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Transcriptome-wide profiling of cystic and solid vestibular schwannomas reveals candidate long non-coding RNA signatures.

A correction to this paper has been published: the notice, 42622948, from Europe PMC.

Code ↔ Paper

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  1. [1] § Results › LncRNA-protein coding genes correlation reveals cVS signatures ↔ coexpression_plots.R, the whole file · a weak match · score 0.87 · K14.12, AC132217.4, F11.4, F13.3, TENM3 AS1, ADIRF AS1
  2. [2] § Results › LncRNA-protein coding genes correlation reveals cVS signatures ↔ run_DESeq2_lncRNAs.R, lines 151–218 · score 0.76 · AC132217.4, F11.4, F13.3, TENM3 AS1, EGFLAM AS1, RP11
  3. [3] § Results › Differential regulation of lncRNAs and protein-coding genes (PCGs) in vestibular schwannoma ↔ run_DESeq2_lncRNAs.R, lines 151–218 · score 0.60 · K13.1, TENM3 AS1, EGFLAM AS1, fold change, RP5, LncRNAs
  4. [4] § Methods › Transcriptome analysis of vestibular schwannoma ↔ run_hisat2PE.sh, the whole file · a weak match · score 0.57 · HISAT2, hg38, splice, BAM, FASTQ, genome
  5. [5] § Methods › Transcriptome analysis of vestibular schwannoma ↔ run_featurecountsPE.sh, the whole file · a weak match · score 0.57 · featureCounts, Subread, BAM, quantification, GENCODE, v38

Paper

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The authors' code

R · 300 lines · 14 KB · no license · 2 matches

  2. module load bioinfo-tools
  3. module load R/4.0.0
  4. module load R_packages/4.0.0
  5. R
  8. library("DESeq2")
  9. library(ComplexHeatmap)
  10. library(ggplot2)
  11. library('PCAtools')
  12. library(ggbiplot, lib.loc = "/proj/uppstore2018034/santhilal/Dakshu_data/R")
  15. rpath <- "/proj/uppstore2018034/santhilal/Vestibular_schwannoma_proj/";
  16. inpath <- paste0(rpath,"quantification/");
  17. outpath <- paste0(rpath,"DE_analysis/");
  18. plot_path <- paste0(rpath,"DE_analysis/DEplots/");
  19. coldata <- read.table(paste0(rpath,"scripts/sample_table.txt"), sep="\t", row.names=1, header=T)
  23. #### data loading (LincRNAs)
  25. annot <- read.table(paste0(rpath,"reference/annotation/gencode.v38.long_noncoding_RNAs_annotation.txt"), sep="\t", header=T, row.names=1)
  26. x <- read.table(paste0(inpath,"gencode.v38_lncRNAs_gcounts.txt"), sep="\t", header=T, row.names=1)
  28. #exprCols <- c(1:14)
  30. exprCols <- c(1,4,8,10,11,14)
  31. #exprCols <- c(1,4,7,8,10,11,14)
  32. coldata <- coldata[c(1,4,8,10,11,14),]
  33. #coldata <- coldata[c(1,4,7,8,10,11,14),]
  34. #tmp <- x[apply(x[, exprCols], 1, function(x) {all(x >= 1)}),c(1,exprCols)]
  35. tmp <- x
  36. cts <- tmp[,exprCols]
  37. cts <- as.matrix(cts)
  38. cts <- cts[, rownames(coldata)]
  39. all(rownames(coldata) == colnames(cts))
  43. ####### Quality control measures using normalized counts (CPM) - LncRNAs
  45. dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ sample_group)
  46. dds <- dds[ rowSums(counts(dds)) > 1, ]
  47. dds$condition <- factor(dds$sample_group, levels = levels(coldata$sample_group))
  48. dds <- DESeq(dds)
  49. ddsnorm <- as.data.frame(counts(dds, normalized=T))
  51. pdf(paste0(plot_path,"PCA_plot_lncRNAs.pdf"))
  53. my.pca <- prcomp(ddsnorm, center = TRUE,scale. = TRUE)
  54. ggbiplot(my.pca)
  57. p <- pca(ddsnorm, metadata = coldata, removeVar = 0.1)
  58. screeplot(p, axisLabSize = 18, titleLabSize = 22)
  59. biplot(p, lab = paste0(p$metadata$OriginalIDs), colby = 'sample_group', hline = 0, vline = 0, legendPosition = 'right')
  60. pairsplot(p)
  61. #eigencorplot(p, metavars = c('sample_type','age_group','age','mutation_group','group','general_group','age_by_median'))
  62. horn <- parallelPCA(ddsnorm)
  63. horn$n
  64. elbow <- findElbowPoint(p$variance)
  65. elbow
  66. screeplot(p,components = getComponents(p, 1:20),vline = c(horn$n, elbow)) + geom_label(aes(x = horn$n + 1, y = 50,label = 'Horn\'s', vjust = -1, size = 8)) + geom_label(aes(x = elbow + 1, y = 50,label = 'Elbow method', vjust = -1, size = 8))
  67. which(cumsum(p$variance) > 80)[1]
  68. biplot(p, lab = paste0(p$metadata$condition), colby = 'sample_group', hline = 0, vline = 0, legendPosition = 'right')
  70. dev.off()
  72. pdf(paste0(plot_path,"complete_correlation_plot_lncRNAs.pdf"))
  74. m1y <- t(apply(ddsnorm, 1, function(y) (y - mean(y)) / sd(y) ^ as.logical(sd(y))))
  75. top_ha = HeatmapAnnotation(sampleType=coldata$sample_group, Condition=coldata$condition, Group=coldata$group)
  76. Heatmap(m1y, show_row_names=F, show_column_names=F, top_annotation = top_ha)
  78. top_ha = HeatmapAnnotation(sampleType=coldata$sample_group, Condition=coldata$condition, Group=coldata$group)
  79. row_ha = rowAnnotation(sampleType=coldata$sample_group, Condition=coldata$condition, Group=coldata$group)
  80. Heatmap(cor(ddsnorm), show_row_names=F, show_column_names=F, cluster_columns=T, cluster_rows=T, top_annotation = top_ha, left_annotation = row_ha)
  82. dev.off()
  85. ##### multi-group comparisons (lncRNAs)
  87. compGroup <- ~ sample_group
  88. mygroup <- "sample_group"
  90. annot <- read.table(paste0(rpath,"reference/annotation/gencode.v38.long_noncoding_RNAs_annotation.txt"), sep="\t", header=T, row.names=1)
  92. dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = compGroup )
  93. dds <- dds[ rowSums(counts(dds)) > 1, ]
  94. dds$condition <- factor(coldata[,as.character(mygroup)], levels = levels(factor(coldata[,as.character(mygroup)])) )
  95. dds <- DESeq(dds)
  96. #mysamples <- levels(factor(coldata[,as.character(mygroup)]))
  99. res1 <- results(dds, contrast=c(as.character(mygroup),"CVS","SVS"), alpha=0.05 ) #sample/control for log2FC # HGPS vs young
  100. res1Anot <- merge(as.data.frame(res1),annot,by="row.names")
  101. resSig1 <- subset(res1, abs(res1$log2FoldChange) > 1 & res1$padj < 0.05 )
  103. resSig1 <- resSig1[order(resSig1$padj),]
  104. resSig1Anot <- merge(as.data.frame(resSig1),annot,by="row.names")
  105. colnames(resSig1Anot)[1] <- "Geneid"
  108. ddsnorm <- as.data.frame(counts(dds, normalized=T))
  109. ddsnorm[,"Geneid"] <- rownames(ddsnorm)
  110. selectedCols <- subset(rownames(coldata),coldata[,as.character(mygroup)]=="CVS" | coldata[,as.character(mygroup)]=="SVS")
  111. ddsnorm <- ddsnorm[,c("Geneid",as.character(selectedCols))]
  113. resSig1AnotExp <- merge(resSig1Anot,ddsnorm,by="Geneid")
  114. resSig1AnotExp <- resSig1AnotExp[order(resSig1AnotExp$padj),]
  118. write.table(resSig1AnotExp, paste0(outpath,"CVS_vs_SVS_lncRNAs_DE.txt"), sep="\t", row.names=F, quote=F)
  120. write.table(res1Anot, paste0(outpath,"CVS_vs_SVS_lncRNAs_all_stat.txt"), sep="\t", row.names=F, quote=F)
  124. pdf(paste0(plot_path,"DE_lncRNAs_heatmap.pdf"))
  126. normDE <-resSig1AnotExp[,12:length(colnames(resSig1AnotExp))]
  127. m1y <- t(apply(normDE, 1, function(y) (y - mean(y)) / sd(y) ^ as.logical(sd(y))))
  128. rownames(m1y) <- resSig1AnotExp$GeneSymbol
  129. colx <- coldata[colnames(resSig1AnotExp[,12:length(colnames(resSig1AnotExp))]),]
  130. top_ha = HeatmapAnnotation(sampletype=colx$sample_group, condition=colx$condition)
  131. Heatmap(m1y, show_row_names=T, show_column_names=T, top_annotation = top_ha, column_names_gp = grid::gpar(fontsize = 6), row_names_gp = grid::gpar(fontsize = 6))
  133. dev.off()
  137. pdf(paste0(plot_path,"DE_lncRNAs_volcano_new.pdf"))
  138. plot(res1Anot$log2FoldChange,-log10(res1Anot$padj),col="#ADADAE",bg="#ADADAE", main="CVS vs SVS lncRNAs", xlim=c(-10,10))
  139. points(subset(res1Anot$log2FoldChange,res1Anot$padj < 0.05 & res1Anot$log2FoldChange > 1 ),-log10(subset(res1Anot$padj,res1Anot$padj < 0.05 & res1Anot$log2FoldChange > 1 )),col="#EB4F4F")
  140. points(subset(res1Anot$log2FoldChange,res1Anot$padj < 0.05 & res1Anot$log2FoldChange < -1 ),-log10(subset(res1Anot$padj,res1Anot$padj < 0.05 & res1Anot$log2FoldChange < -1 )),col="#1A21AC")
  142. topFilt <- subset(res1Anot,res1Anot$GeneSymbol %in% c("RP5-841K13.1","EGFLAM-AS1","RP11-43F13.3","AC132217.4","RP11-728F11.4","RP11-234O6.2","TENM3-AS1","AC005754.7","RP11-67L3.5","RP11-67L3.7") )
  144. points(topFilt$log2FoldChange, -log10(topFilt$padj),col="black",bg="#DBA901", pch=21)
  146. abline(h=-log10(0.05),v=c(-1,1), col="#ADADAE", lty = 2)
  147. text(topFilt$log2FoldChange, -log10(topFilt$padj), labels=topFilt$GeneSymbol, pos=4,cex=0.5, col="black")
  148. #points(subset(res1Anot$log2FoldChange,res1Anot$GeneSymbol %in% c("RP5-841K13.1","EGFLAM-AS1","RP11-43F13.3","AC132217.4","RP11-728F11.4","RP11-234O6.2","TENM3-AS1","AC005754.7","RP11-67L3.5","RP11-67L3.7") ),-log10(subset(res1Anot$padj,res1Anot$GeneSymbol %in% c("RP5-841K13.1","EGFLAM-AS1","RP11-43F13.3","AC132217.4","RP11-728F11.4","RP11-234O6.2","TENM3-AS1","AC005754.7","RP11-67L3.5","RP11-67L3.7") )),col="black",bg="#DBA901", pch=21)
  149. #abline(h=-log10(0.05),v=c(-1,1), col="#ADADAE", lty = 2)
  151. #topg <- c("RP5-841K13.1","EGFLAM-AS1","RP11-43F13.3","AC132217.4","RP11-728F11.4","RP11-234O6.2","TENM3-AS1","AC005754.7","RP11-67L3.5","RP11-67L3.7")
  152. #text(subset(res1Anot$log2FoldChange,res1Anot$GeneSymbol %in% c("RP5-841K13.1","EGFLAM-AS1","RP11-43F13.3","AC132217.4","RP11-728F11.4","RP11-234O6.2","TENM3-AS1","AC005754.7","RP11-67L3.5","RP11-67L3.7") ),-log10(subset(res1Anot$padj,res1Anot$GeneSymbol %in% c("RP5-841K13.1","EGFLAM-AS1","RP11-43F13.3","AC132217.4","RP11-728F11.4","RP11-234O6.2","TENM3-AS1","AC005754.7","RP11-67L3.5","RP11-67L3.7") )), labels=topg, pos=4,cex=0.5, col="black")
  154. dev.off()
  159. #### data loading (PCGs)
  161. annot <- read.table(paste0(rpath,"reference/annotation/gencode.v38.basic.annotation.txt"), sep="\t", header=T, row.names=1)
  162. x <- read.table(paste0(inpath,"gencode.v38_all_gcounts.txt"), sep="\t", header=T, row.names=1)
  166. #exprCols <- c(1:14)
  167. #exprCols <- c(1,4,7,8,10,11,14)
  168. exprCols <- c(1,4,8,10,11,14)
  169. #coldata <- coldata[c(1,4,7,8,10,11,14),]
  170. coldata <- coldata[c(1,4,8,10,11,14),]
  171. #tmp <- x[apply(x[, exprCols], 1, function(x) {all(x >= 1)}),c(1,exprCols)]
  172. tmp <- x
  173. cts <- tmp[,exprCols]
  174. cts <- as.matrix(cts)
  175. cts <- cts[, rownames(coldata)]
  176. all(rownames(coldata) == colnames(cts))
  178. cts <- merge(cts, annot, by=0)
  179. colnames(cts)[1] <- "Geneid"
  180. cts <- subset(cts, cts$Class=="protein_coding")
  181. rownames(cts) <- cts$Geneid
  182. #cts <- cts[,c(2:8)]
  183. cts <- cts[,c(2:7)]
  184. ####### Quality control measures using normalized counts (CPM) - LncRNAs
  186. dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ sample_group)
  187. dds <- dds[ rowSums(counts(dds)) > 1, ]
  188. dds$condition <- factor(dds$sample_group, levels = levels(coldata$sample_group))
  189. dds <- DESeq(dds)
  190. ddsnorm <- as.data.frame(counts(dds, normalized=T))
  192. pdf(paste0(plot_path,"PCA_plot_PCGs.pdf"))
  193. p <- pca(ddsnorm, metadata = coldata, removeVar = 0.1)
  194. screeplot(p, axisLabSize = 18, titleLabSize = 22)
  195. biplot(p, showLoadings = TRUE, lab = NULL)
  196. pairsplot(p)
  197. #eigencorplot(p, metavars = c('sample_type','age_group','age','mutation_group','group','general_group','age_by_median'))
  198. horn <- parallelPCA(ddsnorm)
  199. horn$n
  200. elbow <- findElbowPoint(p$variance)
  201. elbow
  202. screeplot(p,components = getComponents(p, 1:20),vline = c(horn$n, elbow)) + geom_label(aes(x = horn$n + 1, y = 50,label = 'Horn\'s', vjust = -1, size = 8)) + geom_label(aes(x = elbow + 1, y = 50,label = 'Elbow method', vjust = -1, size = 8))
  203. which(cumsum(p$variance) > 80)[1]
  204. biplot(p, lab = paste0(p$metadata$condition), colby = 'sample_group', hline = 0, vline = 0, legendPosition = 'right')
  206. dev.off()
  208. pdf(paste0(plot_path,"complete_correlation_plot_PCGs.pdf"))
  210. m1y <- t(apply(ddsnorm, 1, function(y) (y - mean(y)) / sd(y) ^ as.logical(sd(y))))
  211. #top_ha = HeatmapAnnotation(sampleType=coldata$sample_group, Condition=coldata$condition, Group=coldata$group)
  212. #Heatmap(m1y, show_row_names=F, show_column_names=F, top_annotation = top_ha)
  214. top_ha = HeatmapAnnotation(sampleType=coldata$sample_group, Condition=coldata$condition, Group=coldata$group)
  215. row_ha = rowAnnotation(sampleType=coldata$sample_group, Condition=coldata$condition, Group=coldata$group)
  216. Heatmap(cor(ddsnorm), show_row_names=F, show_column_names=F, cluster_columns=T, cluster_rows=T, top_annotation = top_ha, left_annotation = row_ha)
  218. dev.off()
  221. ##### multi-group comparisons (PCGs)
  223. compGroup <- ~ sample_group
  224. mygroup <- "sample_group"
  226. annot <- read.table(paste0(rpath,"reference/annotation/gencode.v38.basic.annotation.txt"), sep="\t", header=T, row.names=1)
  230. dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = compGroup )
  231. dds <- dds[ rowSums(counts(dds)) > 1, ]
  232. dds$condition <- factor(coldata[,as.character(mygroup)], levels = levels(factor(coldata[,as.character(mygroup)])) )
  233. dds <- DESeq(dds)
  234. #mysamples <- levels(factor(coldata[,as.character(mygroup)]))
  237. res1 <- results(dds, contrast=c(as.character(mygroup),"CVS","SVS"), alpha=0.05 ) #sample/control for log2FC # HGPS vs young
  238. res1Anot <- merge(as.data.frame(res1),annot,by="row.names")
  239. resSig1 <- subset(res1, abs(res1$log2FoldChange) > 1 & res1$padj < 0.05 )
  241. resSig1 <- resSig1[order(resSig1$padj),]
  242. resSig1Anot <- merge(as.data.frame(resSig1),annot,by="row.names")
  243. colnames(resSig1Anot)[1] <- "Geneid"
  246. ddsnorm <- as.data.frame(counts(dds, normalized=T))
  247. ddsnorm[,"Geneid"] <- rownames(ddsnorm)
  248. selectedCols <- subset(rownames(coldata),coldata[,as.character(mygroup)]=="CVS" | coldata[,as.character(mygroup)]=="SVS")
  249. ddsnorm <- ddsnorm[,c("Geneid",as.character(selectedCols))]
  251. resSig1AnotExp <- merge(resSig1Anot,ddsnorm,by="Geneid")
  252. resSig1AnotExp <- resSig1AnotExp[order(resSig1AnotExp$padj),]
  255. write.table(resSig1AnotExp, paste0(outpath,"CVS_vs_SVS_PCGs_DE.txt"), sep="\t", row.names=F, quote=F)
  257. write.table(res1Anot, paste0(outpath,"CVS_vs_SVS_PCGs_all_stat.txt"), sep="\t", row.names=F, quote=F)
  262. pdf(paste0(plot_path,"DE_PCGs_heatmap.pdf"))
  264. normDE <-resSig1AnotExp[,12:length(colnames(resSig1AnotExp))]
  265. m1y <- t(apply(normDE, 1, function(y) (y - mean(y)) / sd(y) ^ as.logical(sd(y))))
  266. rownames(m1y) <- resSig1AnotExp$GeneSymbol
  267. colx <- coldata[colnames(resSig1AnotExp[,12:length(colnames(resSig1AnotExp))]),]
  268. top_ha = HeatmapAnnotation(sampletype=colx$sample_group, condition=colx$condition)
  269. Heatmap(m1y, show_row_names=T, show_column_names=T, top_annotation = top_ha, column_names_gp = grid::gpar(fontsize = 6), row_names_gp = grid::gpar(fontsize = 1))
  271. dev.off()
  275. pdf(paste0(plot_path,"DE_PCGs_volcano_new.pdf"))
  276. plot(res1Anot$log2FoldChange,-log10(res1Anot$padj),col="#ADADAE",bg="#ADADAE", main="CVS vs SVS PCGs")
  277. points(subset(res1Anot$log2FoldChange,res1Anot$padj < 0.05 & res1Anot$log2FoldChange > 1 ),-log10(subset(res1Anot$padj,res1Anot$padj < 0.05 & res1Anot$log2FoldChange > 1 )),col="#EB4F4F")
  278. points(subset(res1Anot$log2FoldChange,res1Anot$padj < 0.05 & res1Anot$log2FoldChange < -1 ),-log10(subset(res1Anot$padj,res1Anot$padj < 0.05 & res1Anot$log2FoldChange < -1 )),col="#1A21AC")
  280. topFilt <- subset(res1Anot,res1Anot$GeneSymbol %in% c("EGFLAM","CYP2E1","MCOLN2","KCNS3","ENPP2","TBC1D10A","DNAI3","ZC3H7B","OLFML3","BEX1","GJA1","APOBEC3C","RANBP1","CALB2","TMEM184B") )
  282. points(topFilt$log2FoldChange,-log10(topFilt$padj),col="black",bg="#DBA901", pch=21)
  283. abline(h=-log10(0.05),v=c(-1,1), col="#ADADAE", lty = 2)
  285. text(topFilt$log2FoldChange, -log10(topFilt$padj), labels=topFilt$GeneSymbol, pos=4,cex=0.5, col="black")
  287. #points(subset(res1Anot$log2FoldChange,res1Anot$GeneSymbol %in% c("EGFLAM","CYP2E1","MCOLN2","KCNS3","ENPP2","TBC1D10A","DNAI3","ZC3H7B","OLFML3","BEX1","GJA1","APOBEC3C","RANBP1","CALB2","TMEM184B") ),-log10(subset(res1Anot$padj,res1Anot$GeneSymbol %in% c("EGFLAM","CYP2E1","MCOLN2","KCNS3","ENPP2","TBC1D10A","DNAI3","ZC3H7B","OLFML3","BEX1","GJA1","APOBEC3C","RANBP1","CALB2","TMEM184B") )),col="black",bg="#DBA901", pch=21)
  288. #abline(h=-log10(0.05),v=c(-1,1), col="#ADADAE", lty = 2)
  290. #topg <- c("EGFLAM","CYP2E1","MCOLN2","KCNS3","ENPP2","TBC1D10A","DNAI3","ZC3H7B","OLFML3","BEX1","GJA1","APOBEC3C","RANBP1","CALB2","TMEM184B")
  291. #text(subset(res1Anot$log2FoldChange,res1Anot$GeneSymbol %in% c("EGFLAM","CYP2E1","MCOLN2","KCNS3","ENPP2","TBC1D10A","DNAI3","ZC3H7B","OLFML3","BEX1","GJA1","APOBEC3C","RANBP1","CALB2","TMEM184B") ),-log10(subset(res1Anot$padj,res1Anot$GeneSymbol %in% c("EGFLAM","CYP2E1","MCOLN2","KCNS3","ENPP2","TBC1D10A","DNAI3","ZC3H7B","OLFML3","BEX1","GJA1","APOBEC3C","RANBP1","CALB2","TMEM184B") )), labels=topg, pos=4,cex=0.5, col="black")
  293. dev.off()

run_DESeq2_lncRNAs.R at commit c8d267e, no license · at the source

Overview

Authors: Nele Teichmann1, Santhilal Subhash2,3, Mario Giordano1, Ashraqat Ahmed1, Robert Geffers4, Madjid Samii1, Chandrasekhar Kanduri3, Amir Samii1,5, Souvik Kar1
  1. International Neuroscience Institute, Rudolf-Pichlmayr-Strasse 4, D-30625 Hannover, Germany
  2. Department of Biosciences and Bioengineering, Indian Institute of Technology Jammu, Jammu, Jammu, Kashmir India
  3. Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
  4. Genome Analytics Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
  5. Leibniz-Institute for Neurobiology, Magdeburg, Germany
Institutions: International Neuroscience Institute (Germany); Indian Institute of Technology Jammu (India); University of Gothenburg (Sweden); Helmholtz Centre for Infection Research (Germany); Leibniz Institute for Neurobiology (Germany)
Journal: Molecular biology reports, volume 53, issue 1, article 1273
Dates: received 26 April 2026; accepted 14 July 2026; published online 27 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1007/s11033-026-12429-y · PMID 42507076 · PMCID PMC13407589 · OpenAlex W7171428065
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population)
Methods: Statistics, Connectivity
Keywords: Vestibular schwannoma, Cystic vestibular schwannoma, Long non-coding RNAs, RNA sequencing, Differential gene expression, Biomarker development
MeSH: Neuroma, Acoustic*, RNA, Long Noncoding*, Biomarkers, Tumor, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Sequence Analysis, RNA, Transcriptome (* major topic)
Topic: Meningioma and schwannoma management (Epidemiology, Medicine), according to OpenAlex
Funding: Neurobionik Foundation Hannover
Citations: not cited yet (Europe PMC); 34 references in the paper
Notices: A correction to this paper has been published (42622948, from Europe PMC)

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

decodebiology/Vestibular_schwannoma_2025

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c8d267ec5419da9e9368a0d3e7ed88c167922eb9, 31 July 2026
Languages: R (5), Shell (5)
Size: 46 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ComplexHeatmap (4 files), circlize (3 files), ggplot2 (3 files), patchwork (2 files), tidyverse (2 files), data.table (1 file), DESeq2 (1 file), ggpubr (1 file), SAMtools (1 file), Subread (featureCounts) (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/decodebiology/Vestibular_schwannoma_2025

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1007/s11033-026-12429-y.

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;
  • 10 scripts, each with its path and the digest of its content;
  • 5 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 statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1007/s11033-026-12429-y.

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 2, 28 September 2026

  • Publisher: n/a → Springer Nature

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 keywords, 8 MeSH terms, 1 funder, 33 references, 1 integrity notice.

Cite

This paper

Teichmann, N., Subhash, S., Giordano, M., Ahmed, A., Geffers, R., Samii, M., Kanduri, C., Samii, A., & Kar, S. (2026). Transcriptome-wide profiling of cystic and solid vestibular schwannomas reveals candidate long non-coding RNA signatures. Molecular biology reports, 53(1), 1273. https://doi.org/10.1007/s11033-026-12429-y

BibTeX

@article{teichmann2026transcriptome,
author = {Teichmann, Nele and Subhash, Santhilal and Giordano, Mario and Ahmed, Ashraqat and Geffers, Robert and Samii, Madjid and Kanduri, Chandrasekhar and Samii, Amir and Kar, Souvik},
title = {{Transcriptome-wide profiling of cystic and solid vestibular schwannomas reveals candidate long non-coding RNA signatures}},
journal = {Molecular biology reports},
year = {2026},
month = jul,
volume = {53},
number = {1},
pages = {1273},
publisher = {Springer Nature},
issn = {0301-4851},
doi = {10.1007/s11033-026-12429-y},
url = {https://doi.org/10.1007/s11033-026-12429-y},
pmid = {42507076},
pmcid = {PMC13407589}
}

RIS

TY - JOUR
AU - Teichmann, Nele
AU - Subhash, Santhilal
AU - Giordano, Mario
AU - Ahmed, Ashraqat
AU - Geffers, Robert
AU - Samii, Madjid
AU - Kanduri, Chandrasekhar
AU - Samii, Amir
AU - Kar, Souvik
TI - Transcriptome-wide profiling of cystic and solid vestibular schwannomas reveals candidate long non-coding RNA signatures
T2 - Molecular biology reports
J2 - Mol Biol Rep
PY - 2026
DA - 2026/07/27
VL - 53
IS - 1
SP - 1273
SN - 0301-4851
PB - Springer Nature
DO - 10.1007/s11033-026-12429-y
UR - https://doi.org/10.1007/s11033-026-12429-y
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s11033-026-12429-y",
"type": "article-journal",
"title": "Transcriptome-wide profiling of cystic and solid vestibular schwannomas reveals candidate long non-coding RNA signatures",
"container-title": "Molecular biology reports",
"author": [
{
"family": "Teichmann",
"given": "Nele"
},
{
"family": "Subhash",
"given": "Santhilal"
},
{
"family": "Giordano",
"given": "Mario"
},
{
"family": "Ahmed",
"given": "Ashraqat"
},
{
"family": "Geffers",
"given": "Robert"
},
{
"family": "Samii",
"given": "Madjid"
},
{
"family": "Kanduri",
"given": "Chandrasekhar"
},
{
"family": "Samii",
"given": "Amir"
},
{
"family": "Kar",
"given": "Souvik"
}
],
"container-title-short": "Mol Biol Rep",
"volume": "53",
"issue": "1",
"page": "1273",
"DOI": "10.1007/s11033-026-12429-y",
"PMID": "42507076",
"PMCID": "PMC13407589",
"ISSN": "0301-4851",
"publisher": "Springer Nature",
"URL": "https://doi.org/10.1007/s11033-026-12429-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
27
]
]
}
}

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

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