Transcriptome of Escherichia coli K-12 (MG1655) at stationary phase in minimal medium

Gene expression analysis provides a lens into the cellular processes dominant in a cell at a particular point in time. Such understanding helps guide further experiments as well as providing the bedrock on which theoretical underpinnings of cellular processes emanates. In essence, gene expression analysis tells us the genes implicated in particular cellular processes and is the starting point for in-depth mechanistic studies. Two approaches operating at different levels of biological complexities are available for probing gene expression analysis, one of which is proteomics, the other is RNA-seq based transcriptome analysis. While proteomics workflow has been automated significantly over the past decade with the advent of liquid chromatography mass spectrometry, the dataset obtained pales in comparison to that available from RNA sequencing (RNA-seq). Specifically, probing the transcriptome of cells using contemporary next-generation high throughput sequencing typically generates millions of reads, which when aligned to a reference genome of the same species, tells a quantitative story of the relative expression levels of different genes in the organism. Hence, RNA-seq is the dominant technology utilized for gaining an understanding of the differential expression of genes at the global level with the assumption that mRNA abundance correlates linearly with protein abundance, hence, providing a readout of gene expression levels. This work entails the processing of a RNA-seq dataset of <i>Escherichia coli</i> K-12 (MG1655) during stationary growth in liquid minimal medium (Array Express accession number: E-GEOD-77325) using an in-house MATLAB software. A total of 3.5 million reads was processed to yield the final transcriptome dataset, which is catalogued in an Excel table detailing the gene abbreviation, description of gene function, gene sequence, and expression count. The last variable, which signifies expression level of a gene, is calculated based on the alignment of a RNA-seq read with a corresponding gene sequence. The software does not count reads that include part of the 5’ UTR of a gene. Overall, the top ten highest expressed genes of <i>E. coli</i> K-12 during stationary phase growth were ompF, fliC, ompA, fusA, tufA, ompC, metE, rpsA, ompT, and gapA. Such a catalogue of highly expressed genes speaks of the importance of outer membrane proteins, translation related proteins, and glyceraldehyde-3-phosphate dehydrogenase A in driving cellular processes during stationary phase. Overall, the dataset should provide a good reference for researchers interested in the gene expression patterns of <i>E. coli</i> K-12 during stationary phase growth, as well as providing a starting point for further investigation of differences in gene expression patterns of <i>E. coli</i> between different growth phases.<br><br>