The identification of genes that are significantly up-regulated or down-regulated using RNA-Seq and Ribo-Seq data can be used to differentiate between genes that are likely to be regulated at the level of transcription (both RNA-Seq and Ribo-Seq show the same tendency) from those that are regulated primarily at the level of translation (significantly up-regulated or down-regulated by Ribo-Seq but not RNA-Seq data), or that undergo post-transcriptional buffering of gene expression (only significant by RNA-Seq) . In order to perform differential gene expressoin (DGE) analysis for each experiment we subsampled the CDS table of counts so as to have approximately the same number of mapped reads in each of the samples. We then normalized the data using the Trimmed Mean of M-values (TMM) algorithm from the R/Bioconductor package edgeR . Subsequently, we used the limma-voom method to determine which genes showed significant changes in abundance in stress conditions , separately for RNA-Seq and Ribo-Seq data. Significantly up-regulated or down-regulated genes were those with adjusted p-value lower than 0.05 and logdosFC greater than one standard deviation (SD) of the log2FC distribution for the corresponding data. The SD values were as follows: Scer.aa- Ribo-Seq: 0.99 and RNA-Seq: 0.87; Scer. Oxi Ribo-Seq: 1.53 and RNA-Seq: 1.47; Spom.N- Ribo-Seq: 1.38 and RNA-Seq: 1.01.
Gene ontology label enrichment
We calculated the enrichment in Gene Ontology (GO) terms of the Biological Process category in different subsets of S. pombe genes that showed specific regulatory patterns. We selected representative terms that were significantly over-represented in the set of interest with FDR < 0.01. For this we used the AnGeLi webserver application from the Bahler Lab at University College London (
Way to obtain data and you can material
Extra file 1 includes additional tables and you will figures stated throughout the text message. The newest datasets supporting the results for the blog post appear in the Figshare repository [
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