Title : Genome - wide prediction of MicroRNAs and long non-coding RNAs and their interaction in Hordeum vulgare
Abstract:
Barley (Hordeum vulgare) is an important cereal crop for its dietary value with rich source of proteins, lipids, carbohydrates, dietary fibres, flavonoids and unsaturated fatty acids. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two non-coding RNAs which play important role in Post-Transcriptional Gene Regulation (PTGR). Plant miRNAs are small ncRNAs which control various post-transcriptional and translational processes in the form of either repression or cleavage of the target. Long non-coding RNAs (lncRNAs) are responsible for up-regulation and down-regulation of transcription. In spite of the availability of the genome information of barley, very less study has been done so far on its non-coding genome. In this study, 870 miRNAs have been predicted by computational methods among which 451 are novel. These miRNAs belong to 137 different families. We have also identified 496 targets of 220 miRNAs along with their functional annotation. Besides, we have predicted 1567 lncRNAs, of which 67 are targeted by 45 novel miRNAs. We have also experimentally validated five randomly chosen miRNAs. Through this study, we have predicted new miRNAs and lncRNAs along with their targets to elucidate their roles in various biological pathways. Our analysis provides information about the non-coding genome of barley and their roles in PTGR, which may be used to improve the agricultural traits of this economically important crop.
Audience Takeaway:
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I will explain the computational predictions of both miRNAs and lncRNAs along with experimental validation in this presentation. The audience can use the same prediction pipeline for the prediction of miRNAs and lncRNAs.
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There are only handful number of miRNAs present in miRBase. We only validated few miRNAs from the pool. Other faculties can experimentally validate the other miRNAs and find their function in the PTGR.
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The algorithm provided for the computational prediction of miRNAs and lncRNAs can be used by the other faculties and this will simplify their prediction methods.