Abstract
BackgroundAs sequencing costs are being lowered continuously, RNA-seq has gradually been adopted as the first choice for comparative transcriptome studies with bacteria. Unlike microarrays, RNA-seq can directly detect cDNA derived from mRNA transcripts at a single nucleotide resolution. Not only does this allow researchers to determine the absolute expression level of genes, but it also conveys information about transcript structure. Few automatic software tools have yet been established to investigate large-scale RNA-seq data for bacterial transcript structure analysis.ResultsIn this study, 54 directional RNA-seq libraries from Salmonella serovar Typhimurium (S. Typhimurium) 14028s were examined for potential relationships between read mapping patterns and transcript structure. We developed an empirical method, combined with statistical tests, to automatically detect key transcript features, including transcriptional start sites (TSSs), transcriptional termination sites (TTSs) and operon organization. Using our method, we obtained 2,764 TSSs and 1,467 TTSs for 1331 and 844 different genes, respectively. Identification of TSSs facilitated further discrimination of 215 putative sigma 38 regulons and 863 potential sigma 70 regulons. Combining the TSSs and TTSs with intergenic distance and co-expression information, we comprehensively annotated the operon organization in S. Typhimurium 14028s.ConclusionsOur results show that directional RNA-seq can be used to detect transcriptional borders at an acceptable resolution of ±10-20 nucleotides. Technical limitations of the RNA-seq procedure may prevent single nucleotide resolution. The automatic transcript border detection methods, statistical models and operon organization pipeline that we have described could be widely applied to RNA-seq studies in other bacteria. Furthermore, the TSSs, TTSs, operons, promoters and unstranslated regions that we have defined for S. Typhimurium 14028s may constitute valuable resources that can be used for comparative analyses with other Salmonella serotypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1555-8) contains supplementary material, which is available to authorized users.
Highlights
As sequencing costs are being lowered continuously, RNA-seq has gradually been adopted as the first choice for comparative transcriptome studies with bacteria
Extraction of transcriptional start sites (TSSs) and termination sites (TTSs) based on real read mapping patterns We developed an empirical method, based on the biased distribution of reads at the 5′ end of transcripts, to extract transcript structure information from standard bacterial RNA-seq data
A biased distribution is problematic because the existing methods for extracting transcript information from bacterial RNAseq data are based on Poisson distribution models, which assume a hypothetically even distribution across an entire transcript [7,29]
Summary
As sequencing costs are being lowered continuously, RNA-seq has gradually been adopted as the first choice for comparative transcriptome studies with bacteria. RNA-seq can directly detect cDNA derived from mRNA transcripts at a single nucleotide resolution. Does this allow researchers to determine the absolute expression level of genes, but it conveys information about transcript structure. RNA-seq can determine the absolute gene expression levels with lower variation compared to microarray technology, but can be used to find new genes and resolve the structure of transcripts [1,2]. The transcript structure has only been determined in detail for a few bacterial strains [12,13], while for many others, including Salmonella enterica, the transcript organization has been resolved for only select groups of genes [14]. Recent studies have revealed dynamic TSS and operon patterns in the same strains under different growth conditions, demonstrating the increased complexity of transcript structure analysis [15,16]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
