Volume 13 Supplement 15
Transcriptome profile of a bovine respiratory disease pathogen: Mannheimia haemolytica PHL213
© Reddy et al.; licensee BioMed Central Ltd. 2012
Published: 11 September 2012
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.
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.
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.
A systems-level understanding of organisms is not feasible by studying the functions of individual genes or proteins using reductionist approaches. It requires describing all molecular-level components that constitute building blocks of the system, identifying interactions among these components and determining regulatory modules to model emergent behavior . As such, identifying all functional elements including genes, RNA, and proteins is a prerequisite to generating predictive models of biological response to biotic or abiotic perturbations. The genome sequence encodes all the necessary information required to decipher its functions. Therefore, genome sequencing, with concomitant structural annotation, i.e., identification of the functional elements within the genome, including genes, gene structures, open reading frames and regulatory motifs, is a critical step for conducting systems biology research. It is imperative that current and up-to-date knowledge of molecular level components exists for a genome sequence. Therefore, re-annotation is key to identifying these fundamental components of biological processes.
De novo assembly of a genome is followed by mapping of functional elements using computational methods. Computational methods for prokaryotic gene annotation such as Gene Locator and Interpolated Markov ModelER (GLIMMER)  and GeneMark.hmm  use hidden Markov models  based on a sequence similarity measure generated from previously annotated genomes. These algorithms do not accurately identify all genes in the genome and sometimes result in errors, especially in positioning of translational start codons  and in the identification of small protein coding genes. Another major problem with computational approaches is over-annotation, which arises from the failure to discriminate between random open reading frames and those that are translated. Computational prediction of small non-coding RNA (sRNA), which lack sequence conservation in closely related species, has limited accuracy since transcriptional signal prediction (promoter and rho-independent terminator prediction) is also not accurate. Therefore, sRNA that regulate many biological processes, including virulence in bacterial pathogens, cannot be identified by computational approaches alone.
Experimental identification of expressed regions in the genome can help overcome some of the drawbacks of computational methods and is a complementary approach to computational genome annotation methods. DNA microarrays, serial analysis of gene expression (SAGE) or high throughput transcriptome sequencing technologies such as RNA-Seq, can all be used to measure genome expression [6–9]. Of these methods, RNA-Seq, which generates a single nucleotide resolution map of the transcriptome, can help annotate mRNA, non-coding RNA and sRNA, transcriptional structure of genes, and post-transcriptional modifications induced by alternate splicing in eukaryotes [10–13].
In this study, we report re-annotation of M. haemolytica, a gram-negative bacterial pathogen and one of the causative agents of bovine respiratory disease (BRD) in cattle. BRD is responsible for over $3 billion in losses to the cattle industry every year . M. haemolytica is most commonly isolated in field cases of BRD  and is considered to be the primary pathogen for this disease. Due to its importance for disease etiology, the genome of a bovine strain of M. haemolytica was sequenced in 2006. However, to date, the 2.6 Mb M. haemolytica PHL213 genome sequenced with an 8.4× coverage, is still in its draft phase. Despite being sequenced 6 years ago, the M. haemolytica genome sequence has not seen any improvement in its quality. Therefore, we chose to conduct RNA-Seq based re-annotation of M.haemolytica. The single nucleotide resolution map generated helped identify novel protein coding regions, sRNA, correct annotation errors, and operon structures.
Materials and methods
M. haemolytica PHL213 was cultured in brain heart infusion (BHI) to mid-log phase (OD620 = 0.8). Cells from a single culture were treated with RNAprotect reagent (Qiagen, Valencia, CA) and stored at -80°C for subsequent RNA isolation. Total RNA from this single culture was extracted using the RNeasy mini kit (Qiagen, Valencia, CA), following manufacturer's protocols. It is to be noted that this kit allows for the extraction of transcripts that are at least 200 nucleotides and larger. RNA preparations were treated with RNase-free DNAse (Invitrogen, Carlsbad, CA) and the integrity of the RNA was determined using Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA). RNA sample with RNA Integrity Number (RIN) of 8 was used for the RNA-Seq experiment. From total RNA, mRNA was enriched by removing the rRNAs using MICROBExpress™ kit (Ambion, Austin, TX). This enrichment step specifically removes large rRNAs; small RNAs (i.e., tRNA and 5S rRNA) are not removed. In the first step of the MICROBExpress™ kit procedure, total RNA was mixed with an optimized set of capture oligonucleotides that bind to the bacterial 16S and 23S rRNAs. Next, the rRNA hybrids were removed from the solution using derivatized magnetic microbeads. The mRNA remained in the supernatant and was recovered by ethanol precipitation and quantified by Bioanalyzer 2100. Our RNA preparation did not include entities < 200 nucleotides in length.
A cDNA library was constructed using the Illumina mRNA-Seq sample prep kit (Illumina, San Diego, CA) with 100 ng RNA enriched for mRNA isolated from a single in vitro culture, following manufacturer's instructions. mRNA was chemically fragmented and randomly primed for reverse transcription and second-strand synthesis. The resulting cDNA was end-repaired and 'A' overhangs were added. Illumina paired-end sequence adaptors were ligated to the cDNA fragments. Fragments of approximately 200 bp were isolated from a 2% agarose gel and amplified (18 cycles) according to the Illumina protocol. Bioanalyzer 2100 (Agilent) was used to quantify and confirm the fragment size of each library. 1 nM of mRNA-seq library sample prepared for sequencing on the Illumina GAII (San Diego, CA) was denatured and diluted to 6 pM for clustering (v2) according to the manufacturer's protocol. Single read sequencing of the clustered flow cell was performed using Illumina's SBS chemistry (v3) and SCS data analysis pipeline v2.4. Flow-cell image analysis and cluster intensity calculations were carried out by Illumina Real Time Analysis (RTA v126.96.36.199) software. Subsequent base-calling was performed using the Illumina GA Pipeline v1.5.1 software. The resulting Illumina reads were quality-filtered by removing reads containing Ns.
The sequencing experiment produced 9,055,826 reads. FASTQ reads generated by Illumina were converted to Sanger FASTQ format using Perl scripts from the Mapping and Assembly with Qualities (MAQ) software package . Reads (Sanger FASTQ format) were mapped to the 2.6 Mb M. haemolytica PHL213 [GenBank: AASA00000000] reference genome using Bowtie . The parameters in Bowtie that control the speed and sensitivity were adjusted as follows: reads with no more than 2 mismatches per read (n = 2) were aligned, and any reads mapped to more than one location across the genome (ambiguous reads) were discarded (m = 1). Post alignment, a human-readable sequence alignment/map (.SAM) format file was converted to a "pileup" format file using SAMTools . This pileup file contains the count of reads per base aligned to each location across the length of the genome. The SAM file was also converted into a binary alignment/map (.BAM) format. These BAM formatted files are necessary for visualization of read alignments in Artemis viewer. The Artemis browser enabled the visual/manual inspection of alignment results in the context of the existing genome annotation. The pileup file, in conjunction with the annotation information of M. haemolytica PHL213, was processed using in-house Perl  scripts. Data generated from the RNA-Seq experiment was submitted to the NCBI Sequence Read Archive [SRA049621.1] as reads in FASTQ format [SRR402063.1] and the .BAM alignment file [SRR402079.4] generated by aligning the reads to the reference genome.
Analysis of expressed intergenic regions
Identifying expressed regions within the genome that have not been previously annotated will improve the existing structural annotation of the M. haemolytica PHL213. Prior to the analysis of expressed regions in the genome, we determined the signal to noise ratio cutoff for background expression using the pileup file. Coverage depth (reads per base) greater than the lower tenth percentile of all reads was considered to be expressed and in this dataset, this corresponded to 7 reads/base [8, 20]. Based on this read/base cutoff, expressed intergenic regions (EIRs) were identified by applying an additional length cutoff of 70 bp. Shorter regions (less than 70 bp) were discarded to reduce the number of false positives. Custom Perl scripts were written to parse the pileup file and the existing genome structural annotation to identify (i) expressed annotated regions, (ii) expressed regions previously not annotated and, (iii) regions that are annotated but are not expressed. All EIRs were further analyzed using BLASTX  searches to determine their protein coding potential. If an EIR was found to be a perfect match (~100% coverage) for a protein, it was classified as a putative novel protein coding region. All EIRs with partial BLASTX hits were evaluated for the presence of an alternate start site or mutation in the start or stop codon associated with the annotated region. If the BLASTX search revealed a frameshift mutation, the EIR and the gene associated with the frameshift mutation were classified as a frameshift. EIRs with poor BLASTX hits and without any association to genes containing annotation errors were excluded from further analysis. EIRs without BLASTX hits were considered to be potential small non-coding RNA.
The Prokaryotic Promoter Prediction (PPP) program  (from PePPER suite ) and Transterm HP  were used to predict promoters and rho-independent terminators, respectively, in the forward and reverse strands of the M. haemolytica PHL213 genome. The locations of promoters and terminators were organized into .GFF files. A Perl script was written to identify putative sRNA i.e. EIRs with promoters or terminators associated to their loci. EIRs with no computationally-predicted promoters or rho-independent terminators were searched against the Rfam database  to determine whether these sequences were annotated in Rfam. EIRs that could not be classified as sRNA by Rfam were excluded from further analysis.
Analysis of expressed annotated regions
Using the annotation information (gene loci) of M. haemolytica PHL213 and the pileup file, all annotated regions that were expressed above the background signal to noise ratio cutoff with at least 60% coverage were considered to be expressed, which accounts for uniform evaluation of varying gene lengths. Similar measures have been used in other transcriptome profiling studies [8, 26, 27]. Annotated regions below 60% coverage were considered as 'not expressed' under the current experimental conditions. After having identified expressed genes, operon structures within the genome were also defined. The first step towards identifying an operon was to identify co-expressed pairs of coding regions. Two regions were considered to be co-expressed when they were identified as expressed on the same strand (5' to 3' or 3' to 5') and the region between them was also expressed. After such co-expressed pairs were identified, they were extended to construct operons by including additional co-expressed pairs in the vicinity satisfying the same conditions for co-expression as described earlier. Operon structures identified by RNA-Seq were compared to the computationally-predicted operon structures described by the Database for prOkaryotic OpeRons (DOOR)  for cross validation.
Read alignment to the M. haemolytica PHL213 genome
The M. haemolytica PHL213 is a 2.6 Mb draft genome containing 2,837 annotated regions of which 2,695 are protein coding with a 40% G+C content . For structural annotation of M. haemolytica at the RNA level, the transcriptome of M. haemolytica PHL213 was sequenced using RNA-Seq. Sequencing-based analysis of the transcriptome overcomes the limitations of the hybridization-based microarray approach. Head-on comparison of RNA-Seq with microarrays has shown that RNA-Seq has negligible technical variability , making it possible to obtain a reliable estimate of gene expression without replicate analysis. Therefore, we applied RNA-Seq for re-annotation of M. haemolytica and conducted the analysis from a single in vitro experiment. Reads with an average length of 76 bp generated on the Illumina platform were mapped onto the reference genome using the Bowtie read alignment program. Bowtie is an ultrafast, memory efficient alignment program that uses the Burrows-Wheeler transform  with a novel quality backtracking algorithm that permits mismatches. Bowtie performs better than Short Oligonucleotide Analysis Package (SOAP)  and MAQ, and its sensitivity at aligning reads is as good as both SOAP and MAQ. Of the 9,055,826 reads generated by Illumina, 3,917,458 reads (43.26%) that mapped uniquely to the genome were used for downstream analysis. 2,989,603 reads (33.01%) failed to align due to mismatches. The remaining 2,148,765 reads (23.73%), which mapped to more than one location in the genome (ambiguous reads), were excluded from analysis. For annotation purposes, reads that map to unique locations alone are used [8, 33–37]. The cutoff value for true-positive expression of a coding region of 7 reads/base was calculated from the expression (number of reads per base) in the tenth percentile of all reads [8, 20], as we did earlier for RNA-Seq based re-annotation of another BRD pathogen Histophilus somni .
Expressed intergenic regions
Novel protein coding regions
Potential novel protein coding regions identified in M. haemolytica PHL213
hypothetical protein HPS_04442
Haemophilus parasuis 29755
conserved hypothetical protein
Methylococcus capsulatus str. Bath
hypothetical protein COI_2717
M. haemolytica serotype A2 str. OVINE
Lactobacillus crispatus ST1
hypothetical protein GG9_1745
Haemophilus haemolyticus M19501
hypothetical protein Csp_D29610
Curvibacter putative symbiont of Hydra magnipapillata
hypothetical protein COK_2315
M. haemolytica serotype A2 str. Bovine
hypothetical protein COI_2717
M. haemolytica serotype A2 str. Ovine
hypothetical protein COI_1129
M. haemolytica serotype A2 str. Ovine
hypothetical protein COK_1081
M. haemolytica serotype A2 str. Bovine
hypothetical protein COK_2196
M. haemolytica serotype A2 str. Bovine
Haemophilus influenzae NT127
hypothetical protein COK_0003
M. haemolytica serotype A2 str. Bovine
hypothetical protein COK_1399
M. haemolytica serotype A2 str. Bovine
Corrections made to the existing genome annotation
Suggested corrections made to the existing annotation of M. haemolytica PHL213
Corrected gene length
Corrected protein length
Mutated Start (L)
Mutated Start (L)
Mutated Start (L)
Mutated Start (L)
Putative novel sRNA identified in M. haemolytica PHL213
Flanking gene (left)
Flanking gene (right)
Haemophilus parasuis SH0165
Mannheimia granulomatis str. P1135/26
Haemophilus parasuis SH0165
Histophilus somni 2336
Haemophilus ducreyi strain 35000 HP
Actinobacillus pleuropneumoniae serovar 3 str. JL03
Putative sRNA identified in identified in M. haemolytica PHL213 using the Rfam database
Gene expression and operons
The M. haemolytica PHL213 genome consists of 2,837 annotated genes, 2,695 of which code for proteins. Genes were considered to be expressed if 60% of the gene length had at least 7 reads aligned/nucleotide. Based on this criteria, 2,506 of all annotated regions in the genome (87.63%) were identified as expressed with 95.25% coverage i.e. approximately 95% of the sequence of the annotated region had at least 7 reads aligned/nucleotide. Expressed annotated genes and their coverage are documented in Additional file 2.
Functional analysis of the expressed annotated regions was based on the existing annotation of M. haemolytica genome available at NCBI. It is interesting to note that genes that are described as virulence factors are also expressed under normal culture conditions. For example, genes related to leukotoxin (MHA_0253, MHA_0254, MHA_0255 and MHA_0266), an important virulence factor, were all expressed. Also, the 12 capsule forming genes whose role in virulence includes adherence to host and resistance to serum-mediated killing and phagocytosis [45, 46] were all found to be expressed. In addition to these, we also found that 40 genes associated with lipopolysaccharide or lipoproteins and contribute to virulence by initiating an inflammatory cytokine response [45, 47] to be expressed. Genes responsible for forming the type IV pilus associated with M. haemolytica that is responsible for DNA uptake, adhesion, and motility  were expressed. Filamentous hemagglutinin genes of M. haemolytica (MHA_0866, MHA_0867), responsible for adhesion to host mucosa , were expressed. Adhesins play an important role in virulence, and all annotated genes related to this function, such as MHA_2262, MHA_0708, MHA_2492, MHA_2701, MHA_1367, MHA_0563 and MHA_2800, among others, were all identified as expressed in our experiment. Genes responsible for resistance towards antibiotics such as β-lactams, tetracycline, streptomycin, and sulfonamides  in M. haemolytica were also expressed. Annotated regions that were not expressed had coverage of only 30%. Of the 331 annotated regions that were not expressed 236 were annotated as "hypothetical proteins" and 26 were "hypothetical bacteriophage proteins."
Using expression patterns of coding regions, we identified paired gene expression and operon structures. RNA-Seq based operon structures were compared to the computationally predicted structures using DOOR . We identified 1,086 co-expressed pairs of genes that could be organized into 518 potential operons. DOOR predicted 1,295 co-expressed pairs forming 599 operons (Additional file 3). The overlap between RNA-Seq based and DOOR-based co-expressed pairs was 854. Our study identified relatively fewer co-expressed pairs as compared to DOOR. This could be due to the fact that 331 of the 2,837 annotated regions were not expressed in our dataset. Furthermore, this method cannot detect genes whose expression is suppressed by polar mutations. The single nucleotide resolution map enabled the identification of co-expressed pairs and definition of operon structures and regulatory patterns. Availability of operon structures will facilitate understanding the coordinated regulation of genes in M. haemolytica to moderate metabolic pathways under different environmental conditions.
Identification of all functional elements of the genome is fundamental to understanding the dynamics of biological processes that occur within any living organism. Gene models are available for sequenced genomes that are based on computational approaches. However, a number of recent studies highlight the need for genome re-annotation, prior to conducting holistic systems biology analyses. Experimental approaches, at times, shed light on regions of the genome where computational methods of structural annotation fail. Re-annotation studies of several species including disease causing pathogens have revealed numerous genes, regulatory regions and complex metabolic pathways that remained undetected based on the initial annotation [8, 50–55]. In this study, we applied a combinatorial approach i.e. RNA-Seq based transcriptome analysis in conjunction with computational resources, to structurally annotate a bacterial genome at the RNA level. For the first time, we report RNA-Seq based annotation of the genome of M. haemolytica PHL213, one of the primary pathogens of Bovine Respiratory Disease in cattle . Its genome was sequenced with 8.4× coverage, and is in draft phase since 2006.
We have recently re-annotated Histophilus somni 2336, another BRD pathogen belonging to Pasteurellaceae like M. haemolytica. RNA-Seq based transcriptome analysis identified 38 novel protein coding regions and 82 sRNA in H. somni . Compared to the draft genome for M. haemolytica, H. somni has a complete genome sequence. Yet, re-annotation of this genome identified a number of functional elements missed in the initial annotation. The relatively poor quality of the existing structural annotation of M. haemolytica can be enhanced by re-annotation, and this was the motivation behind the current study.
Re-annotation enabled us to fix errors in existing annotation. A mutation that might have occurred during replication could alter the structure of the gene in its vicinity. Computational methods, when predicting a gene, seek to identify an ORF and its putative start and stop codons to define gene boundaries. Mutations in the sequence between the start or stop codon of a gene might not actually affect gene prediction or may sometimes result in a frameshift. If the mutation is to occur in the start or stop codon itself, algorithms would seek to identify the next available start or stop codon. This would lead to alteration in gene locus and a subsequent gene annotation error. Such annotation errors cannot be detected without experimental validations. The single nucleotide resolution transcription map generated by RNA-Seq is one of the most efficient ways to detect such annotation errors. As described in our workflow (Figure 1), once EIRs overlapping a certain gene were identified, BLASTX searches of these regions helped in defining the actual boundaries and correct annotation errors, if any. Mutations leading to a frameshift can result in a gene being completely disrupted. Such frameshifts remain undetected by automated approaches, but can be identified by experimental approaches such as RNA-Seq used in this study. Genome-wide studies using experimental methods can help validate these predictions and improve the quality of annotation across genomes and eliminate errors from being transferred from one genome to another during annotation of novel assemblies.
Understanding coordinated regulation of gene expression in bacteria requires the description of operon structures in the genome. Prior to this study, operon structures were unavailable for M. haemolytica. Since computationally-predicted operon structures were unavailable, we first generated a set of computationally-predicted operons using DOOR (Additional file 3). RNA-Seq enabled us to identify expressed gene pairs that could be expanded into potential operons. Comparison of DOOR predicted operons with RNA-Seq based operons in M. haemolytica showed a major overlap and cross-validated the findings in both approaches. Thus re-annotation helped validate 599 operons predicted by DOOR. We also identified 233 co-expressed pairs that were not identified by DOOR. Since the strand specificity of expression is lost in RNA-Seq experiment described here, at best the operons identified in this study should be considered 'potential operons' that will require experimental validation in future studies. Furthermore, this experiment-based identification of co-expression will not be able to identify genes that are expressed in a polar fashion within the operon. Analysis of the functions of genes identified as expressed by RNA-Seq resulted in an interesting finding. Genes that are annotated as being virulence factors were identified as expressed under normal culture conditions. These results are consistent with our findings in H. somni. Our results indicate that the expectation of 'virulence factor' being expressed only during pathogenesis may not be accurate. It is possible that there is a basal pervasive level of expression of these factors and that it is the difference in the expression level that actually corresponds to virulence.
Computational methods for identification of sRNA are not accurate, and transcriptome profiling using deep sequencing methods can help identify novel sRNA. sRNA play a crucial role in adaptive response to stress by directly or indirectly regulating virulence genes , as shown in Staphylococcus aureus , Pseudomonas aeruginosa  and Vibrio cholerae [59, 60]. However, a comprehensive understanding of sRNA regulatory roles during adaptive responses and pathogenesis is only possible after their identification. Despite the drawbacks in sample preparation and lack of strand specificity, we identified 44 potential novel sRNA. The identified novel sRNA were searched for homology in the sRNA database (sRNAdb) against other bacterial sRNA identified through similar transcriptomics studies and/or computational approaches . Only 15 sRNA had partial alignments of 20-30 nucleotides and the remaining had very poor sequence conservation across the database (Additional file 4). We also compared the 44 sRNA identified in the M. haemolytica genome with 82 H. somni sRNA using 'BLAST 2 sequences' megablast . No similarity was found, indicating poor consensus among non-coding RNA. These results suggest that regulation of sRNA is probably as diverse and as complex as gene or protein regulation.
The inherent limitations of our experimental setup i.e. lack of enrichment specifically for sRNA, lack of strand specificity information and lack of biological replicates, isolation of RNA at different stages of in vitro growth, etc, did not allow comprehensive identification of sRNA. Due to the same limitations, the identified gene co-expression also needs further validation work in future. However, as the results indicate, application of RNA-Seq enhanced the existing annotation of M. haemolytica. RNA-Seq based annotation is not the 'final' and conclusive step in identifying functional elements in this important bacterial pathogen. In fact, this work is part of the continuum in a typical systems biology work flow.
The RNA-Seq based transcriptome map of M. haemolytica PHL213 validated annotated open reading frames and led to the discovery of potential novel protein coding regions. We identified operon structures and were able to fix exiting annotation errors by correcting gene boundaries. The availability of experimentally validated open reading frames, potential novel sRNA, potential protein coding regions, and operon structures form the basis for future investigations to determine the role of these elements during BRD pathogenesis. This study also demonstrates the utility of free and easy to bioinformatics tools for RNA-Seq data analysis workflow.
List of abbreviations used
Basic Local Alignment Search Tool
Bovine Respiratory Disease
Brain Heart Infusion
Database for prOkaryotic OpeRons
Expressed Intergenic Region
Gene Locator and Interpolated Markov ModelER
Mapping and Assembly with Qualities
Open Reading Frame
Prokaryotic Promoter Prediction
Serial Analysis of Gene Expression
Short Oligonucleotide Analysis Package
small non-coding RNA database.
This project was partially supported by the Institute for Genomics, Biocomputing and Biotechnology, and the National Science Foundation (Mississippi EPSCoR-0903787), and Mississippi INBRE funded by grants from the National Center for Research Resources (5P20RR016476-11) and the National Institute of General Medical Sciences (8 P20 GM103476-11) from the National Institutes of Health.
This article has been published as part of BMC Bioinformatics Volume 13 Supplement 15, 2012: Proceedings of the Ninth Annual MCBIOS Conference. Dealing with the Omics Data Deluge. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/13/S15
- Oltvai ZN, Barabasi AL: Systems Biology. Life's Complexity Pyramid. Science 2002, 298(5594):763–4. 10.1126/science.1078563View ArticlePubMedGoogle Scholar
- Delcher AL, Harmon D, Kasif S, White O, Salzberg SL: Improved Microbial Gene Identification with Glimmer. Nucleic Acids Res 1999, 27(23):4636–41. 10.1093/nar/27.23.4636PubMed CentralView ArticlePubMedGoogle Scholar
- Lukashin AV, Borodovsky M: Genemark.Hmm: New Solutions for Gene Finding. Nucleic Acids Res 1998, 26(4):1107–15. 10.1093/nar/26.4.1107PubMed CentralView ArticlePubMedGoogle Scholar
- Eddy SR: What Is a Hidden Markov Model? Nat Biotechnol 2004, 22(10):1315–6. 10.1038/nbt1004-1315View ArticlePubMedGoogle Scholar
- Mathe C, Sagot MF, Schiex T, Rouze P: Current Methods of Gene Prediction, Their Strengths and Weaknesses. Nucleic Acids Res 2002, 30(19):4103–17. 10.1093/nar/gkf543PubMed CentralView ArticlePubMedGoogle Scholar
- Shoemaker DD, Schadt EE, Armour CD, He YD, Garrett-Engele P, McDonagh PD, Loerch PM, Leonardson A, Lum PY, Cavet G, et al.: Experimental Annotation of the Human Genome Using Microarray Technology. Nature 2001, 409(6822):922–7. 10.1038/35057141View ArticlePubMedGoogle Scholar
- Harbers M, Carninci P: Tag-Based Approaches for Transcriptome Research and Genome Annotation. Nat Methods 2005, 2(7):495–502. 10.1038/nmeth768View ArticlePubMedGoogle Scholar
- Kumar R, Lawrence ML, Watt J, Cooksey AM, Burgess SC, Nanduri B: Rna-Seq Based Transcriptional Map of Bovine Respiratory Disease Pathogen " Histophilus Somni 2336". PLoS One 2012, 7(1):e29435. 10.1371/journal.pone.0029435PubMed CentralView ArticlePubMedGoogle Scholar
- Sorek R, Cossart P: Prokaryotic Transcriptomics: A New View on Regulation, Physiology and Pathogenicity. Nat Rev Genet 2010, 11(1):9–16.View ArticlePubMedGoogle Scholar
- Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript Assembly and Quantification by Rna-Seq Reveals Unannotated Transcripts and Isoform Switching During Cell Differentiation. Nat Biotechnol 2010, 28(5):511–5. 10.1038/nbt.1621PubMed CentralView ArticlePubMedGoogle Scholar
- Cho BK, Zengler K, Qiu Y, Park YS, Knight EM, Barrett CL, Gao Y, Palsson BO: The Transcription Unit Architecture of the Escherichia Coli Genome. Nat Biotechnol 2009, 27(11):1043–9. 10.1038/nbt.1582View ArticlePubMedGoogle Scholar
- Mandlik A, Livny J, Robins WP, Ritchie JM, Mekalanos JJ, Waldor MK: Rna-Seq-Based Monitoring of Infection-Linked Changes in Vibrio Cholerae Gene Expression. Cell Host Microbe 2011, 10(2):165–74. 10.1016/j.chom.2011.07.007PubMed CentralView ArticlePubMedGoogle Scholar
- Sharma CM, Hoffmann S, Darfeuille F, Reignier J, Findeiss S, Sittka A, Chabas S, Reiche K, Hackermuller J, Reinhardt R, et al.: The Primary Transcriptome of the Major Human Pathogen Helicobacter Pylori. Nature 2010, 464(7286):250–5. 10.1038/nature08756View ArticlePubMedGoogle Scholar
- Griffin D: Economic Impact Associated with Respiratory Disease in Beef Cattle. Vet Clin North Am Food Anim Pract 1997, 13(3):367–77.PubMedGoogle Scholar
- Frank GH, Tabatabai LB: Neuraminidase Activity of Pasteurella Haemolytica Isolates. Infect Immun 1981, 32(3):1119–22.PubMed CentralPubMedGoogle Scholar
- Li H, Ruan J, Durbin R: Mapping Short DNA Sequencing Reads and Calling Variants Using Mapping Quality Scores. Genome Res 2008, 18(11):1851–8. 10.1101/gr.078212.108PubMed CentralView ArticlePubMedGoogle Scholar
- Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and Memory-Efficient Alignment of Short DNA Sequences to the Human Genome. Genome Biol 2009, 10(3):R25. 10.1186/gb-2009-10-3-r25PubMed CentralView ArticlePubMedGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map Format and Samtools. Bioinformatics 2009, 25(16):2078–9. 10.1093/bioinformatics/btp352PubMed CentralView ArticlePubMedGoogle Scholar
- Wall L, Christiansen T, Orwant J: Programming Perl. 3rd edition. Beijing; Cambridge, Mass.: O'Reilly; 2000:1067. xxxiii xxxiiiGoogle Scholar
- Wurtzel O, Sapra R, Chen F, Zhu Y, Simmons BA, Sorek R: A Single-Base Resolution Map of an Archaeal Transcriptome. Genome Res 2010, 20(1):133–41. 10.1101/gr.100396.109PubMed CentralView ArticlePubMedGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped Blast and Psi-Blast: A New Generation of Protein Database Search Programs. Nucleic Acids Res 1997, 25(17):3389–402. 10.1093/nar/25.17.3389PubMed CentralView ArticlePubMedGoogle Scholar
- Prokaryote Promoter Prediction[http://pepper.molgenrug.nl/index.php/pepper-tools/promoter-predictie-tool]
- Pepper: A Web Based Regulon, Tf and Tfbs Mining System[http://pepper.molgenrug.nl]
- Kingsford CL, Ayanbule K, Salzberg SL: Rapid, Accurate, Computational Discovery of Rho-Independent Transcription Terminators Illuminates Their Relationship to DNA Uptake. Genome Biol 2007, 8(2):R22. 10.1186/gb-2007-8-2-r22PubMed CentralView ArticlePubMedGoogle Scholar
- Gardner PP, Daub J, Tate J, Moore BL, Osuch IH, Griffiths-Jones S, Finn RD, Nawrocki EP, Kolbe DL, Eddy SR, et al.: Rfam: Wikipedia, Clans and The "Decimal" Release. Nucleic Acids Res 2011, 39: D141–5. 10.1093/nar/gkq1129PubMed CentralView ArticlePubMedGoogle Scholar
- Kumar R, Burgess SC, Lawrence ML, Nanduri B: Taapp: Tiling Array Analysis Pipeline for Prokaryotes. Genomics Proteomics Bioinformatics 2011, 9(1–2):56–62. 10.1016/S1672-0229(11)60008-9View ArticlePubMedGoogle Scholar
- David L, Huber W, Granovskaia M, Toedling J, Palm CJ, Bofkin L, Jones T, Davis RW, Steinmetz LM: A High-Resolution Map of Transcription in the Yeast Genome. Proc Natl Acad Sci USA 2006, 103(14):5320–5. 10.1073/pnas.0601091103PubMed CentralView ArticlePubMedGoogle Scholar
- Mao F, Dam P, Chou J, Olman V, Xu Y: Door: A Database for Prokaryotic Operons. Nucleic Acids Res 2009, 37: D459–63. 10.1093/nar/gkn757PubMed CentralView ArticlePubMedGoogle Scholar
- Gioia J, Qin X, Jiang H, Clinkenbeard K, Lo R, Liu Y, Fox GE, Yerrapragada S, McLeod MP, McNeill TZ, et al.: The Genome Sequence of Mannheimia Haemolytica A1: Insights into Virulence, Natural Competence, and Pasteurellaceae Phylogeny. J Bacteriol 2006, 188(20):7257–66. 10.1128/JB.00675-06PubMed CentralView ArticlePubMedGoogle Scholar
- Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y: Rna-Seq: An Assessment of Technical Reproducibility and Comparison with Gene Expression Arrays. Genome Res 2008, 18(9):1509–17. 10.1101/gr.079558.108PubMed CentralView ArticlePubMedGoogle Scholar
- Li H, Durbin R: Fast and Accurate Long-Read Alignment with Burrows-Wheeler Transform. Bioinformatics 2010, 26(5):589–95. 10.1093/bioinformatics/btp698PubMed CentralView ArticlePubMedGoogle Scholar
- Li R, Li Y, Kristiansen K, Wang J: Soap: Short Oligonucleotide Alignment Program. Bioinformatics 2008, 24(5):713–4. 10.1093/bioinformatics/btn025View ArticlePubMedGoogle Scholar
- Frank S, Klockgether J, Hagendorf P, Geffers R, Schock U, Pohl T, Davenport CF, Tummler B: Pseudomonas Putida Kt2440 Genome Update by Cdna Sequencing and Microarray Transcriptomics. Environ Microbiol 2011, 13(5):1309–26. 10.1111/j.1462-2920.2011.02430.xView ArticlePubMedGoogle Scholar
- Perkins TT, Kingsley RA, Fookes MC, Gardner PP, James KD, Yu L, Assefa SA, He M, Croucher NJ, Pickard DJ, et al.: A Strand-Specific Rna-Seq Analysis of the Transcriptome of the Typhoid Bacillus Salmonella Typhi. PLoS Genet 2009, 5(7):e1000569. 10.1371/journal.pgen.1000569PubMed CentralView ArticlePubMedGoogle Scholar
- Croucher NJ, Fookes MC, Perkins TT, Turner DJ, Marguerat SB, Keane T, Quail MA, He M, Assefa S, Bahler J, et al.: A Simple Method for Directional Transcriptome Sequencing Using Illumina Technology. Nucleic Acids Res 2009, 37(22):e148. 10.1093/nar/gkp811PubMed CentralView ArticlePubMedGoogle Scholar
- Hernandez D, Francois P, Farinelli L, Osteras M, Schrenzel J: De Novo Bacterial Genome Sequencing: Millions of Very Short Reads Assembled on a Desktop Computer. Genome Res 2008, 18(5):802–9. 10.1101/gr.072033.107PubMed CentralView ArticlePubMedGoogle Scholar
- Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M: The Transcriptional Landscape of the Yeast Genome Defined by Rna Sequencing. Science 2008, 320(5881):1344–9. 10.1126/science.1158441PubMed CentralView ArticlePubMedGoogle Scholar
- Rutherford K, Parkhill J, Crook J, Horsnell T, Rice P, Rajandream MA, Barrell B: Artemis: Sequence Visualization and Annotation. Bioinformatics 2000, 16(10):944–5. 10.1093/bioinformatics/16.10.944View ArticlePubMedGoogle Scholar
- Romby P, Vandenesch F, Wagner EG: The Role of Rnas in the Regulation of Virulence-Gene Expression. Curr Opin Microbiol 2006, 9(2):229–36. 10.1016/j.mib.2006.02.005View ArticlePubMedGoogle Scholar
- Raghavan R, Groisman EA, Ochman H: Genome-Wide Detection of Novel Regulatory Rnas in E. Coli. Genome Res 2011, 21(9):1487–97. 10.1101/gr.119370.110PubMed CentralView ArticlePubMedGoogle Scholar
- Liu JM, Livny J, Lawrence MS, Kimball MD, Waldor MK, Camilli A: Experimental Discovery of Srnas in Vibrio Cholerae by Direct Cloning, 5s/Trna Depletion and Parallel Sequencing. Nucleic Acids Res 2009, 37(6):e46. 10.1093/nar/gkp080PubMed CentralView ArticlePubMedGoogle Scholar
- Repoila F, Darfeuille F: Small Regulatory Non-Coding Rnas in Bacteria: Physiology and Mechanistic Aspects. Biol Cell 2009, 101(2):117–31. 10.1042/BC20070137View ArticlePubMedGoogle Scholar
- Wassarman KM: 6s Rna: A Small Rna Regulator of Transcription. Curr Opin Microbiol 2007, 10(2):164–8. 10.1016/j.mib.2007.03.008View ArticlePubMedGoogle Scholar
- Argaman L, Hershberg R, Vogel J, Bejerano G, Wagner EG, Margalit H, Altuvia S: Novel Small Rna-Encoding Genes in the Intergenic Regions of Escherichia Coli . Curr Biol 2001, 11(12):941–50. 10.1016/S0960-9822(01)00270-6View ArticlePubMedGoogle Scholar
- Highlander SK: Molecular Genetic Analysis of Virulence in Mannheimia (Pasteurella) Haemolytica . Front Biosci 2001, 6: D1128–50. 10.2741/HighlandView ArticlePubMedGoogle Scholar
- Lo RY, McKerral LJ, Hills TL, Kostrzynska M: Analysis of the Capsule Biosynthetic Locus of Mannheimia (Pasteurella) Haemolytica A1 and Proposal of a Nomenclature System. Infect Immun 2001, 69(7):4458–64. 10.1128/IAI.69.7.4458-4464.2001PubMed CentralView ArticlePubMedGoogle Scholar
- Leite F, Gyles S, Atapattu D, Maheswaran SK, Czuprynski CJ: Prior Exposure to Mannheimia Haemolytica Leukotoxin or Lps Enhances Beta(2)-Integrin Expression by Bovine Neutrophils and Augments Lkt Cytotoxicity. Microb Pathog 2003, 34(6):267–75. 10.1016/S0882-4010(03)00060-3View ArticlePubMedGoogle Scholar
- Bakaletz LO, Baker BD, Jurcisek JA, Harrison A, Novotny LA, Bookwalter JE, Mungur R, Munson RS Jr: Demonstration of Type Iv Pilus Expression and a Twitching Phenotype by Haemophilus Influenzae . Infect Immun 2005, 73(3):1635–43. 10.1128/IAI.73.3.1635-1643.2005PubMed CentralView ArticlePubMedGoogle Scholar
- Coutte L, Alonso S, Reveneau N, Willery E, Quatannens B, Locht C, Jacob-Dubuisson F: Role of Adhesin Release for Mucosal Colonization by a Bacterial Pathogen. J Exp Med 2003, 197(6):735–42. 10.1084/jem.20021153PubMed CentralView ArticlePubMedGoogle Scholar
- Rashid Y, Kamran Azim M: Structural Bioinformatics of Neisseria Meningitidis Ld-Carboxypeptidase: Implications for Substrate Binding and Specificity. Protein J 2011, 30(8):558–65. 10.1007/s10930-011-9364-7View ArticlePubMedGoogle Scholar
- Du MZ, Guo FB, Chen YY: Gene Re-Annotation in Genome of the Extremophile Pyrobaculum Aerophilum by Using Bioinformatics Methods. J Biomol Struct Dyn 2011, 29(2):391–401. 10.1080/07391102.2011.10507393View ArticlePubMedGoogle Scholar
- Wood V, Rutherford KM, Ivens A, Rajandream MA, Barrell B: A Re-Annotation of the Saccharomyces Cerevisiae Genome. Comp Funct Genomics 2001, 2(3):143–54. 10.1002/cfg.86PubMed CentralView ArticlePubMedGoogle Scholar
- Gundogdu O, Bentley SD, Holden MT, Parkhill J, Dorrell N, Wren BW: Re-Annotation and Re-Analysis of the Campylobacter Jejuni Nctc11168 Genome Sequence. BMC Genomics 2007, 8: 162. 10.1186/1471-2164-8-162PubMed CentralView ArticlePubMedGoogle Scholar
- Chen LL, Ma BG, Gao N: Reannotation of Hypothetical Orfs in Plant Pathogen Erwinia Carotovora Subsp. Atroseptica Scri1043. FEBS J 2008, 275(1):198–206. 10.1111/j.1742-4658.2007.06190.xView ArticlePubMedGoogle Scholar
- Camus JC, Pryor MJ, Medigue C, Cole ST: Re-Annotation of the Genome Sequence of Mycobacterium Tuberculosis H37rv. Microbiology 2002, 148(Pt 10):2967–73.View ArticlePubMedGoogle Scholar
- Lamm CG, Love BC, Krehbiel CR, Johnson NJ, Step DL: Comparison of Antemortem Antimicrobial Treatment Regimens to Antimicrobial Susceptibility Patterns of Postmortem Lung Isolates from Feedlot Cattle with Bronchopneumonia. J Vet Diagn Invest 2012, 24(2):277–82. 10.1177/1040638711428149View ArticlePubMedGoogle Scholar
- Pichon C, Felden B: Small Rna Genes Expressed from Staphylococcus Aureus Genomic and Pathogenicity Islands with Specific Expression among Pathogenic Strains. Proc Natl Acad Sci USA 2005, 102(40):14249–54. 10.1073/pnas.0503838102PubMed CentralView ArticlePubMedGoogle Scholar
- Wilderman PJ, Sowa NA, FitzGerald DJ, FitzGerald PC, Gottesman S, Ochsner UA, Vasil ML: Identification of Tandem Duplicate Regulatory Small Rnas in Pseudomonas Aeruginosa Involved in Iron Homeostasis. Proc Natl Acad Sci USA 2004, 101(26):9792–7. 10.1073/pnas.0403423101PubMed CentralView ArticlePubMedGoogle Scholar
- Lenz DH, Mok KC, Lilley BN, Kulkarni RV, Wingreen NS, Bassler BL: The Small Rna Chaperone Hfq and Multiple Small Rnas Control Quorum Sensing in Vibrio Harveyi and Vibrio Cholerae . Cell 2004, 118(1):69–82. 10.1016/j.cell.2004.06.009View ArticlePubMedGoogle Scholar
- Lenz DH, Miller MB, Zhu J, Kulkarni RV, Bassler BL: Csra and Three Redundant Small Rnas Regulate Quorum Sensing in Vibrio Cholerae . Mol Microbiol 2005, 58(4):1186–202. 10.1111/j.1365-2958.2005.04902.xView ArticlePubMedGoogle Scholar
- Small Non-Coding Regulatory Rna Database[http://bioinfo.mikrobio.med.uni-giessen.de/sRNAdb/Blast]
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