Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Now, a team led by researchers at the Boston University School of Medicine have developed new algorithms, specific to bacterial gene structures and transcriptomes, for analysis of RNA-seq data. The algorithms are implemented in an open source software system called Rockhopper that supports various stages of bacterial RNA-seq data analysis, including aligning sequencing reads to a genome, constructing transcriptome maps, quantifying transcript abundance, testing for differential gene expression, determining operon structures and visualizing results. The researchers demonstrate the performance of Rockhopper using 2.1 billion sequenced reads from 75 RNA-seq experiments conducted with Escherichia coli, Neisseria gonorrhoeae, Salmonella enterica, Streptococcus pyogenes and Xenorhabdus nematophila. They found that the transcriptome maps generated by these algorithms are highly accurate when compared with focused experimental data from E. coli and N. gonorrhoeae, and they validate the system’s ability to identify novel small RNAs, operons and transcription start sites. These results suggest that Rockhopper can be used for efficient and accurate analysis of bacterial RNA-seq data, and that it can aid with elucidation of bacterial transcriptomes.
Availability – Rockhopper is available at http://cs.wellesley.edu/∼btjaden/Rockhopper.
- McClure R, Balasubramanian D, Sun Y, Bobrovskyy M, Sumby P, Genco CA, Vanderpool CK, Tjaden B. (2013) Computational analysis of bacterial RNA-Seq data. Nucleic Acids Res [Epub ahead of print]. [article]