Identification of small non-coding RNAs from Rhizobium etli by integrated genome-wide and transcriptome-based methods
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Rajendran et al. ExRNA (2020) 2:14 https://doi.org/10.1186/s41544-020-00054-1
RESEARCH
Open Access
Identification of small non-coding RNAs from Rhizobium etli by integrated genomewide and transcriptome-based methods Kasthuri Rajendran1, Vikram Kumar2, Ilamathi Raja1, Manoharan Kumariah1 and Jebasingh Tennyson2*
Abstract Background: Small non-coding RNAs (sRNAs) are regulatory molecules, present in all forms of life, known to regulate various biological processes in response to the different environmental signals. In recent years, deep sequencing and various other computational prediction methods have been employed to identify and analyze sRNAs. Results: In the present study, we have applied an improved sRNA scanner method to predict sRNAs from the genome of Rhizobium etli, based on PWM matrix of conditional sigma factor 32. sRNAs predicted from the genome are integrated with the available stress specific transcriptome data to predict putative conditional specific sRNAs. A total of 271 sRNAs from the genome and 173 sRNAs from the transcriptome are computationally predicted. Of these, 25 sRNAs are found in both genome and transcriptome data. Putative targets for these sRNAs are predicted using TargetRNA2 and these targets are involved in a wide array of cellular functions such as cell division, transport and metabolism of amino acids, carbohydrates, energy production and conversion, translation, cell wall/membrane biogenesis, posttranslation modification, protein turnover and chaperones. Predicted targets are functionally classified based on COG analysis and GO annotations. Conclusion: sRNAs predicted from the genome, using PWM matrices for conditional sigma factor 32 could be a better method to identify the conditional specific sRNAs which expand the list of putative sRNAs from the intergenic regions (IgRs) of R. etli and closely related α-proteobacteria. sRNAs identified in this study would be helpful to explore their regulatory role in biological cellular process during the stress. Keywords: sRNA, Rhizobium etli, Sigma factor 32, Genome, Transcriptome
Background Small non-coding RNAs are bacterial regulatory molecules, 50-500 nt (bp) in length and contain several stem loops. sRNAs are often located in the intergenic regions, transcribed from their own promoter or promoters of nearby genes and contain rho-independent terminator. sRNAs regulate the gene expression by perfect or imperfect base pairing with complementary sequence stretches, generally located in 5′-UTR regions of transencoded target mRNAs, resulting in altered target * Correspondence: [email protected] 2 Department of Plant Sciences, School of Biological Sciences, Madurai Kamaraj University, Madurai, Tamil Nadu 625 021, India Full list of author information is available at the end of the article
mRNA translation and stability [1–3]. The regulation of sRNAs are mediated with the help of chaperone Hfq, enhance RNA-RNA interaction, through the preferential binding at single-stranded AU-rich regions of the noncoding RNAs and their target mRNAs [4]
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