Deciphering transcript architectural complexity in bacteria and archaea.

RNA transcripts are potential therapeutic targets, yet bacterial transcripts have uncharacterized biodiversity. We developed an algorithm for transcript prediction called tp.py using it to predict transcripts (mRNA and other RNAs) in Escherichia coli K12 and E2348/69 strains (Bacteria:gamma-Proteobacteria), Listeria monocytogenes strains Scott A and RO15 (Bacteria:Firmicute), Pseudomonas aeruginosa strains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), and Haloferax volcanii (Archaea:Halobacteria). From >5 million E. coli K12 and >3 million E. coli E2348/69 newly generated Oxford Nanopore Technologies direct RNA sequencing reads, 2,487 K12 mRNAs and 1,844 E2348/69 mRNAs were predicted, with the K12 mRNAs containing more than half of the predicted E. coli K12 proteins. While the number of predicted transcripts varied by strain based on the amount of sequence data used, across all strains examined, the predicted average size of the mRNAs was 1.6-1.7 kbp, while the median size of the 5'- and 3'-untranslated regions (UTRs) were 30-90 bp. Given the lack of bacterial and archaeal transcript annotation, most predictions were of novel transcripts, but we also predicted many previously characterized mRNAs and ncRNAs, including post-transcriptionally generated transcripts and small RNAs associated with pathogenesis in the E. coli E2348/69 LEE pathogenicity islands. We predicted small transcripts in the 100-200 bp range as well as >10 kbp transcripts for all strains, with the longest transcript for two of the seven strains being the nuo operon transcript, and for another two strains it was a phage/prophage transcript. This quick, easy, and reproducible method will facilitate the presentation of transcripts, and UTR predictions alongside coding sequences and protein predictions in bacterial genome annotation as important resources for the research community.IMPORTANCEOur understanding of bacterial and archaeal genes and genomes is largely focused on proteins since there have only been limited efforts to describe bacterial/archaeal RNA diversity. This contrasts with studies on the human genome, where transcripts were sequenced prior to the release of the human genome over two decades ago. We developed software for the quick, easy, and reproducible prediction of bacterial and archaeal transcripts from Oxford Nanopore Technologies direct RNA sequencing data. These predictions are urgently needed for more accurate studies examining bacterial/archaeal gene regulation, including regulation of virulence factors, and for the development of novel RNA-based therapeutics and diagnostics to combat bacterial pathogens, like those with extreme antimicrobial resistance.

Deciphering Bacterial and Archaeal Transcriptional Dark Matter and Its Architectural Complexity.

Transcripts are potential therapeutic targets, yet bacterial transcripts remain biological dark matter with uncharacterized biodiversity. We developed and applied an algorithm to predict transcripts for Escherichia coli K12 and E2348/69 strains (Bacteria:gamma-Proteobacteria) with newly generated ONT direct RNA sequencing data while predicting transcripts for Listeria monocytogenes strains Scott A and RO15 (Bacteria:Firmicute), Pseudomonas aeruginosa strains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), and Haloferax volcanii (Archaea:Halobacteria) using publicly available data. From >5 million E. coli K12 ONT direct RNA sequencing reads, 2,484 mRNAs are predicted and contain more than half of the predicted E. coli proteins. While the number of predicted transcripts varied by strain based on the amount of sequence data used for the predictions, across all strains examined, the average size of the predicted mRNAs is 1.6-1.7 kbp while the median size of the predicted bacterial 5'- and 3'- UTRs are 30-90 bp. Given the lack of bacterial and archaeal transcript annotation, most predictions are of novel transcripts, but we also predicted many previously characterized mRNAs and ncRNAs, including post-transcriptionally generated transcripts and small RNAs associated with pathogenesis in the E. coli E2348/69 LEE pathogenicity islands. We predicted small transcripts in the 100-200 bp range as well as >10 kbp transcripts for all strains, with the longest transcript for two of the seven strains being the nuo operon transcript, and for another two strains it was a phage/prophage transcript. This quick, easy, inexpensive, and reproducible method will facilitate the presentation of operons, transcripts, and UTR predictions alongside CDS and protein predictions in bacterial genome annotation as important resources for the research community.

Common analysis of direct RNA sequencinG CUrrently leads to misidentification of m5C at GCU motifs.

RNA modifications, such as methylation, can be detected with Oxford Nanopore Technologies direct RNA sequencing. One commonly used tool for detecting 5-methylcytosine (m5C) modifications is Tombo, which uses an "Alternative Model" to detect putative modifications from a single sample. We examined direct RNA sequencing data from diverse taxa including viruses, bacteria, fungi, and animals. The algorithm consistently identified a m5C at the central position of a GCU motif. However, it also identified a m5C in the same motif in fully unmodified in vitro transcribed RNA, suggesting that this is a frequent false prediction. In the absence of further validation, several published predictions of m5C in a GCU context should be reconsidered, including those from human coronavirus and human cerebral organoid samples.

Common Analysis of Direct RNA SequencinG CUrrently Leads to Misidentification of 5-Methylcytosine Modifications at GCU Motifs.

RNA modifications, such as méthylation, can be detected with Oxford Nanopore Technologies direct RNA sequencing. One commonly used tool for detecting 5-methylcytosine (m5C) modifications is Tombo, which uses an "Alternative Model" to detect putative modifications from a single sample. We examined direct RNA sequencing data from diverse taxa including virus, bacteria, fungi, and animals. The algorithm consistently identified a 5-methylcytosine at the central position of a GCU motif. However, it also identified a 5-methylcytosine in the same motif in fully unmodified in vitro transcribed RNA, suggesting that this a frequent false prediction. In the absence of further validation, several published predictions of 5-methylcytosine in human coronavirus and human cerebral organoid RNA in a GCU context should be reconsidered.