Article

PsRNA: a computing engine for the comparative identification of putative small RNA locations within intergenic regions.

Centre of Excellence in Bioinformatics, School of Biotechnology, Madurai Kamaraj University, Madurai, India.
Genomics Proteomics & Bioinformatics 06/2010; 8(2):127-34. DOI: 10.1016/S1672-0229(10)60014-9
Source: PubMed

ABSTRACT 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/.

0 Bookmarks
 · 
146 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Bacterial, small RNAs were once regarded as potent regulators of gene expression and are now being considered as essential for their diversified roles. Many small RNAs are now reported to have a wide array of regulatory functions, ranging from environmental sensing to pathogenesis. Traditionally, noncoding transcripts were rarely detected by means of genetic screens. However, the availability of approximately 2200 prokaryotic genome sequences in public databases facilitates the efficient computational search of those molecules, followed by experimental validation. In principle, the following four major computational methods were applied for the prediction of sRNA locations from bacterial genome sequences: (1) comparative genomics, (2) secondary structure and thermodynamic stability, (3) 'Orphan' transcriptional signals and (4) ab initio methods regardless of sequence or structure similarity; most of these tools were applied to locate the putative genomic sRNA locations followed by experimental validation of those transcripts. Therefore, computational screening has simplified the sRNA identification process in bacteria. In this review, a plethora of small RNA prediction methods and tools that have been reported in the past decade are discussed comprehensively and assessed based on their attributes, compatibility, and their prediction accuracy.
    Bioinformatics and biology insights 01/2013; 7:83-95.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Non-coding functional elements recently have proved to play vital functions in eukaryotes worthy ignoring in the prokaryotic counter parts. We argue that elucidating these non-coding elements will lead to a better understanding of the differences coexisting between the Streptococci species, an important human pathogen responsible for vast diseases devastating the economy. The study analyzed 56 strains from NCBI, where 44,523 sequences were extracted by PERL script. Similarity search by Blast, Genemark and CPC revealed a total of 1443 potential sequences preceding functional studies by deploying Pfam, InterproScan and COG depicting 144 proteins allowing them to be designated as novel ones. The results potentially could be used as new vaccine targets, better understanding of the different biological niches; re-annotation also can be looked unto for potential genomic island identification and further wet-lab extensions research work for better understanding of the Streptococci family for further directions and incorporation of the identified proteins into database.
    International Journal of Pharma and Bio Sciences. 10/2013; 4(4):B 923.

Full-text (2 Sources)

View
11 Downloads
Available from
Jun 4, 2014