Computational Small RNA Prediction in Bacteria

Sridhar, Jayavel; Gunasekaran, Paramasamy

doi:10.4137/bbi.s11213

Cited by 36 publications

(35 citation statements)

References 62 publications

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“…Our interest in complementing our experimental search with such approaches stemmed from the fact that even though RNA sequencing is a powerful transcriptome analysis technique, it can only capture transcripts expressed during the particular experimental condition under which cells are collected for RNA preparation. It is therefore not surprising that computational predictions have also become widely used for the discovery of small regulatory RNAs in bacteria (14,21). We performed two specific computational prediction approaches to identify novel sRNA candidates in Z. mobilis: (i) SIPHT (sRNA identification protocol using high-throughput technologies) (38) and (ii) a bioinformatics analysis recently developed in our laboratory (unpublished data) based on the search of long and conserved intergenic regions.…”

Section: Transcriptome Analysis Of Zymomonas Mobilis For Identifying mentioning

confidence: 99%

“…When cells encounter environmental changes, regulatory sRNAs help to modulate gene expression by optimizing cellular metabolism for survival. Our motivation in this work was rooted by the ubiquitous discovery and validation of these regulatory elements in bacteria using many computational and experimental strategies (14,(21)(22)(23). Interestingly, recent data have shown higher expression of Hfq under anaerobic conditions in Z. mobilis, with higher ethanol production than under aerobic conditions (4).…”

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Discovery of Ethanol-Responsive Small RNAs in Zymomonas mobilis

Cho

Lei

Henninger

et al. 2014

Appl Environ Microbiol

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bZymomonas mobilis is a bacterium that can produce ethanol by fermentation. Due to its unique metabolism and efficient ethanol production, Z. mobilis has attracted special interest for biofuel energy applications; an important area of study is the regulation of those specific metabolic pathways. Small RNAs (sRNAs) have been studied as molecules that function as transcriptional regulators in response to cellular stresses. While sRNAs have been discovered in various organisms by computational prediction and experimental approaches, their discovery in Z. mobilis has not yet been reported. In this study, we have applied transcriptome analysis and computational predictions to facilitate identification and validation of 15 novel sRNAs in Z. mobilis. We furthermore characterized their expression in the context of high and low levels of intracellular ethanol. Here, we report that 3 of the sRNAs (Zms2, Zms4, and Zms6) are differentially expressed under aerobic and anaerobic conditions, when low and high ethanol productions are observed, respectively. Importantly, when we tested the effect of ethanol stress on the expression of sRNAs in Z. mobilis, Zms2, Zms6, and Zms18 showed differential expression under 5% ethanol stress conditions. These data suggest that in this organism regulatory RNAs can be associated with metabolic functions involved in ethanol stress responses.

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Section: Transcriptome Analysis Of Zymomonas Mobilis For Identifying mentioning

confidence: 99%

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confidence: 99%

Discovery of Ethanol-Responsive Small RNAs in Zymomonas mobilis

Cho

Lei

Henninger

et al. 2014

Appl Environ Microbiol

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“…However, the experimental verification of a relatively small number of sRNAs, primarily in Escherichia coli K12 (Argaman et al, 2001;Rivas, 2005) and Salmonella enterica serovar Typhimurium LT2 (SLT2) (Padalon-Brauch et al, 2008;Pfeiffer et al, 2007;Sittka et al, 2009;Vogel, 2009), is not sufficient to help detect them in the large list of available sequenced genomes. Therefore, there is a requirement of developing computational algorithms capable of robustly encoding the knowledge of known sRNAs available in certain genomes and using this knowledge to predict sRNAs in other species (Sridhar and Gunasekaran, 2013).…”

Section: Introductionmentioning

confidence: 99%

A multiobjective method for robust identification of bacterial small non-coding RNAs

et al. 2014

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Motivation: Small non-coding RNAs (sRNAs) have major roles in the post-transcriptional regulation in prokaryotes. The experimental validation of a relatively small number of sRNAs in few species requires developing computational algorithms capable of robustly encoding the available knowledge and using this knowledge to predict sRNAs within and across species.Results: We present a novel methodology designed to identify bacterial sRNAs by incorporating the knowledge encoded by different sRNA prediction methods and optimally aggregating them as potential predictors. Because some of these methods emphasize specificity, whereas others emphasize sensitivity while detecting sRNAs, their optimal aggregation constitutes trade-off solutions between these two contradictory objectives that enhance their individual merits. Many non-redundant optimal aggregations uncovered by using multiobjective optimization techniques are then combined into a multiclassifier, which ensures robustness during detection and prediction even in genomes with distinct nucleotide composition. By training with sRNAs in Salmonella enterica Typhimurium, we were able to successfully predict sRNAs in Sinorhizobium meliloti, as well as in multiple and poorly annotated species. The proposed methodology, like a meta-analysis approach, may begin to lay a possible foundation for developing robust predictive methods across a wide spectrum of genomic variability. Availability and implementation: Scripts created for the experimentation are available at

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“…Aided by sRNA prediction algorithms, these large data sets are paving the way for continual sRNA discovery (12,18,19). However, sRNA validation as well as determination of mechanistic function remains elusive.…”

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Genome-Wide Analyses in Bacteria Show Small-RNA Enrichment for Long and Conserved Intergenic Regions

et al. 2015

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Interest in finding small RNAs (sRNAs) in bacteria has significantly increased in recent years due to their regulatory functions. Development of high-throughput methods and more sophisticated computational algorithms has allowed rapid identification of sRNA candidates in different species. However, given their various sizes (50 to 500 nucleotides [nt]) and their potential genomic locations in the 5= and 3= untranslated regions as well as in intergenic regions, identification and validation of true sRNAs have been challenging. In addition, the evolution of bacterial sRNAs across different species continues to be puzzling, given that they can exert similar functions with various sequences and structures. In this study, we analyzed the enrichment patterns of sRNAs in 13 well-annotated bacterial species using existing transcriptome and experimental data. All intergenic regions were analyzed by WU-BLAST to examine conservation levels relative to species within or outside their genus. In total, more than 900 validated bacterial sRNAs and 23,000 intergenic regions were analyzed. The results indicate that sRNAs are enriched in intergenic regions, which are longer and more conserved than the average intergenic regions in the corresponding bacterial genome. We also found that sRNA-coding regions have different conservation levels relative to their flanking regions. This work provides a way to analyze how noncoding RNAs are distributed in bacterial genomes and also shows conserved features of intergenic regions that encode sRNAs. These results also provide insight into the functions of regions surrounding sRNAs and into optimization of RNA search algorithms. R ecently, small noncoding RNAs (sRNAs) have been under closer scrutiny as mediators and regulators of gene expression (1-5). This class of RNAs has been found to play a variety of roles in important cell functions (6, 7). Typically composed of 50 to 500 nucleotides, sRNAs are known to control plasmid replication, bacterial virulence, and various stress responses (8-11).An interesting aspect of sRNAs is the wide diversity of their functional mechanisms. sRNAs can repress or stimulate gene expression posttranscriptionally by pairing their targets through base complementarity; a target can be, but is not limited to, an mRNA or a protein. sRNAs that regulate other RNAs can be cis encoded or trans encoded. A cis-encoded sRNA is typically encoded adjacent to its regulatory target on the same strand as a riboswitch or on the opposite strand to an antisense sRNA. In most cases, they will base pair to their targets or change the secondary structure to inhibit ribosome binding (12)(13)(14). In contrast, a trans-encoded sRNA is encoded away from its target, has a lower base complementarity to its target, and can potentially bind multiple targets (15).With advances in high-throughput sequencing technologies (16), it is now possible to sequence gigabases of nucleotides in a matter of hours (17). Aided by sRNA prediction algorithms, these large data sets are paving the way for cont...

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Computational Small RNA Prediction in Bacteria

Cited by 36 publications

References 62 publications

Discovery of Ethanol-Responsive Small RNAs in Zymomonas mobilis

Discovery of Ethanol-Responsive Small RNAs in Zymomonas mobilis

A multiobjective method for robust identification of bacterial small non-coding RNAs

Genome-Wide Analyses in Bacteria Show Small-RNA Enrichment for Long and Conserved Intergenic Regions

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