Small RNA Identification in<i>Enterobacteriaceae</i>Using Synteny and Genomic Backbone Retention

Sridhar, Jayavel; Rafi, Ziauddin Ahamed

doi:10.1089/omi.2006.0006

Cited by 10 publications

(14 citation statements)

References 46 publications

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“…Comparative genomic approaches are restricted to identifying sRNA candidates located within conserved genomic backbone regions common to closely related bacteria [26]. However, most bacterial species have significant cumulative spans of multiple strain-specific sequences or islands, dispersed along the genome, many of which play key adaptive and/or pathogenesis-related roles [27], [28].…”

Section: Introductionmentioning

confidence: 99%

sRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes

et al. 2010

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BackgroundBacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement.Methodology/Principal FindingsHere we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain (Sittka et al. (2008) PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5′-ends of these six Northern-supported sRNA candidates were successfully mapped using 5′-RACE analysis.Conclusions/SignificanceWe have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that ∼40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.

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Section: Introductionmentioning

confidence: 99%

sRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes

et al. 2010

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“…The highest numbers of the homologous and non-homologous sRNA locations are identified in Enterobacteriaceae genomes 11 , 21 . Among them, significant number of sRNAs are identified and studied in E. coli K12-MG1655 20 , 21 .…”

Section: Applicationmentioning

confidence: 99%

“…Such comparison with the predictions made by Rfam approach confirms the reliability of PsRNA server in predicting the functionally important sRNA regions in bacterial genomes using KO terms. Above computational approaches simply look for possible conserved secondary structures and predict some of the mRNA regions (CDS) as sRNA regions false positively ( 11 ) . But the proposed approach predicts the putative intergenic sRNA regions alone.…”

Section: Applicationmentioning

confidence: 99%

“…Recently, accumulated sRNA data in various public databases like Rfam ( 7 ) , NONCODE ( 8 ) , KEGG ( 9 ) and GenBank ( 10 ) open a possibility of new context based prediction methods. An analysis of the existing sRNA data from Enterobacteriaceae family indicates that 21 different sRNA groups do not show sequence homology within closely related genomes ( 11 ) . However, such homologous and non-homologous sRNA regions of a specific sRNA group can be identified using their specific conserved flanking genes 12 , 13 .…”

Section: Introductionmentioning

confidence: 99%

“…Using KO terms, the prediction server has been designed to locate potentially important putative intergenic regions for prokaryotic sRNAs. In order to remove false positives, simultaneous occurrence of the orthologous sRNA specific flanking gene pairs that follow gene synteny and genomic backbone retention rule in query genomes ( 11 ) is reported. The co-existence of the flanking genes assigned with KO numbers is being re-analyzed by their gene locus numbers, and pairs found beyond the limit of five genes are excluded to minimize the false positives.…”

Section: Introductionmentioning

confidence: 99%

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PsRNA: A Computing Engine for the Comparative Identification of Putative Small RNA Locations within Intergenic Regions

Sridhar

Sowmiya

Sekar

et al. 2010

Genomics, Proteomics &Amp; Bioinformatics

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Small RNAs (sRNAs) are non-coding transcripts exerting their functions in the cells directly. Identification of sRNAs is a difficult task due to the lack of clear sequence and structural biases. Most sRNAs are identified within genus specific intergenic regions in related genomes. However, several of these regions remain un-annotated due to lack of sequence homology and/or potent statistical identification tools. A computational engine has been built to search within the intergenic regions to identify and roughly annotate new putative sRNA regions in Enterobacteriaceae genomes. It utilizes experimentally known sRNA data and their flanking genes/KEGG Orthology (KO) numbers as templates to identify similar sRNA regions in related query genomes. The search engine not only has the capability to locate putative intergenic regions for specific sRNAs, but also has the potency to locate conserved, shuffled or deleted gene clusters in query genomes. Because it uses the KO terms for locating functionally important regions such as sRNAs, any further KO number assignment to additional genes will increase the sensitivity. The PsRNA server is used for the identification of putative sRNA regions through the information retrieved from the sRNA of interest. The computing engine is available online at http://bioserver1.physics.iisc.ernet.in/psrna/ and http://bicmku.in:8081/psrna/.

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Noncoding RNAs, Neurodevelopment, and Neurodegeneration

Chen

Deng

Yang

et al. 2013

Posttranscriptional Gene Regulation

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Small RNA Identification inEnterobacteriaceaeUsing Synteny and Genomic Backbone Retention

Cited by 10 publications

References 46 publications

sRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes

sRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes

PsRNA: A Computing Engine for the Comparative Identification of Putative Small RNA Locations within Intergenic Regions

Noncoding RNAs, Neurodevelopment, and Neurodegeneration

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