RESUMO
Cyprinus carpio is regarded as a substitute vertebrate fish model for zebrafish. A varied category of non-coding RNAs is comprised of long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). These ncRNAs were once considered non-functional "junk DNA" but research now shows they play important roles in gene expression regulation, chromatin modification, and epigenetic regulation. The systemic tissue-specific research of the lncRNAs and circRNAs of C. carpio is yet unexplored. A total of 468 raw RNA-Seq dataset across 28 distinct tissues from different varieties of common carp retrieved from public domain were pre-processing, mapped and assembled for lncRNA identification/ classification using various bioinformatics tools. A total of 33,990 lncRNAs were identified along with revelation of 9 miRNAs having 19 unique lncRNAs acting as their precursors. Additionally, 2,837 miRNAs were found to target 4,782 distinct lncRNAs in the lncRNA-miRNA-mRNA interaction network analysis, which resulted in the involvement of 3,718 mRNAs in common carp. A total of 22,854 circRNAs were identified tissue-wise across all the 28 tissues. Moreover, the examination of the circRNA-miRNA-mRNA interaction network revealed that 15,731 circRNAs were targeted by 5,906 distinct miRNAs, which in turn targeted 4,524 mRNAs in common carp. Significant signaling pathways like necroptosis, NOD-like receptor signaling pathway, hypertrophic cardiomyopathy, small cell lung cancer, MAPK signaling pathway, etc. were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The web resource of common carp ncRNAs, named CCncRNAdb and available at http://backlin.cabgrid.res.in/ccncrnadb/ gives a comprehensive information about common carp lncRNAs, circRNAs, and ceRNAs interactions, which can aid in investigating their functional roles for its management.
RESUMO
CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In this paper, an effort has been made to create models based on three machine learning-based techniques [namely, artificial neural networks (ANN), support vector machines (SVM), and random forests (RF)] for the prediction of the CRISPR-Cas9 cleavage sites that will be cleaved by a particular sgRNA. The plant dataset was the sole source of inspiration for all of these machine learning-based algorithms. 70% of the on-target and off-target dataset of various plant species that was gathered was used to train the models. The remaining 30% of the data set was used to evaluate the model's performance using a variety of evaluation metrics, including specificity, sensitivity, accuracy, precision, F1 score, F2 score, and AUC. Based on the aforementioned machine learning techniques, eleven models in all were developed. Comparative analysis of these produced models suggests that the model based on the random forest technique performs better. The accuracy of the Random Forest model is 96.27%, while the AUC value was found to be 99.21%. The SVM-Linear, SVM-Polynomial, SVM-Gaussian, and SVM-Sigmoid models were trained, making a total of six ANN-based models (ANN1-Logistic, ANN1-Tanh, ANN1-ReLU, ANN2-Logistic, ANN2-Tanh, and ANN-ReLU) and Support Vector Machine models (SVM-Linear, SVM-Polynomial, SVM-Gaussian However, the overall performance of Random Forest is better among all other ML techniques. ANN1-ReLU and SVM-Linear model performance were shown to be better among Artificial Neural Network and Support Vector Machine-based models, respectively.
