Identification of small non-coding RNAs from Rhizobium etli by integrated genome-wide and transcriptome-based methods

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.

Identification of sigma factor 54-regulated small non-coding RNAs by employing genome-wide and transcriptome-based methods in rhizobium strains

“…Transcripts with true non-coding nature were considered for further annotation of sRNA. Length and GC content of the putative non-coding transcripts were analyzed using customized PERL script as described in our previous publications (Raja et al, 2018;Rajendran et al, 2020). In order to re ne the data, other regulatory RNAs which are predicted along with the sRNAs, were eliminated by searching against Rfam database.…”

“…The genome of this strain harbours chromosome (3.84 Mb) and three plasmids pRtrCIAT899a (0. Rajendran et al, 2020). However, there is scanty information on the conditional speci c sRNAs and its regulation.…”

Microbial Small RNAs – The Missing Link in the Nitrogen Cycle?

“…Advances in computational biology such as comparative genomics, RNA structure and thermodynamic stability-based approaches as well as transcriptional signal-based sRNA identification have contributed to the identification of a plethora of sRNAs (Sridhar and Gunasekaran, 2013). Though the majority of sRNAs have been identified using comparative genomics, transcriptional signal-based approaches are promising in the discovery of novel intergenic sRNAs (Rajendran et al, 2020). The breadth of bacterial and archaeal species with fully sequenced genomes and pre-existing whole transcriptome studies may well also contribute to the identification of novel sRNA.…”

Identification and New Indication of Melanin-Concentrating Hormone Receptor 1 (MCHR1) Antagonist Derived from Machine Learning and Transcriptome-Based Drug Repositioning Approaches

“…A transcriptome-based method was used to select targets of the screened molecules. As the gene expression by the administered drug differs depending on the experimental group, it is possible to find genes that are significantly expressed through a differentially expressed gene (DEG) analysis in comparison to that of the control group [ 15 , 16 , 17 ]. Through Gene Ontology (GO) [ 18 ] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [ 19 ] enrichment analyses, the biological functions and mechanisms of target proteins were identified.…”