Overall, all three technologies can be used for genome and transcriptome sequencing. Other applications aimed at RNA-seq of single cells (Tang et al., 2009) are eagerly awaited, but not yet described for bacteria and are

not commercially available. As indicated previously, high-throughput CX-4945 solubility dmso sequencing of cDNA libraries has the potential to study transcription at the single nucleotide level and hence yield much more detail on RNA transcripts present in a population of microbial cells. However, when compared with eukaryotic RNA, working with bacterial RNA has always been a challenge. Unlike eukaryotic mRNA, most bacterial mRNAs do not have a poly-A tail (Deutscher, 2003), and hence cannot be isolated from other RNA sources by hybridization to immobilized poly-T. Furthermore, bacterial RNA preparations learn more usually contain up to 80% rRNA and tRNA (Condon, 2007), and to add insult to injury, bacterial mRNA often has a very short half-life and hence can be highly unstable (Deutscher, 2003; Condon, 2007). Hence, it is not surprising that high-throughput sequencing of the transcriptome of a cell (RNA-seq or mRNA-seq)

was first described for eukaryotic cells, including the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe (Nagalakshmi et al., 2008; Wilhelm et al., 2008), mouse organs and embryonic stem cells (Cloonan et al., 2008; Mortazavi et al., 2008), human cell lines (Sultan et al., 2008) and the plant Arabidopsis thaliana (Lister et al., 2008). In these studies, transcriptome sequencing was highly informative,

and allowed for investigation of levels of transcripts as well as (alternative) splicing events. More information on RNA-seq in eukaryotic organisms can be found in recent reviews (Wang et al., 2009; Wilhelm & Landry, 2009). Figure 1 outlines the basic steps involved in generating cDNA libraries for high-throughput sequencing of microbial transcriptomes, and the subsequent analysis of these. So far, all papers describing the use of high-throughput sequencing for bacterial transcriptomics have specified using the optional enrichment methods, usually PAK5 based on depletion of tRNA and/or rRNA (Passalacqua et al., 2009; Perkins et al., 2009; Yoder-Himes et al., 2009). Size selection has also been used for the removal of mRNA and rRNA (Liu et al., 2009), although this is a potentially risky approach because this could remove long noncoding or antisense RNA species, as reported in Listeria and Bacillus (Rasmussen et al., 2009; Toledo-Arana et al., 2009). After sequence reads are mapped onto the genome sequence, these are usually visualized by generating histograms of reads on the annotated genome sequence, using a freely available software like artemis (Carver et al., 2008) or the Affymetrix Integrated Genome Browser (http://www.affymetrix.