294

BibTeX

@article{boutet2026developmental,
author = {Boutet, Thomas and Sakr, Rosy and Marzullo, Marta and Goyal, Manisha and Cattenoz, Pierre B and Ciapponi, Laura and Mukherjee, Tina and Giangrande, Angela},
title = {{Developmental stage dominates cell-type identity and reveals a chromatin regulatory function for Rad50 in Drosophila}},
journal = {Nucleic acids research},
year = {2026},
month = apr,
volume = {54},
number = {7},
pages = {gkag294},
publisher = {Oxford University Press},
issn = {0305-1048},
doi = {10.1093/nar/gkag294},
url = {https://doi.org/10.1093/nar/gkag294},
pmid = {41978263},
pmcid = {PMC13076223}
}

RIS

TY - JOUR
AU - Boutet, Thomas
AU - Sakr, Rosy
AU - Marzullo, Marta
AU - Goyal, Manisha
AU - Cattenoz, Pierre B
AU - Ciapponi, Laura
AU - Mukherjee, Tina
AU - Giangrande, Angela
TI - Developmental stage dominates cell-type identity and reveals a chromatin regulatory function for Rad50 in Drosophila
T2 - Nucleic acids research
J2 - Nucleic Acids Res
PY - 2026
DA - 2026/04/01
VL - 54
IS - 7
SP - gkag294
SN - 0305-1048
PB - Oxford University Press
DO - 10.1093/nar/gkag294
UR - https://doi.org/10.1093/nar/gkag294
LA - en
ER -

CSL-JSON

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"title": "Developmental stage dominates cell-type identity and reveals a chromatin regulatory function for Rad50 in Drosophila",
"container-title": "Nucleic acids research",
"author": [
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"family": "Boutet",
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"container-title-short": "Nucleic Acids Res",
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"issue": "7",
"page": "gkag294",
"DOI": "10.1093/nar/gkag294",
"PMID": "41978263",
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"ISSN": "0305-1048",
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"language": "en",
"issued": {
"date-parts": [
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2026,
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1
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}

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