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The H3K36me3 methyltransferase SETD2 contributes to PAF1C interactions with RNA Pol II and is required for neuronal differentiation.

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  1. [1] § Results › Reduced association of the PAF1 complex with the elongating RNA Pol II in the absence of SETD2 ↔ chromID/chromID_analysis.Rmd, lines 342–363 · score 0.81 · nTurbo, log2 FC, LFQ intensity, log2 fold change, TurboID, enriched proteins
  2. [2] § Methods › PolyA RNA-sequencing and differential gene expression analysis ↔ rnaseq/analysis/RNA-seq-analysis-DGE-GSEA.Rmd, lines 201–246 · score 0.76 · filterByExpr, glmTreat, edgeR, GSEA, rnaseq, model
  3. [3] § Methods › ChromID and label-free MS data acquisition and analysis ↔ chromID/chromID_analysis.Rmd, lines 19–57 · score 0.73 · MaxQuant, biotin incubation, bait, MS, Proteus, log2
  4. [4] § Methods › PolyA RNA-sequencing and differential gene expression analysis ↔ rnaseq/mapping_rnaseq_nfcore/rnaseq_nextflow_submission.sh, the whole file · a weak match · score 0.71 · gencode.vM35.annotation.gtf, rnaseq, Salmon, STAR, nf, trimgalore
  5. [5] § Results › Reduced association of the PAF1 complex with the elongating RNA Pol II in the absence of SETD2 ↔ chromID/chromID_analysis.Rmd, lines 265–303 · score 0.53 · proteins enriched, TurboID, Setd2 KO, NLS, SRI, WT

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The authors' code

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  1. ---
  2. title: "Ambrosi_ChromID_Analysis"
  3. author: "Ramon Pfaendler"
  4. date: "`r format(Sys.time(), '%d %B, %Y')`"
  5. output:
  6. html_document:
  7. theme: spacelab
  8. highlight: tango
  9. df_print: paged
  10. toc: true
  11. toc_float: true
  12. code_folding: "hide"
  13. fig_crop: false
  14. code_download: true
  15. editor_options:
  16. chunk_output_type: console
  17. ---
  19. ## Setup {.tabset .tabset-fade}
  22. ```{r setup, include=TRUE, warning=F, message=F}
  24. library(tidyr)
  25. library(tidyverse)
  26. # the knitr package is required to set an a different working directory to e.g. a different root where data is stored (while keeping the Rmd file stored in a different place simultaneously!)
  27. library(knitr)
  28. library(ggplot2)
  29. library(tidyverse)
  30. library(plotly)
  31. library(ggrepel)
  32. library(gridExtra)
  33. library(circlize)
  34. library(ComplexHeatmap)
  35. library(pheatmap)
  36. library(proteus)
  37. library(limma)
  39. devtools::install_github("bartongroup/Proteus", build_opts= c("--no-resave-data", "--no-manual"), build_vignettes=TRUE)
  41. # new function that creates a volcano plot more easily than just writing it every time
  42. volcano_rp <- function(data,comparison, timepoint, cellline,expnumber ){
  43. ggplot() + geom_point(data, mapping = aes(text = names,x = logFC, y = -log10(adj.P.Val), colour = significant)) +
  44. xlim(c(-12,12)) +geom_text_repel(data %>% filter(significant == "TRUE"), mapping = aes(y = -log10(adj.P.Val), x = logFC, label = names), cex = 2.5, colour = "darkblue", max.overlaps = 50, segment.linetype = 'dotted', segment.alpha = 0.6) +
  45. scale_color_manual(values = c("palegreen3","darkblue")) +
  46. ggtitle(paste(comparison," \n(Biotin Incubation = ",timepoint,")\n(",cellline,";", expnumber,")")) +
  47. xlab("enrichment log2(bait/control)") +
  48. ylab("-log10 adj.p-value")+annotate("text", x= -8.75, y = 8, label = "adj.p-val = 0.05", cex = 4) + theme_bw(base_size = 15)
  49. }
  52. # load the evidence.txt file - this is the output from MaxQuant
  53. # Change the path to the correct location :-)
  54. evi_sri<- proteus::readEvidenceFile("/Users/ramonpfaendler/Documents/UZH/BaubecLab/MS/rpx77_3/rpx77_3_evidence.txt")
  57. ```
  60. ## SRI-ChromID Data {.tabset .tabset-fade}
  61. ### Loading the data, playing around with some EDA
  64. ```{r}
  65. meta_sri <- as.data.frame(unique(evi_sri$experiment))
  66. colnames(meta_sri) <- "experiment"
  68. #create measure column (just repeat "intensity" for each sample, as intensities of the peptides are available in the evidence.txt)
  69. meta_sri$measure <- rep("Intensity",length(meta_sri$experiment))
  71. # create sample column ("evade" unnecessary characters in the front, in this case, first the rpx.. is removed, followed by the removal of numbers (with \\d )and the letter S)
  72. gsub("\\d\\d_S\\d\\d\\d\\d\\d\\d_","",unique(meta_sri$experiment)) %>% gsub("(TurboID_\\d).*","\\1",.) %>% gsub ("\\d\\d\\d\\d\\d\\d_\\d","",.) %>% gsub("rpx77_3_","",.) -> meta_sri$sample
  74. # create condition column, to do this, arrange the dataframe first (arrange by sample, to have it properly ordered!), to make it easier later!
  75. meta_sri %>% arrange(., sample) -> meta_sri
  76. meta_sri$condition[1:16] <- c(rep("WT - NLS-TurboID",4), rep("Setd2-KO - NLS-TurboID",4), rep("Setd2-KO - SRI-TurboID",4), rep("WT - SRI-TurboID",4))
  78. # add replicate number and column
  79. meta_sri %>% group_by(condition) %>% mutate(replicate = row_number()) -> meta_sri
  81. # concatenate strings to generate simpler sample ID than the one coming from FGCZ
  82. meta_sri %>% unite(., sample_new,condition, replicate, sep = "_") %>% .$sample_new -> sample_new
  83. meta_sri$sample <- sample_new
  85. ```
  88. ## Creation of peptide and protein dataset using all 16 samples {.tabset .tabset-fade}
  89. ### Create a peptide and a protein datast by merging evidence and meta data
  91. This section is based on the proteus package. Check also the other tab that uses similar visualisations of the same data.
  93. ```{r}
  94. # merge the files
  95. pepdat_sri <- proteus::makePeptideTable(evi_sri, meta_sri)
  98. # plot peptide counts per condition (of non-zero peptide intensities per sample)
  99. proteus::plotCount(pepdat_sri)
  102. prodat_sri <- proteus::makeProteinTable(pepdat_sri)
  104. proteus::plotCount(prodat_sri)
  106. # on the protein level, the samples seem to be quite similar to each other
  108. # next one can normalize data: this way, the data is normalized the way that the median
  109. # intensity in all samples is the same
  110. prodat.med_sri <- proteus::normalizeData(prodat_sri)
  113. # plot data as violin plots prior normalisation
  114. proteus::plotSampleDistributions(prodat_sri, title="Not normalised", fill="condition", method="violin")
  115. proteus::plotSampleDistributions(prodat.med_sri, title="Median normalised", fill="condition", method="violin")
  117. #with median normalisation, all samples seem to be in the same range which, I think, makes it plausible to keep all of them.
  119. ```
  121. ### Plotting independently of Proteus Package
  123. In this section, I tried to visualise some of the qc plots independently of the proteus package. This gives more flexibility, for example to render visualiations to log2 space or other properties. (log2 space might be interesting later to check if proteins only present in one condition are found on the lower or higher level of the intensity distribution.)
  126. ```{r}
  128. # to create violin plots, one needs to reshape the data into long format
  129. prodat_sri$tab %>% reshape2::melt(., id.vars = NULL) -> prodat_reshaped
  131. # add condition column, which can later be used for visualisation purposes
  132. prodat_reshaped$condition <- prodat_reshaped$Var2 %>% as.vector(.) %>% gsub(".{2}$", "",.)
  133. prodat_reshaped$condition %>% unique(.) # sanity check
  136. ggplot(prodat_reshaped, aes(x = Var2, y = log2(value), fill = condition )) + geom_violin() +theme_light() + theme(axis.text.x = element_text(angle = 90, hjust = 1,
  137. vjust = 0.5, size =8)) + labs(fill = "Condition", x = "Sample", y = "log2(LFQ_intensity)")+ ggtitle("Non-normalised LFQ Intensitites") + geom_boxplot(width = 0.1, outlier.shape = NA, show.legend = F)+ scale_fill_brewer(palette = "Pastel2")
  140. # to create violin plots, one needs to reshape the data into long format
  141. prodat.med_sri$tab %>% reshape2::melt(., id.vars = NULL) -> prodat_med_reshaped
  143. # add condition column, which can later be used for visualisation purposes
  144. prodat_med_reshaped$condition <- prodat_reshaped$Var2 %>% as.vector(.) %>% gsub(".{2}$", "",.)
  145. prodat_med_reshaped$condition %>% unique(.) # sanity check
  148. ggplot(prodat_med_reshaped, aes(x = Var2, y = log2(value), fill = condition )) + geom_violin() +theme_light() + theme(axis.text.x = element_text(angle = 90, hjust = 1,
  149. vjust = 0.5, size =8)) + labs(fill = "Condition", x = "Sample", y = "log2(LFQ_intensity)") + ggtitle("Median-normalised LFQ Intensitites") + geom_boxplot(width = 0.1, outlier.shape = NA, show.legend = F) + scale_fill_brewer(palette = "Pastel2")
  153. ```
  155. ## Differential Enrichment Calculations {.tabset .tabset-fade}
  156. In this section, different conditions are compared to each other. The proteus package provides limma-based enrichment calculations. Here, it is important to note, that proteins that are only found in one group and are entirely missing in the control group (or vice versa) will not show a logFC or p-value, since the linear model cannot find a value to compare it to! To do this, imputation-based methods, such as Perseus, would be needed.
  157. For the individual contrasts, we've always used an FDR of 0.05 as significance-threshold. (The FDR is based on a Benjamini-Hochberg adjusted p-value.)
  160. ### nTurboID vs. SETD2KO nTurboID
  161. ```{r}
  162. meta_sri$condition %>% unique()
  164. ctrls_rpx77 <- proteus::limmaDE(prodat.med_sri, conditions=c( "WT - NLS-TurboID","Setd2-KO - NLS-TurboID" ), sig.level = 0.05)
  165. #20 NAs
  168. ctrls_rpx77$names <- gsub('sp.*\\|','',ctrls_rpx77$protein)
  169. ctrls_rpx77$names <- gsub('_MOUSE','',ctrls_rpx77$names)
  171. #assign uniprot id's before rendering the names of the majority protein ids
  172. names_up <- (gsub('sp.|*\\*','',ctrls_rpx77$protein))
  173. ctrls_rpx77$UniprotID <- str_extract(names_up,"\\w+|")
  175. #use new function of volcano plots specified earlier in the script
  176. volcano_rp(ctrls_rpx77, comparison = paste(unique(meta_sri$condition)[1], "vs.", unique(meta_sri$condition)[2]), timepoint = "12 hours", cellline = "mNPCs", expnumber = "RPX77") ->p_ctrls_rpx77
  177. p_ctrls_rpx77
  179. # grid.arrange(p_sri_rpx77, p_sri_setd2ko_rpx77)
  180. library(plotly)
  182. # to plot the name of the protein later in the plotly plot, specify an additional aes variable (here: text = Majority.protein.IDs), this one can be used later in the ggplotly command by using tooltip!
  184. ggplotly(p_ctrls_rpx77, tooltip = c("text","x","y"))
  187. ```
  190. ## ChromID analysis based on merged controls {.tabset .tabset-fade}
  191. ### Updated meta-data to unify nTurbo controls in one condition
  192. Given that the the control conditions in both backgrounds did not display any differential enrichment, we subsequently merged the control conditions from the WT and SETD2 -/- genetic backgrounds and perform all the DE analysis compared to these merged control samples.
  194. ```{r}
  196. meta_sri2 <- meta_sri
  198. meta_sri2$condition[1:16] <- c(rep("NLS-TurboID",8), rep("Setd2-KO - SRI-TurboID",4), rep("WT - SRI-TurboID",4))
  199. #updated conditions column, sample column remains the same to see which one's which later on!
  201. # merge the files
  202. pepdat_sri2 <- proteus::makePeptideTable(evi_sri, meta_sri2)
  203. prodat_sri2 <- proteus::makeProteinTable(pepdat_sri2)
  205. prodat.med_sri2 <- proteus::normalizeData(prodat_sri2)
  207. ```
  209. ### DE - SRI WT vs. nTurboID (HA36CB1, RPX77)
  210. ```{r}
  211. meta_sri2$condition
  213. #make sure to use the normalised version here
  214. sri_rpx77_2 <- proteus::limmaDE(prodat.med_sri2, conditions=c( "WT - SRI-TurboID","NLS-TurboID" ), sig.level = 0.05)
  215. # 32 NAs; 7 less than in non-grouped comparison
  217. sri_rpx77_2$names <- gsub('sp.*\\|','',sri_rpx77_2$protein)
  218. sri_rpx77_2$names <- gsub('_MOUSE','',sri_rpx77_2$names)
  220. #assign uniprot id's before rendering the names of the majority protein ids
  221. # names_up <- (gsub('sp.|*\\*','',sri_rpx77_2$protein))
  222. sri_rpx77_2$UniprotID <- str_extract(names_up,"\\w+|")
  224. #use new function of volcano plots specified earlier in the script
  225. volcano_rp(sri_rpx77_2, comparison = paste(unique(meta_sri2$condition)[3], "vs.", unique(meta_sri$condition)[1]), timepoint = "12 hours", cellline = "mNPCs", expnumber = "RPX77") ->p_sri_rpx77_2
  226. p_sri_rpx77_2
  229. # to plot the name of the protein later in the plotly plot, specify an additional aes variable (here: text = Majority.protein.IDs), this one can be used later in the ggplotly command by using tooltip!
  230. ggplotly(p_sri_rpx77_2, tooltip = c("text","x","y"))
  235. ```
  237. ### DE - SRI WT vs. nTurboID (HA36CB1 SETD2KO, RPX77)
  238. ```{r}
  239. sri_setd2ko_rpx77_2 <- proteus::limmaDE(prodat.med_sri2, conditions=c( "Setd2-KO - SRI-TurboID","NLS-TurboID" ), sig.level = 0.05)
  240. #22 NAs; 7 less than in non-grouped comparison
  242. sri_setd2ko_rpx77_2$names <- gsub('sp.*\\|','',sri_setd2ko_rpx77_2$protein)
  243. sri_setd2ko_rpx77_2$names <- gsub('_MOUSE','',sri_setd2ko_rpx77_2$names)
  245. #assign uniprot id's before rendering the names of the majority protein ids
  246. sri_setd2ko_rpx77_2$UniprotID <- str_extract(names_up,"\\w+|")
  249. #use new function of volcano plots specified earlier in the script
  250. volcano_rp(sri_setd2ko_rpx77_2, comparison = paste(unique(meta_sri2$condition)[2], "vs.", unique(meta_sri2$condition)[1]), timepoint = "12 hours", cellline = "mNPCs", expnumber = "RPX77") ->p_sri_setd2ko_rpx77_2
  251. p_sri_setd2ko_rpx77_2
  253. # grid.arrange(p_sri_rpx77, p_sri_setd2ko_rpx77)
  254. library(plotly)
  256. # to plot the name of the protein later in the plotly plot, specify an additional aes variable (here: text = Majority.protein.IDs), this one can be used later in the ggplotly command by using tooltip!
  258. ggplotly(p_sri_setd2ko_rpx77_2, tooltip = c("text","x","y"))
  263. ```
  265. ### Proteins enriched compared to grouped control
  266. In this section, proteins that were significantly enriched (or depleted for SRI vs SRI comparision) are displayed using the median normalised LFQ values.
  267. ```{r,fig.height = 9, fig.width = 11, fig.align = "center"}
  269. c(sri_rpx77_2 %>% filter(.$significant ==T & .$logFC >0) %>% .$protein %>% as.character()) %>% unique(.) -> sig_HA36CB1_2
  271. c(sri_setd2ko_rpx77_2 %>% filter(.$significant ==T & .$logFC >0) %>% .$protein %>% as.character()) %>% unique(.) -> sig_SETD2KO_2
  274. top_anno <- HeatmapAnnotation(condition = colnames(prodat.med_sri$tab) %>% map(function(x, y) y[str_detect(x, y)], c("Setd2-KO - NLS-TurboID","Setd2-KO - SRI-TurboID","WT - SRI-TurboID")
  275. ) %>% lapply(., function(x) if(identical(x, character(0))) "WT - NLS-TurboID" else x) %>% lapply(., function(x) x[1]) %>% unlist(.) , col = list(
  276. condition = structure(names= colnames(prodat.med_sri$tab) %>% map(function(x, y) y[str_detect(x, y)], c("Setd2-KO - NLS-TurboID","Setd2-KO - SRI-TurboID","WT - SRI-TurboID")
  277. ) %>% lapply(., function(x) if(identical(x, character(0))) "WT - NLS-TurboID" else x) %>% lapply(., function(x) x[1]) %>% unlist(.) %>% unique(),c('#b3e2cd','#fdcdac','#e6f5c9','#f4cae4'))))
  279. #c(setdiff(sig_HA36CB1_2, sig_SETD2KO_2),setdiff(sig_SETD2KO_2, sig_HA36CB1_2)) %>% unique(.)
  283. as.matrix(prodat.med_sri2$tab %>% log2(.) %>% as.data.frame(.)%>% rownames_to_column('protein') %>% filter(.$protein %in% sig_HA36CB1_2) %>% column_to_rownames('protein')) -> mat_ha36_2
  284. as.matrix(prodat.med_sri2$tab %>% log2(.) %>% as.data.frame(.)%>% rownames_to_column('protein') %>% filter(.$protein %in% sig_SETD2KO_2) %>% column_to_rownames('protein')) -> mat_setd2ko_2
  287. #top_anno2 <- HeatmapAnnotation(condition = colnames(prodat.med_sri$tab) %>% map(function(x, y) y[str_detect(x, y)], c("SETD2KO_nTurboID","SETD2KO_SRI-WT_TurboID","SRI-WT_TurboID")) %>% lapply(., function(x) if(identical(x, character(0))) "nTurboID" else x) %>% lapply(., function(x) x[1]) %>% unlist(.) , col = list( condition = structure(names= colnames(prodat.med_sri$tab) %>% map(function(x, y) y[str_detect(x, y)], c("SETD2KO_nTurboID","SETD2KO_SRI-WT_TurboID","SRI-WT_TurboID")) %>% lapply(., function(x) if(identical(x, character(0))) "nTurboID" else x) %>% lapply(., function(x) x[1]) %>% unlist(.) %>% unique(),c("#B3E2CD", "#FDCDAC", "#CBD5E8", "#F4CAE4"))))
  289. rownames(mat_ha36_2) <- gsub('.*\\|','',rownames(mat_ha36_2)) %>% gsub('_MOUSE','',.)
  291. Heatmap(mat_ha36_2,col = colorRamp2((c(12,22,32)), c("white", "lightskyblue1", "firebrick")), show_column_dend = T, name = paste("log2(fc)\nn Proteins =",length(sig_HA36CB1_2), ""),row_names_side = "left", row_dend_side = "right", row_names_gp = gpar(fontsize = 6),gap = unit(3, "mm"), column_names_gp = gpar(fontsize = 6),heatmap_legend_param = list(title = "log2(LFQ intensity)"), cluster_columns = T, na_col = "white", top_annotation = top_anno, cluster_rows = T, width = ncol(mat_ha36_2)*unit(5,"mm"), height = nrow(mat_ha36_2)*unit(2.5,"mm"), row_title = "Proteins enriched in HA36CB1", row_title_gp = gpar(fontsize = 10)) ->h_ha36_2
  293. rownames(mat_setd2ko_2) <- gsub('.*\\|','',rownames(mat_setd2ko_2)) %>% gsub('_MOUSE','',.)
  295. Heatmap(mat_setd2ko_2,col = colorRamp2((c(12,22,32)), c("white", "lightskyblue1", "firebrick")), show_column_dend = T, name = paste("log2(fc)\nn Proteins =",length(sig_SETD2KO_2), ""),row_names_side = "left", row_dend_side = "right", row_names_gp = gpar(fontsize = 6),gap = unit(3, "mm"), column_names_gp = gpar(fontsize = 6),heatmap_legend_param = list(title = "log2(LFQ intensity)"), cluster_columns = T, na_col = "white", top_annotation = top_anno, cluster_rows = T, width = ncol(mat_setd2ko_2)*unit(5,"mm"), height = nrow(mat_setd2ko_2)*unit(2.5,"mm"), row_title = "Proteins enriched in SETD2KO", row_title_gp = gpar(fontsize = 10)) ->h_setd2ko_2
  299. h_list_2 = h_setd2ko_2 %v% h_ha36_2
  300. draw(h_list_2, merge_legend = T)
  303. ```
  306. ### Combined Heatmaps
  307. The same as above, just with an additional row annotation to plot the heatmap only once.
  308. ```{r,fig.height = 9, fig.width = 11, fig.align = "center"}
  309. sig_2 <- c(sig_HA36CB1_2,sig_SETD2KO_2) %>% unique(.)
  312. as.matrix(prodat.med_sri2$tab %>% log2(.) %>% as.data.frame(.)%>% rownames_to_column('protein') %>% filter(.$protein %in% sig_2) %>% column_to_rownames('protein')) -> another_mat
  314. Heatmap(another_mat,col = colorRamp2((c(12,22,32)), c("white", "lightskyblue1", "firebrick")), show_column_dend = T, name = paste("log2(fc)\nn Proteins =",length(sig_2), ""),row_names_side = "left", row_dend_side = "right", row_names_gp = gpar(fontsize = 3.5),gap = unit(2, "mm"), column_names_gp = gpar(fontsize = 6),heatmap_legend_param = list(title = paste("log2(LFQ intensity)\nn Proteins enriched in HA36CB1 =",length(sig_2))), cluster_columns = T, na_col = "white", top_annotation = top_anno, cluster_rows = T, width = ncol(another_mat)*unit(2.5,"mm"), height = nrow(another_mat)*unit(1,"mm"), row_title = "Proteins enriched in either foreground", row_title_gp = gpar(fontsize = 10)) ->h_another
  318. draw(h_another+ rowAnnotation(HA36CB1 = ifelse(sig_2 %in% sig_HA36CB1_2 & !(sig_2 %in% sig_SETD2KO_2), "yes", "no"), col = list(HA36CB1 = c("yes" = 'grey50', "no"= "white"))) +rowAnnotation(SETD2KO = ifelse(sig_2 %in% sig_SETD2KO_2 & !(sig_2 %in% sig_HA36CB1_2), "yes", "no"), col = list(SETD2KO = c("yes" = 'grey80', "no"= "white"))) + rowAnnotation(both = ifelse(sig_2 %in% sig_HA36CB1_2 & (sig_2 %in% sig_SETD2KO_2), "yes", "no"), col = list(both = c("yes" = 'darkgreen', "no"= "white"))), merge_legend = T)
  322. # try to get rowannotation with all info of where proteins enriched in one
  324. anno_df <- as.data.frame(ifelse(sig_2 %in% sig_HA36CB1_2 & !(sig_2 %in% sig_SETD2KO_2), "HA36CB1", NA))
  325. colnames(anno_df)[1] <- "ha36cb1"
  326. anno_df$setd2ko <- ifelse(sig_2 %in% sig_SETD2KO_2 & !(sig_2 %in% sig_HA36CB1_2), "SETD2KO", NA)
  327. anno_df$both <- ifelse(sig_2 %in% sig_HA36CB1_2 & (sig_2 %in% sig_SETD2KO_2), "both", NA)
  329. # weird workaround that seems to work later on
  330. within(anno_df, ha36cb1 <- ifelse(is.na(ha36cb1), setd2ko,ha36cb1)) ->anno_df2
  331. within(anno_df2, ha36cb1 <- ifelse(is.na(ha36cb1), both,ha36cb1)) ->anno_df3
  332. anno_df3$ha36cb1[anno_df3$ha36cb1 == 1] <- "HA36CB1"
  335. draw(h_another + rowAnnotation(Enrichment = (anno_df3$ha36cb1) ,col = list(Enrichment = c("HA36CB1" = "#7FC97F", "SETD2KO" = "#BEAED4", "both" = "#FDC086"))), merge_legend = T)
  340. ```
  342. ### Heatmaps of log2-FC
  343. Heatmap visualisation of all significanly enriched proteins in log2 fold-change world.
  345. ```{r,fig.height = 9, fig.width = 11, fig.align = "center"}
  348. sri_rpx77_2 %>% filter(., .$protein %in% sig_2) %>% column_to_rownames('names') ->df_1
  349. sri_setd2ko_rpx77_2 %>% filter(., .$protein %in% sig_2) %>% column_to_rownames('names') -> df_2
  351. cbind(df_1, df_2$logFC) -> comb_df
  354. colnames(comb_df)
  355. colnames(comb_df)[2] = "log2FC_HA36CB1_SRI-WT-TurboID"
  356. colnames(comb_df)[14] = "log2FC_SETD2KO_SRI-WT-TurboID"
  359. Heatmap(as.matrix(comb_df[,c(2,14)]), col = colorRamp2(c(-4, 0, 4), c("#2166ac","#f7f7f7","#b2182b")), show_column_dend = FALSE, name = paste("log2(fc)\nn Proteins =",length(comb_df$protein), ""),row_names_side = "left", row_dend_side = "right", row_dend_width = unit(4, "cm"), row_names_gp = gpar(fontsize = 4), clustering_method_rows = "ward.D", gap = unit(2, "mm"), column_names_gp = gpar(fontsize = 4), heatmap_legend_param = list(title = paste("log2(fc(LFQ intensity))\nn Proteins =",length(comb_df$protein)), title_gp = gpar(fontsize = 5), labels_gp = gpar(fontsize = 5)), width = ncol(comb_df)*unit(1,"mm"), height = nrow(comb_df)*unit(1.4,"mm"), row_title = "log2(FC) vs. nTurbo-grouped controls", row_title_gp = gpar(fontsize = 10))
  363. ```
  365. ## Session Info
  367. ```{r}
  368. sessionInfo()$R.version
  369. installed.packages()[names(sessionInfo()$otherPkgs), "Version"]
  370. ```

chromID_analysis.Rmd at commit 9d6f50c, no license · at the source

Overview

Authors: Christina Ambrosi1,2, Ramon Pfaendler1,2,3, Kristeli Eleftheriou4, Stefan Butz1,2, Davide Recchia1,2,4, Xue Bao4, Richard Cardoso da Silva4, Niklas Kupfer4, Ilse M Lagerwaard4, Hanneke Vlaming4, Nina Schmolka1,5, Vivek Bhardwaj4, Tuncay Baubec1,4
ORCID iDs: Ramon Pfaendler, Kristeli Eleftheriou, Davide Recchia, Xue Bao, Richard Cardoso da Silva, Niklas Kupfer, Ilse M Lagerwaard, Hanneke Vlaming, Vivek Bhardwaj, Tuncay Baubec
  1. Department of Molecular Mechanisms of Disease, University of Zurich, Zurich, Switzerland
  2. Life Science Zurich Graduate School, University of Zurich and ETH Zurich, Zurich, Switzerland
  3. Present Address: Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
  4. Genome Biology and Epigenetics, Institute of Biodynamics and Biocomplexity, Department of Biology, Utrecht University, Utrecht, The Netherlands
  5. Present Address: Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
Institutions: University of Zurich (Switzerland); ETH Zurich (Switzerland); Life Science Zurich (Switzerland); Institute for Molecular Systems Biology (Switzerland); Utrecht University (Netherlands)
Journal: The EMBO journal, volume 45, issue 10, pages 3430-3443
Dates: received 27 February 2025; accepted 11 March 2026; published online 10 April 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44318-026-00768-2 · PMID 41963556 · PMCID PMC13187324 · OpenAlex W7153067057
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Chromatin, H3K36me3, SETD2, Transcription & Genomics, Development
MeSH: Cell Differentiation*, Histone-Lysine N-Methyltransferase*, Neurons*, Nuclear Proteins*, RNA Polymerase II*, Animals, Carrier Proteins, Histones, Mice, Mouse Embryonic Stem Cells (* major topic)
Topic: RNA modifications and cancer (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: EC | Horizon Europe | Excellent Science | HORIZON EUROPE European Research Council (ERC) (865094); Swiss National Science Foundation (183722, 180354, 186012)
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

Chromatin modifications are essential for mammalian development, and their aberrant deposition is associated with human disease. While the mechanisms that deposit and remove histone modifications have been largely elucidated, their roles in regulating gene activity during cellular differentiation are yet to be fully understood. Here, we performed a deletion screen to identify stage-specific requirements of chromatin regulators during neuronal differentiation of mouse embryonic stem cells. We show that the H3K36me3 methyltransferase SETD2 is required for the establishment of neuronal gene expression during late stages of differentiation but is dispensable in mature neurons. Notably, this function is independent of its histone methyltransferase activity. Instead, SETD2 promotes interactions between the PAF1 complex and elongating RNA Pol II, suggesting a role in supporting efficient transcription of neuronal genes.

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 5 matches between paragraphs and lines of code.

FelixKrueger/TrimGalore

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c6528e54512e0e388a392d36475291bcbf0eb0dd, 27 June 2026
Languages: Rust (24), TypeScript (2), Shell (2), Python (1)
Size: 267 files, 29 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file, CITATION.cff, environment (Dockerfile), tests, continuous integration, documentation
Tools: Matplotlib (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
31 files
At the source: github.com/FelixKrueger/TrimGalore

BaubecLab/Setd2

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 9d6f50ce61cf1d521f4f2889a56d8e3a2a03192f, 17 September 2025
Languages: Shell (3), R (2)
Size: 11 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (chip_seq/mapping/conda_env_ngs.yaml, rnaseq/mapping_rnaseq_nfcore/conda_env_nextflow.yaml), 2 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: ggplot2 (2 files), limma (2 files), tidyverse (2 files), circlize (1 file), clusterProfiler (1 file), ComplexHeatmap (1 file), deepTools (1 file), edgeR (1 file), Nextflow (1 file), pheatmap (1 file), Plotly (1 file), reshape2 (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
6 files
At the source: github.com/BaubecLab/Setd2

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

Tracing map

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  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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

High-throughput sequencing data obtained from ChIP-seq, ATAC-seq, RNA-seq, and single-cell RNA-seq studies were deposited to NCBI Gene Expression Omnibus under the following accessions: GSE278955 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE278955) (RNA-seq), GSE278956 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE278956) (ChIP-seq), GSE278957 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE278957) (ATAC-seq), GSE321694 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE321694) (scRNA-seq). Scripts used to analyze genomics and proteomics data were deposited to https://github.com/BaubecLab/Setd2.

The source data of this paper are collected in the following database record: biostudies:S-SCDT-10_1038-S44318-026-00768-2 (https://www.ebi.ac.uk/biostudies/sourcedata/studies/S-SCDT-10_1038-S44318-026-00768-2).

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 10 MeSH terms, 2 funders, 78 references.

Cite

This paper

Ambrosi, C., Pfaendler, R., Eleftheriou, K., Butz, S., Recchia, D., Bao, X., Cardoso da Silva, R., Kupfer, N., Lagerwaard, I. M., Vlaming, H., Schmolka, N., Bhardwaj, V., & Baubec, T. (2026). The H3K36me3 methyltransferase SETD2 contributes to PAF1C interactions with RNA Pol II and is required for neuronal differentiation. The EMBO journal, 45(10), 3430-3443. https://doi.org/10.1038/s44318-026-00768-2

BibTeX

@article{ambrosi2026h3k36me3,
author = {Ambrosi, Christina and Pfaendler, Ramon and Eleftheriou, Kristeli and Butz, Stefan and Recchia, Davide and Bao, Xue and Cardoso da Silva, Richard and Kupfer, Niklas and Lagerwaard, Ilse M and Vlaming, Hanneke and Schmolka, Nina and Bhardwaj, Vivek and Baubec, Tuncay},
title = {{The H3K36me3 methyltransferase SETD2 contributes to PAF1C interactions with RNA Pol II and is required for neuronal differentiation}},
journal = {The EMBO journal},
year = {2026},
month = apr,
volume = {45},
number = {10},
pages = {3430--3443},
publisher = {Nature Publishing Group},
issn = {0261-4189},
doi = {10.1038/s44318-026-00768-2},
url = {https://doi.org/10.1038/s44318-026-00768-2},
pmid = {41963556},
pmcid = {PMC13187324}
}

RIS

TY - JOUR
AU - Ambrosi, Christina
AU - Pfaendler, Ramon
AU - Eleftheriou, Kristeli
AU - Butz, Stefan
AU - Recchia, Davide
AU - Bao, Xue
AU - Cardoso da Silva, Richard
AU - Kupfer, Niklas
AU - Lagerwaard, Ilse M
AU - Vlaming, Hanneke
AU - Schmolka, Nina
AU - Bhardwaj, Vivek
AU - Baubec, Tuncay
TI - The H3K36me3 methyltransferase SETD2 contributes to PAF1C interactions with RNA Pol II and is required for neuronal differentiation
T2 - The EMBO journal
J2 - EMBO J
PY - 2026
DA - 2026/04/10
VL - 45
IS - 10
SP - 3430
EP - 3443
SN - 0261-4189
PB - Nature Publishing Group
DO - 10.1038/s44318-026-00768-2
UR - https://doi.org/10.1038/s44318-026-00768-2
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s44318-026-00768-2",
"type": "article-journal",
"title": "The H3K36me3 methyltransferase SETD2 contributes to PAF1C interactions with RNA Pol II and is required for neuronal differentiation",
"container-title": "The EMBO journal",
"author": [
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"family": "Ambrosi",
"given": "Christina"
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{
"family": "Eleftheriou",
"given": "Kristeli"
},
{
"family": "Butz",
"given": "Stefan"
},
{
"family": "Recchia",
"given": "Davide"
},
{
"family": "Bao",
"given": "Xue"
},
{
"family": "Cardoso da Silva",
"given": "Richard"
},
{
"family": "Kupfer",
"given": "Niklas"
},
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"given": "Ilse M"
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}
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"PMCID": "PMC13187324",
"ISSN": "0261-4189",
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"language": "en",
"issued": {
"date-parts": [
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}
}

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