OSCR

Multifaceted conserved functions of Notch during post-embryonic neurogenesis in the annelid Platynereis.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Methods and protocols › Transcriptomic analysis of Notch pathway inhibition effects on posterior regeneration ↔ DEG_analysis/script/GO_terms.R, lines 70–143 · score 0.73 · enriched GO terms, term enrichment, dotplots, clusterProfiler, DEG, Transcriptomic
  2. [2] § Methods › Methods and protocols › Transcriptomic analysis of Notch pathway inhibition effects on posterior regeneration ↔ DEG_analysis/script/DEG_analysis.R, lines 156–199 · score 0.61 · removeBatchEffect, batch corrected, quality, PCA, matrix, genes
  3. [3] § Methods › Methods and protocols › Transcriptomic analysis of Notch pathway inhibition effects on posterior regeneration ↔ DEG_analysis/script/DEG_analysis.R, lines 259–334 · score 0.57 · EnhancedVolcano, Trinotate, FDR, log, DEGs, matrix

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R · 334 lines · 11 KB · no license · 2 matches

  1. ## Libraries
  2. library(edgeR)
  3. library(rgl)
  4. library(ggfortify)
  5. library("FactoMineR")
  6. library("factoextra")
  7. library("smacof")
  8. library("dplyr")
  9. library(ggplot2)
  10. library(gplots)
  11. library(statmod)
  12. library(magrittr)
  13. library(EnhancedVolcano)
  14. library(Seurat)
  16. #### Functions
  18. plotPCA <- function(x, groups) {
  19. plot3d(x, col=c("red","blue","green","yellow"), type="s", size=1, axes=F)
  20. axes3d(edges=c("x--", "y--", "z"), lwd=3, axes.len=2, labels=FALSE)
  21. grid3d("x")
  22. grid3d("y")
  23. grid3d("z")
  24. }
  28. # Plot a sample correlation matrix using the log CPM count and a list
  29. # giving column indexes.
  30. plot_individual_cor_heatmap <- function(matrix, list_pos){
  32. nbr_of_rep = length(list_pos)
  33. head_names = colnames(matrix[,list_pos])
  34. submatrix = matrix[,list_pos]
  35. cor_matrix = matrix(nrow = nbr_of_rep, ncol = nbr_of_rep)
  36. rownames(cor_matrix) = head_names
  37. colnames(cor_matrix) = head_names
  39. for (i in 1:nbr_of_rep) {
  40. for (y in 1:nbr_of_rep) {
  41. cor_matrix[i,y] = cor(x = submatrix[,i], y = submatrix[,y])
  42. }
  43. }
  44. colors = c(seq(0.5,0.7,length=100),seq(0.71,0.79,length=100), seq(0.80,1,length=100))
  45. mypalette = colorRampPalette(c("purple","black","yellow"))(n = 299)
  47. par(mar = c(5.1, 6.1, 6.1, 10))
  48. heatmap.2(cor_matrix, col=mypalette,breaks = colors, key=T, keysize=1.5,
  49. density.info="none", trace="none",cexCol=0.9, cexRow = 0.9,
  50. main = "Sample correlation matrix", margins = c(6.5,7),
  51. dendrogram = 'both', symkey = F, symm = F, symbreaks = F,
  52. scale = "none", lwid = c(1,5), lhei = c(1.5,5)
  53. )
  55. }
  58. shorter <- function(str2short){
  59. if(grepl("^", str2short, fixed = T)){
  60. return(unlist(strsplit(str2short,split = "^", fixed = T))[[1]])
  61. }else{
  62. return(unlist(strsplit(str2short,split = "`", fixed = T))[[1]])
  63. }
  64. }
  67. # Concatenate a list into a single string.
  68. agglom <- function(str2aggl){
  69. aggl = ""
  70. for (y in 1:length(unlist(str2aggl))) {
  71. aggl = paste(aggl, shorter(unlist(str2aggl)[y]))
  72. }
  73. return(aggl)
  74. }
  76. # Add annotation to a Edger DEG output matrix with the trinotate report.
  77. add_annotation <- function(table, annotation){
  79. blastx = c()
  80. blastp = c()
  81. GOterms = c()
  82. kegg = c()
  84. for (i in 1:length(table[,1])) {
  85. line = annotation[which(annotation$X.gene_id == rownames(table)[i]),]
  86. blastx = c(blastx, agglom(line[3]))
  87. blastp = c(blastp, agglom(line[7]))
  88. GOterms = c(GOterms, agglom(line[13]))
  89. kegg = c(kegg, agglom(line[12]))
  90. }
  91. table$blastx = blastx
  92. table$blastp = blastp
  93. table$GOterms = GOterms
  94. table$kegg = kegg
  95. return(table)
  96. }
  98. ##
  101. #### Main
  103. # Data Loading:
  105. data = read.table("kallisto/kallisto.gene.counts.matrix",
  106. header = T, row.names = 1, com='', sep = "\t")
  108. trinotate = read.table("trinotate_annotation_report.xls",
  109. header=T, stringsAsFactors = F,sep = "\t", quote="", com='')
  111. cluster_correspondance = read.table("cluster_to_geneID_correspondance.csv",
  112. sep = ",", header = T)
  115. DMSO_1dpa_1tmm = read.table("Tables/DMSO_1dpa/DMSO_1dpa_1TMM_threshold_matrix.csv",
  116. header = T, row.names = 1, com='', sep = ",")
  118. DMSO_2dpa_1tmm = read.table("Tables/DMSO_2dpa/DMSO_2dpa_1TMM_threshold_matrix.csv",
  119. header = T, row.names = 1, com='', sep = ",")
  121. LY_1dpa_1tmm = read.table("Tables/LY_1dpa/LY_1dpa_1TMM_threshold_matrix.csv",
  122. header = T, row.names = 1, com='', sep = ",")
  124. LY_2dpa_1tmm = read.table("Tables/LY_2dpa/LY_2dpa_1TMM_threshold_matrix.csv",
  125. header = T, row.names = 1, com='', sep = ",")
  128. # Contrast preparation:
  129. rep = factor(c("rep1","rep2","rep3","rep1","rep2","rep3","rep1","rep2","rep3","rep1","rep2","rep3"))
  130. cond = factor(c("DMSO_1dpa","DMSO_1dpa","DMSO_1dpa","DMSO_2dpa","DMSO_2dpa","DMSO_2dpa","LY_1dpa","LY_1dpa","LY_1dpa","LY_2dpa","LY_2dpa","LY_2dpa"))
  131. cond = relevel(cond, ref="DMSO_1dpa")
  133. # Create EdgeR object and filter out lowly or non expressed genes.
  136. keep = unique(c(rownames(DMSO_1dpa_1tmm),rownames(DMSO_2dpa_1tmm),
  137. rownames(LY_1dpa_1tmm),rownames(LY_2dpa_1tmm)))
  138. all_gene = rownames(data)
  139. keep2 = rep(FALSE,length(all_gene))
  140. names(keep2) = all_gene
  141. for (i in 1:length(all_gene)) {
  142. if(all_gene[i] %in% keep){
  143. keep2[i] = TRUE
  144. }
  145. }
  148. data = DGEList(counts = data, group = cond)
  149. keep <- filterByExpr(data)
  150. table(keep2)
  151. data <- data[keep2, , keep.lib.sizes = F]
  154. data = calcNormFactors(data)
  156. ### Analysis
  158. ## Data quality
  160. # Plot MDS
  162. data_mds = cpm(data, log=TRUE, prior.count = 1)
  163. plotMDS(data_mds, col=rep(1:4, each = 3), main = "MDS plot before batch correction")
  164. plotMD(data_mds, column = 1)
  166. data_bc = removeBatchEffect(cpm(data, log=TRUE, prior.count = 1), batch = rep, design = model.matrix(~0+cond))
  167. plotMDS(data_bc, col=rep(1:4, each = 3), main = "MDS plot after batch correction")
  169. ## Apply batch effect, run:
  170. data_mds = data_bc
  171. ##
  174. design = model.matrix(~rep+rep:cond)
  175. logFC <- predFC(data, design, prior.count = 1, dispersion = 0.05)
  176. cor(logFC[,6:6])
  179. # compute top 1000 variable gene
  181. data_sd = apply(data_mds, MARGIN = 1, FUN = sd)
  183. data_sd = data.frame(data_mds, data_sd)
  185. data_sd = data_sd[order(-data_sd$data_sd),]
  187. top1000 = head(data_sd, n = 1000)
  189. # ACP generation.
  190. # 2D PCA
  191. res.pca <- PCA(t(data_mds), graph = F)
  192. fviz_eig(res.pca, addlabels = TRUE, ylim = c(0,100))
  193. var <- get_pca_var(res.pca)
  194. fviz_pca_ind(res.pca,axes = c(1,2), geom = c("point","text"),
  195. col.var = cond,
  196. title = "PCA from the initial matrix", col.ind = cond, addEllipses = T) +
  197. theme(panel.background = element_rect(fill= "white", colour = "grey50"),
  198. panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
  199. scale_shape_manual(values=c(15,15,15,15,15,15,15,15,15,15,15,15))
  201. # 3D PCA
  202. pca = prcomp(t(data_mds), scale. = T)
  203. plotPCA(pca$x[,1:3], cond)
  205. #
  207. # Dendrogram
  208. dd <- dist(t(top1000[1:length(top1000[1,])-1]), method = "euclidean")
  209. hc <- hclust(dd, method = "complete")
  211. plot(hc, main = "Dendrogram clustering on top 1000 variable genes", xlab = "Condition")
  213. dd <- dist(t(data_mds), method = "euclidean")
  214. hc <- hclust(dd, method = "complete")
  216. plot(hc, main = "Dendrogram clustering on all genes", xlab = "Condition")
  219. # Heatmap generation
  221. # Basic heatmap
  222. heatmap(x = as.matrix(top1000[1:12]), main = "Heatmap",
  223. ylab = "Genes", xlab = "Condition")
  225. # Heatmap with heatmap2
  226. # matrix arrangement for heatmap.
  227. mat_df =as.matrix(top1000[,1:12])
  228. rownames(mat_df) = rownames(top1000)
  230. colors = c(seq(-10,-2,length=100),seq(-1.9,1.9,length=100), seq(2,10,length=100))
  231. mypalette = colorRampPalette(c("white","orange","red"))(n = 299)
  234. heatmap.2(mat_df, col=mypalette,breaks = colors, key=T, keysize=1.5,
  235. density.info="none", trace="none",cexCol=0.9,
  236. main = "Heatmap of the top 1000 variable transcripts", labRow = F, margins = c(6.5,1.5),
  237. dendrogram = 'column', symkey = F, symm = F, symbreaks = F,
  238. scale = "none", xlab = "Samples", ylab = "Transcripts")
  240. ## Correlation matrix
  242. cor_matrix = matrix(nrow = length(rep), ncol = length(rep))
  243. rownames(cor_matrix) = paste(cond,rep,sep="_")
  244. colnames(cor_matrix) = paste(cond,rep,sep="_")
  245. cor_matrix
  246. for (i in 1:length(cor_matrix[,1])) {
  247. for (y in 1:length(cor_matrix[,1])) {
  248. cor_matrix[i,y] = cor(x = data_bc[,i], y = data_bc[,y])
  249. }
  250. }
  252. colors = c(seq(0.4,0.7,length=100),seq(0.71,0.79,length=100), seq(0.80,1,length=100))
  253. mypalette = colorRampPalette(c("purple","black","yellow"))(n = 299)
  255. hclust2 = function(x){
  256. return(hclust(x, method = "mcquitty"))
  257. }
  259. par(mar = c(5.1, 6.1, 6.1, 3.1))
  260. heatmap.2(cor_matrix,Rowv = T, Colv = T, col=mypalette,breaks = colors, key=T, keysize=1.5,
  261. density.info="none", trace="none",cexCol=0.9,
  262. main = "Sample correlation matrix", margins = c(8,10),
  263. dendrogram = 'both', symkey = F, symm = F, symbreaks = F,
  264. scale = "none", lwid = c(1,5), lhei = c(1.5,5),
  265. RowSideColors = c(
  266. rep("#81061A",3),
  267. rep("#A3B0E4",3),
  268. rep("#A3D0E4",3),
  269. rep("#B0E4A3",3)
  270. ),
  271. ColSideColors = c(
  272. rep("#81061A",3),
  273. rep("#A3B0E4",3),
  274. rep("#A3D0E4",3),
  275. rep("#B0E4A3",3)
  276. )
  277. )
  281. ##
  282. # Differential expression
  284. design = model.matrix(~0+cond+rep)
  285. rownames(design) <- colnames(data)
  287. data <- estimateDisp(data, design, robust = T)
  289. # tests
  290. fit <- glmFit(data, design)
  292. cond_fixed = unique(paste("cond", cond, sep =""))
  293. nbr_of_cond = length(cond_fixed)
  294. for (i in 1:(nbr_of_cond-1)){
  295. for(y in 1:(nbr_of_cond-i)){
  296. cond1 = cond_fixed[i]
  297. cond2 = cond_fixed[i+y]
  298. actual_contrast = makeContrasts(paste(cond2, "-", cond1,sep=""), levels = colnames(design))
  300. print(actual_contrast)
  301. lrt <- glmLRT(fit, contrast = actual_contrast)
  302. DET_matrix = topTags(lrt, n = length(lrt$table[,1]))
  303. cond1 = substr(cond1, 5,100)
  304. cond2 = substr(cond2, 5,100)
  305. volcano = EnhancedVolcano(DET_matrix$table,
  306. lab = NA,
  307. x = 'logFC',
  308. y = 'FDR',
  309. ylim = c(0,20),
  310. title = paste("Volcano Plot of ",cond1, " vs ", cond2, " conditions",sep=""),
  311. subtitle = bquote(italic("Log2 FC threshold = 1 and p_value (FDR) threshold = 0.05")),
  312. FCcutoff = 1,
  313. pCutoff = 0.05,
  314. caption = paste0("total = ", nrow(DET_matrix$table), " genes"))
  315. ggsave(filename = paste("DEG/",cond1,"vs",cond2,"/volcano_plot.pdf", sep = ""), plot = volcano, device = "pdf")
  317. thresholded_matrix = DET_matrix$table[which(abs(DET_matrix$table$logFC) > 1 & DET_matrix$table$FDR < 0.05),]
  318. pdf(file = paste("DEG/",cond1,"vs",cond2,"/MA_plot.pdf", sep = ""))
  319. plotSmear(lrt, de.tags = rownames(thresholded_matrix))
  320. dev.off()
  321. write.table(x = thresholded_matrix, file = paste("DEG/",cond1,"vs",cond2,"/DEG_result_",cond1,"vs",cond2,"_FC_and_FDR_filter.tsv", sep = ""),
  322. sep = "\t", row.names = T, col.names = NA)
  324. write.table(x = DET_matrix$table, file = paste("DEG/",cond1,"vs",cond2,"/DEG_result_",cond1,"vs",cond2,".tsv", sep = ""),
  325. sep = "\t", row.names = T, col.names = NA)
  327. annotated_matrix = add_annotation(thresholded_matrix, trinotate)
  329. write.table(x = annotated_matrix,
  330. file = paste0("DEG/",cond1,"vs",cond2,"/DEG_result_",cond1,"vs",cond2,"_FC_and_FDR_filter_trinotate.tsv"),
  331. sep = "\t", row.names = T, col.names = NA)
  332. }
  333. }
  334. ##

DEG_analysis.R at commit acdbbea, no license · at the source

Overview

Authors: Loïc Bideau1, Loeiza Baduel1, Gabriel Krasovec1, Caroline Dalle1, Ombeline Lamer1, Mélusine Nicolas1, Alexandre Couëtoux1, Corinne Blugeon2, Louis Paré1, Michel Vervoort1, Pierre Kerner1, Eve Gazave1
ORCID iDs: Loïc Bideau, Loeiza Baduel, Gabriel Krasovec, Ombeline Lamer, Louis Paré, Pierre Kerner, Eve Gazave
  1. Université Paris Cité, CNRS, Institut Jacques Monod,F-75013 Paris, France
  2. GenomiqueENS, Institut de Biologie de l’ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, Université PSL,Paris, France
Institutions: Institut Jacques Monod (France); Université Paris Sciences et Lettres (France)
Journal: EMBO reports, volume 27, issue 9, pages 2345-2368
Dates: received 15 April 2025; accepted 12 February 2026; published online 1 April 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44319-026-00731-6 · PMID 41922842 · PMCID PMC13172424 · OpenAlex W7147317305
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: developmental (subfield)
Methods: Preprocessing, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: Annelid, Neurogenesis, Notch, Posterior Growth, Regeneration, Development, Evolution & Ecology, Neuroscience
MeSH: Neurogenesis*, Polychaeta*, Receptors, Notch*, Animals, Cell Differentiation, Gene Expression Regulation, Developmental, Neurons, Regeneration, Signal Transduction (* major topic)
Topic: Planarian Biology and Electrostimulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Labex Who Am I (ANR- 11-LABX-0071); Agence Nationale de la Recherche (French National Research Agency) (ANR-19-CE27-0027-01); CNRS Diversity of Biological Mechanisms; Fondation ARC pour la Recherche sur le Cancer (ARC) (PJA 20191209482); Ligue Contre le Cancer (RS20/75-20); Université Paris Cité; ENS Lyon; France Génomique, investissement d'avenir, ANR (ANR-10-INBS-0009); Investment for the Future (ANR-11-IDEX-0005-01)
Citations: not cited yet (Europe PMC); 76 references in the paper
Research resources: Imaris 9.5.0 RRID:SCR_007370

Abstract

Notch signaling is an evolutionarily conserved pathway known to orchestrate neurogenesis by regulating the transition from progenitors to neurons and glia, and by directing neurite outgrowth and axon guidance in many species. Although extensively studied in vertebrates and ecdysozoans, the role of Notch in spiralians remains unexplored, limiting our understanding of its conserved functions across bilaterians. Here we focus on the segmented annelid Platynereis dumerilii, a model organism in neurobiology and regeneration, to investigate Notch signaling functions during post-embryonic developmental processes. We show that Notch pathway components are expressed in neurogenic territories during both posterior elongation and regeneration, two processes requiring sustained neurogenesis. Through chemical inhibitions of the pathway and RNA-seq profiling, we find that Notch signaling regulates neural progenitor specification, differentiation, and overall neurogenic balance in the regenerating and elongating posterior part. Disruption of Notch signaling activity leads to severe defects in pygidial and central nervous system organization. Altogether, our results support the hypothesis that Notch has multifaceted conserved functions in neurogenesis across bilaterians, shedding light on the ancestral functions of this critical pathway.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

StemDevEvo/Notch-2025

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: acdbbea053657bf42749f7d183ce558e4cc5c149, 22 January 2026
Languages: R (8), Shell (7)
Size: 227 files, 15 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (Mapping/environment.yml)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: ggplot2 (4 files), edgeR (3 files), tidyverse (3 files), clusterProfiler (1 file), FastQC (1 file), Seurat (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
16 files
At the source: github.com/StemDevEvo/Notch-2025

Zenodo 18341641

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data 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 answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source:

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 15 scripts, each with its path and the digest of its content;
  • 3 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

Other data links

Data availability

The sequencing data generated in this project have been deposited at the European Nucleotide Archive (ENA) repository under the project accession number PRJEB63219 (https://www.ebi.ac.uk/ena/browser/view/PRJEB63219). The scripts used in this project are available on Zenodo 10.5281/zenodo.18341641 or at https://github.com/StemDevEvo/Notch-2025. The original confocal and bright field images have been deposited at the BioImage Archive with accession number S-BIAD2428 (https://www.ebi.ac.uk/biostudies/BioImages/studies/S-BIAD2428?query=S-BIAD2428).

The source data of this paper are collected in the following database record: biostudies:S-SCDT-10_1038-S44319-026-00731-6 (https://www.ebi.ac.uk/biostudies/sourcedata/studies/S-SCDT-10_1038-S44319-026-00731-6).

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, 12 authors, 8 keywords, 9 MeSH terms, 9 funders, 72 references, 1 RRID.

Cite

This paper

Bideau, L., Baduel, L., Krasovec, G., Dalle, C., Lamer, O., Nicolas, M., Couëtoux, A., Blugeon, C., Paré, L., Vervoort, M., Kerner, P., & Gazave, E. (2026). Multifaceted conserved functions of Notch during post-embryonic neurogenesis in the annelid Platynereis. EMBO reports, 27(9), 2345-2368. https://doi.org/10.1038/s44319-026-00731-6

BibTeX

@article{bideau2026multifaceted,
author = {Bideau, Loïc and Baduel, Loeiza and Krasovec, Gabriel and Dalle, Caroline and Lamer, Ombeline and Nicolas, Mélusine and Couëtoux, Alexandre and Blugeon, Corinne and Paré, Louis and Vervoort, Michel and Kerner, Pierre and Gazave, Eve},
title = {{Multifaceted conserved functions of Notch during post-embryonic neurogenesis in the annelid Platynereis}},
journal = {EMBO reports},
year = {2026},
month = apr,
volume = {27},
number = {9},
pages = {2345--2368},
publisher = {Nature Publishing Group},
issn = {1469-221X},
doi = {10.1038/s44319-026-00731-6},
url = {https://doi.org/10.1038/s44319-026-00731-6},
pmid = {41922842},
pmcid = {PMC13172424}
}

RIS

TY - JOUR
AU - Bideau, Loïc
AU - Baduel, Loeiza
AU - Krasovec, Gabriel
AU - Dalle, Caroline
AU - Lamer, Ombeline
AU - Nicolas, Mélusine
AU - Couëtoux, Alexandre
AU - Blugeon, Corinne
AU - Paré, Louis
AU - Vervoort, Michel
AU - Kerner, Pierre
AU - Gazave, Eve
TI - Multifaceted conserved functions of Notch during post-embryonic neurogenesis in the annelid Platynereis
T2 - EMBO reports
J2 - EMBO Rep
PY - 2026
DA - 2026/04/01
VL - 27
IS - 9
SP - 2345
EP - 2368
SN - 1469-221X
PB - Nature Publishing Group
DO - 10.1038/s44319-026-00731-6
UR - https://doi.org/10.1038/s44319-026-00731-6
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s44319-026-00731-6",
"type": "article-journal",
"title": "Multifaceted conserved functions of Notch during post-embryonic neurogenesis in the annelid Platynereis",
"container-title": "EMBO reports",
"author": [
{
"family": "Bideau",
"given": "Loïc"
},
{
"family": "Baduel",
"given": "Loeiza"
},
{
"family": "Krasovec",
"given": "Gabriel"
},
{
"family": "Dalle",
"given": "Caroline"
},
{
"family": "Lamer",
"given": "Ombeline"
},
{
"family": "Nicolas",
"given": "Mélusine"
},
{
"family": "Couëtoux",
"given": "Alexandre"
},
{
"family": "Blugeon",
"given": "Corinne"
},
{
"family": "Paré",
"given": "Louis"
},
{
"family": "Vervoort",
"given": "Michel"
},
{
"family": "Kerner",
"given": "Pierre"
},
{
"family": "Gazave",
"given": "Eve"
}
],
"container-title-short": "EMBO Rep",
"volume": "27",
"issue": "9",
"page": "2345-2368",
"DOI": "10.1038/s44319-026-00731-6",
"PMID": "41922842",
"PMCID": "PMC13172424",
"ISSN": "1469-221X",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s44319-026-00731-6",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
1
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: edgeR, clusterProfiler, Seurat, 2 other tools, 1 reference
[2] doi:10.1093/bioinformatics/btag592 [code]
Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease.
Journal: Bioinformatics (Oxford, England)
In common: FastQC, edgeR, clusterProfiler, 2 other tools
[3] doi:10.1186/s12974-026-03739-w [code]
Sex specific effects of adoptive Tregs transfer on the brain and periphery in maternal immune activation offspring rescuing immune dysregulation.
Journal: Journal of neuroinflammation
In common: FastQC, edgeR, clusterProfiler, 2 other tools
[4] doi:10.1038/s41593-026-02300-5 [code]
Integrated single-cell and spatial transcriptomic profiling in ALS uncovers peripheral-to-central immune infiltration and reprogramming.
Journal: Nature neuroscience
In common: FastQC, edgeR, Seurat, 2 other tools
[5] doi:10.1038/s41593-026-02367-0 [code]
A reproducible three-dimensional model of human brain tissue to investigate physiological and disease-associated microglia phenotypes.
Journal: Nature neuroscience
In common: edgeR, clusterProfiler, Seurat, 2 other tools, 1 reference
[6] doi:10.3390/ijms27136068 [code]
Loss of Neuropeptide Y Signaling Accompanies the Neural-to-Mesenchymal Transcriptional Transition in Glioblastoma: A Multi-Scale Transcriptomic Analysis.
Journal: International journal of molecular sciences
In common: edgeR, clusterProfiler, Seurat, 2 other tools, 1 reference
[7] doi:10.1101/gr.281113.125 [code]
Single-nucleus multiomic profiling of the aging mouse substantia nigra reveals conserved gene alterations linked to Parkinson's disease.
Journal: Genome research
In common: edgeR, clusterProfiler, Seurat, 2 other tools, 1 reference
[8] doi:10.1038/s41586-026-10214-2 [code]
Multidimensional profiling of heterogeneity in supratentorial ependymomas.
Journal: Nature
In common: edgeR, clusterProfiler, Seurat, 2 other tools, 1 reference
[9] doi:10.1038/s42003-026-10059-5 [code]
Transcriptomic analysis in autism spectrum disorder suggests three molecular subtypes with distinct phenotypic profiles and functional pathways.
Journal: Communications biology
In common: FastQC, edgeR, Seurat, 1 other tool
[10] doi:10.1002/imt2.70163 [code]
Spatial multi-omics unveils sphingolipid metabolic reprogramming within the retinal pathological niche.
Journal: iMeta
In common: edgeR, clusterProfiler, Seurat, 2 other tools

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.