# Transcriptome-Wide Analysis of Stationary Phase Small ncRNAs in _E. coli_
## 3 Discussion  
Previous whole transcriptome high-throughput sequencing studies gave rise to a plethora of new data and enabled mapping of several previously uncharacterized RNA species. Several of these newly identified transcripts have been shown to play regulatory roles governing gene expression, with versatile mechanisms affecting post-transcriptional and translational events in all three domains of life. In bacteria, the ever-increasing repertoire of sRNAs positions these short transcripts in complex networks regulating virtually all bacterial processes, including stationary phase, nutritional stress, and virulence [39]. Our deep sequencing analysis of the small transcriptome of _E. coli_ in exponential and stationary phases added to this repertoire of bacterial sRNAs, identifying several small ncRNAs differentially expressed during the stationary phase. These stationary phase-specific RNA molecules identified herein are prime candidates for so far unknown sRNAs potentially governing stationary phase regulatory networks. Based on these findings, subsequent dedicated experimental work is required to unravel whether or not these sRNA candidates indeed represent functional ribo-regulators of stationary phase _E. coli_ and/or persister cell formation.  
Prediction of processing products: Several existing biocomputational tools allow the prediction and identification of RNA processing products from sequencing data. Our previously established APART pipeline [40] allows the identification of RNA processing products  
Figure 5: Overexpression of _sRNA_35_ upregulates _rpoS_ expression. (**a**) Schematic representation of constructed plasmids for _sRNA_35_ (top) and control (bottom) overexpression. P\({}_{\text{lac}}\) = lac operon promoter, inducible by IPTG. rrnB T1 and T7 TE are terminator sequences. (**b**) Northern blot analysis of _sRNA_35_ (intergenic sRNA). E = exponential phase, S = stationary phase, OE = overexpression. Predicted sizes are displayed on the right in nt (nucleotides). (**c**) RT-qPCR mean and SEM for _rpoS_ expression under different conditions (two biological replicates each). E = exponential phase, S = stationary phase, OE = overexpression. log\({}_{2}\) fold change is reported for all conditions in comparison to exponential phase condition.  
through the detection of sharp increases and decreases in read coverages (Figure 6a). This identification strategy might lead to the prediction of RNA species that are not supported by existing reads and overlook existing processing products. STARPA uses Flaimapper [38] for processing product annotation, which overcomes the mentioned limitations. However, in Flaimapper, peak detection on 5\({}^{\prime}\) and 3\({}^{\prime}\) end densities allows the mapping of processing products (Figure 6b). Nevertheless, by this approach, overlapping processing products can be overlooked. To overcome this limitation, STARPA segregates reads by size first and then employs Flaimapper for peak identification (Figure 6c). Furthermore, because Flaimapper is only compatible with SE (single-end) reads, STARPA converts PE (paired-end) reads to pseudo-SE reads (Figure 1b). This approach allows STARPA to be compatible with both SE and PE reads, reducing the complexity of the pipeline by employing the same downstream workflow. Furthermore, within the quantification step of STARPA, several statistics can be collected for predicted sRNAs, such as relative coverage, coverage at single position level, consensus sequence, quality of consensus sequence, and uniqueness (Supplementary Table S1).