bioRxivGenomicsPreprint
Genome-wide mining and comparative analysis of microsatellite markers from Orientia tsutsugamushi genomes
Subhasmita PandaiD, Subrat Kumar SwainiD, Basanta Pravas SahuiD, Rachita SarangiiD
Abstract
Microsatellite markers, otherwise known as the simple sequence repeats (SSRs), are being used for molecular identification and characterization as well as estimation of evolution pattern of the organism due to their high polymorphic nature. These are tandemly repeated sequences observed almost all organisms and differentially distributed across the genome. Although the primary genome information of Orientia tsutsugamushi (OT) suggested the repeats hold the 40% entire of its genome, but lack of characteristic of this repeats increase our interest to study more about it. Thus we investigated a genome-wide presence of microsatellites within nine complete genomes within OT and analyzed their distribution pattern, composition and complexity. The in-silico study revealed the genome of OT enrich with microsatellites having a total of 126187 SSR and 10374 cSSR throughout the genome from which 70% and 30% represented within the coding and non coding region respectively. The relative density (RD) and relative abundance (RA) of SSRs were 42-44.43/kb and 6.25-6.59/kb while for cSSRs this value ranged from 7.06-8.1/kb and 0.50-0.55/kb respectively. However, RA and RD were weakly correlate with genome size and incidence microsatellites. The mononucleotide repeats (54.55%) were prevalent over di- (33.22%), tri- (11.88%), tetra- (0.27%), penta- (0.02%), hexanucleotide (0.04%) repeats, with poly (A/T) richness over poly (G/C). Motif composition of cSSRs revealed that maximum cSSRs were made up of two microsatellites having unique duplication pattern such as AT-x-AT, CG-x-CG. More numbers microsatellites represented within the coding region provides an insight into the genome plasticity that may interfere for gene regulation to mitigate with host-pathogen interaction and evolution of the species.
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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