RNA-Seq Based Transcriptional Map of Bovine Respiratory Disease Pathogen “Histophilus somni 2336”

Abstract

Genome structural annotation, i.e., identification and demarcation of the boundaries for all the functional elements in a genome (e.g., genes, non-coding RNAs, proteins and regulatory elements), is a prerequisite for systems level analysis. Current genome annotation programs do not identify all of the functional elements of the genome, especially small non-coding RNAs (sRNAs). Whole genome transcriptome analysis is a complementary method to identify “novel” genes, small RNAs, regulatory regions, and operon structures, thus improving the structural annotation in bacteria. In particular, the identification of non-coding RNAs has revealed their widespread occurrence and functional importance in gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Histophilus somni, one of the causative agents of Bovine Respiratory Disease (BRD) as well as bovine infertility, abortion, septicemia, arthritis, myocarditis, and thrombotic meningoencephalitis. In this study, we report a single nucleotide resolution transcriptome map of H. somni strain 2336 using RNA-Seq method.The RNA-Seq based transcriptome map identified 94 sRNAs in the H. somni genome of which 82 sRNAs were never predicted or reported in earlier studies. We also identified 38 novel potential protein coding open reading frames that were absent in the current genome annotation. The transcriptome map allowed the identification of 278 operon (total 730 genes) structures in the genome. When compared with the genome sequence of a non-virulent strain 129Pt, a disproportionate number of sRNAs (∼30%) were located in genomic region unique to strain 2336 (∼18% of the total genome). This observation suggests that a number of the newly identified sRNAs in strain 2336 may be involved in strain-specific adaptations.

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

“…Therefore, robust methodological strategy will accelerate transcriptome studies by inferring more accurate spliced transcript variability and tissue specific (or condition specific) isoform diversity as well as allelic genetic differences in tissues (Chan et al, 2002;Li and Dickson, 1997;Robakis and Georgakopoulos, 2014). Thus, we here have a greatly important take-home message to be addressed in the upcoming years as the future direction, the focus on gene level analysis might be a partial analytical technique in transcriptional regulatory mechanisms, especially, when both is related with disease progression and therapeutic effects as mentioned in earlier studies (Gerns Storey et al, 2014;Ginsberg et al, 2010;Han and Jiang, 2014;Kim et al, 2012;Kumar et al, 2012;Li et al, 2014;Mills et al, 2013;Nishiu et al, 2002;Satoh et al, 2014;Wang et al, 2013;Wang et al, 2014;Yalamanchili et al, 2014). Accordingly, followed by differential gene expression analysis, differential expression at transcripts, exons, and isoforms, and allelic specific expression under the particular tissues (conditions) will be a next stage in the ultra-high-throughput community.…”

How are Bayesian and Non-Parametric Methods Doing a Great Job in RNA-Seq Differential Expression Analysis? : A Review

“…In addition, the sample size in RNA-seq is much fewer than the number of variables (genes) (Anders et al, 2012;Beretta et al, 2014;Bernard et al, 2014;Bi and Davuluri, 2013;Bullard et al, 2010;Deng et al, 2011;Glaus et al, 2012;Han and Jiang, 2014;Hiller et al, 2009;Hiller and Wong, 2013;Howard and Heber, 2010;Hu et al, 2014;Jiang and Wong, 2009;Kaur et al, 2012;Kim et al, 2012;Kimes et al, 2014;Kumar et al, 2012;Lee et al, 2011;Leon-Novelo et al, 2014;Lerch et al, 2012;Li et al, 2014;Li et al, 2011;Li and Jiang, 2012;Ma and Zhang, 2013;Marioni et al, 2008;Mezlini et al, 2013;Mills et al, 2013;Mortazavi et al, 2008;Nariai et al, 2013;Nariai et al, 2014;Nicolae et al, 2011;Niu et al, 2014;Oh et al, 2013;Oshlack et al, 2010;Pandey et al, 2013;Patro et al, 2014;Pollier et al, 2013;Rehrauer et al, 2013;Roberts et al, 2011;Robinson and Oshlack, 2010;Ryan et al, 2012;Safikhani et al, 2013;<...>…”

“…With the advent of new elegant count based technology referred to as RNA-seq, newly developed RNA-seq specific methods and adaptively implemented ones from microarrays have been proposed and successfully contributed to transcriptome studies (Gerns Storey et al, 2014;Ginsberg et al, 2010;Han and Jiang, 2014;Kim et al, 2012;Kumar et al, 2012;Li et al, 2014;Mills et al, 2013;Nishiu et al, 2002;Satoh et al, 2014;Wang et al, 2013;Warren et al, 2015;Zhang et al, 2014). Representative statistical methods have been popularly utilized to quantify expression levels of mRNA abundance and identify differentially expressed genes between multiple conditions (Anders and Huber, 2010;Anders et al, 2013;Anders et al, 2012;Bar-Joseph et al, 2012;Bi and Davuluri, 2013;Bullard et al, 2010;Glaus et al, 2012;Hardcastle and Kelly, 2010;Lee et al, 2011;Marioni et al, 2008;Niu et al, 2014;Oh et al, 2013;Oshlack et al, 2010;Pollier et al, 2013;Roberts et al, 2011;Robinson and Oshlack, 2010;Tarazona et al, 2011;Trapnell et al, 2009;Trapnell et al, 2012;Young et al, 2010).…”

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

“…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.…”