Fission yeast time course

Fission yeast time course

Here we provide the code which was used to contruct the RangedSummarizedExperiment object of the fission experiment data package. The count matrix was provided by the first author of the publication:

Leong HS, Dawson K, Wirth C, Li Y, Connolly Y, Smith DL, Wilkinson CR, Miller CJ. A global non-coding RNA system modulates fission yeast protein levels in response to stress. Nat Commun 2014 May 23;5:3947. PMID: 24853205. GEO: GSE56761.

The following is quoted from the GEO series:

“Global transcription profiles of fission yeast wild type (WT) and atf21del strains over an osmotic stress time course following treatment with 1M sorbitol at 0, 15, 30, 60, 120 and 180 mins. Strand-specific single end sequencing of total RNA was performed in biological triplicates on the Applied Biosystems SOLiD 5500xl Genetic Analyzer System.”

“Sequencing reads were aligned to the fission yeast genome (PomBase database release 11) using SHRiMP2 aligner with default parameters. Total number of reads that can be aligned to the genome at exactly one locus per sample range from 7.5 to 20.1 millions. These uniquely mapped reads were used to identify stretches of unambiguous transcription. Reads that aligned to more than one locus (generally paralogous regions in the genome) were discarded. Adjacent unambiguous transcription regions with minimum peak height of two and located within 50 bases of each other were merged to yield an extensive transcription map of S. pombe. These regions were then positioned relative to known annotation and labelled according to the gene(s) they overlapped with using the Bioconductor package annmap.”

Object construction

The following code was used to read in the phenotypic data from GEO.

gse <- getGEO(filename="GSE56761_series_matrix.txt")
pdata <- pData(gse)[,grepl("characteristics",names(pData(gse)))]

The data.frame was cleaned by replacing long character strings with shorter ones, and turning the minute variable into a factor with the correctly ordered levels.

names(pdata) <- c("strain","treatment","time","replicate")
pdataclean <- data.frame(strain=ifelse(grepl("wild type",pdata$strain),"wt","mut"),
                         minute=sub("time  \\(min\\): (.*)","\\1",pdata$time),
                         replicate=paste0("r",sub("replicate: (.*)","\\1",pdata$replicate)),
pdataclean$id <- paste(pdataclean$strain,pdataclean$minute,pdataclean$replicate,sep="_")
pdataclean$strain <- relevel(pdataclean$strain, "wt")
pdataclean$minute <- factor(pdataclean$minute, levels=c("0","15","30","60","120","180"))

The rownames and colnames of the RangedSummarizedExperiment were confirmed to line up with the gene annotations and phenotypic data table.

stopifnot(all.equal(rownames(reads.GSE56761), as.character(gene.annotations$pombase_id)))
colnames(reads.GSE56761) <- tolower(colnames(reads.GSE56761))
stopifnot(all.equal(colnames(reads.GSE56761), pdataclean$id))
colnames(reads.GSE56761) <- rownames(pdataclean)
coldata <- DataFrame(pdataclean)

The gene annotation table was used to construct a GRanges object.

genes <- gene.annotations
rowranges <- GRanges(seqnames=genes$chromosome,
mcols(rowranges)$symbol <- as.character(mcols(rowranges)$symbol)
names(rowranges) <- genes$pombase_id

The Pubmed ID was used to generated a MIAME object to described the experiment.

metadata <- pmid2MIAME("24853205")
metadata@url <- ""

Finally the component parts were combined as a RangedSummarizedExperiment and saved as an RData file.

fission <- SummarizedExperiment(SimpleList(counts=reads.GSE56761),
save(fission, file="fission.RData")