This article is part of the supplement: Ninth Annual MCBIOS Conference. Dealing with the Omics Data Deluge

Open Access Proceedings

Transcriptome profile of a bovine respiratory disease pathogen: Mannheimia haemolytica PHL213

Joseph S Reddy1, Ranjit Kumar2, James M Watt3, Mark L Lawrence1, Shane C Burgess4 and Bindu Nanduri1*

Author Affiliations

1 College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA

2 Center for Clinical and Translational Science, University of Alabama at Birmingham, Birmingham, AL 35294, USA

3 Eagle Applied Sciences LLC, San Antonio, TX 78248, USA

4 College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ 85721, USA

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BMC Bioinformatics 2012, 13(Suppl 15):S4  doi:10.1186/1471-2105-13-S15-S4

Published: 11 September 2012

Abstract

Background

Computational methods for structural gene annotation have propelled gene discovery but face certain drawbacks with regards to prokaryotic genome annotation. Identification of transcriptional start sites, demarcating overlapping gene boundaries, and identifying regulatory elements such as small RNA are not accurate using these approaches. In this study, we re-visit the structural annotation of Mannheimia haemolytica PHL213, a bovine respiratory disease pathogen. M. haemolytica is one of the causative agents of bovine respiratory disease that results in about $3 billion annual losses to the cattle industry. We used RNA-Seq and analyzed the data using freely-available computational methods and resources. The aim was to identify previously unannotated regions of the genome using RNA-Seq based expression profile to complement the existing annotation of this pathogen.

Results

Using the Illumina Genome Analyzer, we generated 9,055,826 reads (average length ~76 bp) and aligned them to the reference genome using Bowtie. The transcribed regions were analyzed using SAMTOOLS and custom Perl scripts in conjunction with BLAST searches and available gene annotation information. The single nucleotide resolution map enabled the identification of 14 novel protein coding regions as well as 44 potential novel sRNA. The basal transcription profile revealed that 2,506 of the 2,837 annotated regions were expressed in vitro, at 95.25% coverage, representing all broad functional gene categories in the genome. The expression profile also helped identify 518 potential operon structures involving 1,086 co-expressed pairs. We also identified 11 proteins with mutated/alternate start codons.

Conclusions

The application of RNA-Seq based transcriptome profiling to structural gene annotation helped correct existing annotation errors and identify potential novel protein coding regions and sRNA. We used computational tools to predict regulatory elements such as promoters and terminators associated with the novel expressed regions for further characterization of these novel functional elements. Our study complements the existing structural annotation of Mannheimia haemolytica PHL213 based on experimental evidence. Given the role of sRNA in virulence gene regulation and stress response, potential novel sRNA described in this study can form the framework for future studies to determine the role of sRNA, if any, in M. haemolytica pathogenesis.