Uziflies (Family: Tachinidae) are dipteran endoparasites of sericigenous insects which cause major economic loss in the silk industry globally. Here, we are presenting the first full mitogenome of Blepharipa sp. (Acc: KY644698, 15,080 bp, A + T = 78.41%), a dipteran parasitoid of Muga silkworm (Antheraea assamensis) found in the Indian states of Assam and Meghalaya. This study has confirmed that Blepharipa sp. mitogenome gene content and arrangement is similar to other Tachinidae and Sarcophagidae flies of Oestroidea superfamily, typical of ancestral Diptera. Although, Calliphoridae and Oestridae flies have undergone tRNA translocation and insertion, forming unique intergenic spacers (IGS) and overlapping regions (OL) and a few of them (IGS, OL) have been conserved across Oestroidea flies. The Tachinidae mitogenomes exhibit more AT content and AT biased codons in their protein-coding genes (PCGs) than the Oestroidea counterpart. About 92.07% of all (3722) codons in PCGs of this new species have A/T in their 3rd codon position. The high proportion of AT and repeats in the control region (CR) affects sequence coverage, resulting in a short CR (Blepharipa sp.: 168 bp) and a smaller tachinid mitogenome. Our research unveils those genes with a high AT content had a reduced effective number of codons, leading to high codon usage bias. The neutrality test shows that natural selection has a stronger influence on codon usage bias than directed mutational pressure. This study also reveals that longer PCGs (e.g., nad5, cox1) have a higher codon usage bias than shorter PCGs (e.g., atp8, nad4l). The divergence rates increase nonlinearly as AT content at the 3rd codon position increases and higher rate of synonymous divergence than nonsynonymous divergence causes strong purifying selection. The phylogenetic analysis explains that Blepharipa sp. is well suited in the family of insectivorous tachinid maggots. It's possible that biased codon usage in the Tachinidae family reduces the effective number of codons, and purifying selection retains the core functions in their mitogenome, which could help with efficient metabolism in their endo-parasitic life style and survival strategy.

Pre-MicroRNAs are the hairpin loops which produces microRNAs that negatively regulate gene expression in several organisms. In insects, microRNAs participate in several biological processes including metamorphosis, reproduction, immune response, etc. Numerous tools have been designed in recent years to predict pre-microRNA using binary machine learning classifiers where predictive models are trained with true and pseudo pre-microRNA hairpin loops. Currently, there is no tool that is exclusively designed for insect pre-microRNA detection. In this experiment we trained machine learning classifiers such as Random Forest, Support Vector Machine, Logistic Regression and k-Nearest Neighbours to predict pre-microRNA hairpin loops in insects while using Synthetic Minority Over-sampling Technique and Near-Miss to handle the class imbalance. The trained model on Support Vector Machine achieved accuracy of 92.19% while the Random Forest attained an accuracy of 80.28% on our validation dataset. These models are hosted online as web application called RNAinsecta. Further, searching target for the predicted pre-microRNA in insect model organism Drosophila melanogaster has been provided in RNAinsecta using miRanda at the backend where experimentally validated genes regulated by microRNA are collected from miRTarBase as target sites. RNAinsecta is freely available at https://rnainsecta.in. GitHub: https://github.com/adhiraj141092/RNAinsecta

Pre-MicroRNAs are the hairpin loops which produces microRNAs that negatively regulate gene expression in several organisms. In insects, microRNAs participate in several biological processes including metamorphosis, reproduction, immune response, etc. Numerous tools have been designed in recent years to predict pre-microRNA using binary machine learning classifiers where predictive models are trained with true and pseudo pre-microRNA hairpin loops. Currently however, there are no existing tool that is exclusively designed for insect pre-microRNA detection. In this experiment we trained machine learning classifiers such as Random Forest, Support Vector Machine, Logistic Regression and k-Nearest Neighbours to predict pre-microRNA hairpin loops in insects while using Synthetic Minority Over-sampling Technique and Near-Miss to handle the class imbalance. The trained model on Support Vector Machine achieved accuracy of 92.19% while the Random Forest attained an accuracy of 80.28% on our validation dataset. These models are hosted online as web application called RNAinsecta. Further, searching target for the predicted pre-microRNA in insect model organism Drosophila melanogaster has been provided in RNAinsecta using miRanda at the backend where experimentally validated genes regulated by microRNA are collected from miRTarBase as target sites. RNAinsecta is freely available at https://rnainsecta.in .