<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
RNA sequencing has emerged as the premier approach to study bacterial transcriptomes. While the earliest published studies analyzed the data qualitatively, the data are readily digitized and lend themselves to quantitative analysis. High-resolution RNA sequence (RNA-seq) data allows transcriptional features (promoters, terminators, operons, among others) to be pinpointed on any bacterial transcriptome. Once the transcriptome is mapped, the activity of transcriptional features can be quantified. Here we highlight how quantitative transcriptome analysis can reveal biological insights and briefly discuss some of the challenges to be faced by the field of bacterial transcriptomics in the near future.
Genetics, Microbial, Bacteria, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Gene Expression Regulation, Bacterial, Molecular Biology
Genetics, Microbial, Bacteria, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Gene Expression Regulation, Bacterial, Molecular Biology
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 92 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |