Pan-Cancer Analysis Reveals Common and Specific Relationships between Intragenic miRNAs and Their Host Genes

MicroRNAs (miRNAs) are small endogenous non-coding RNAs that play important roles in regulating gene expression. Most miRNAs are located within or close to genes (host). miRNAs and their host genes have either coordinated or independent transcription. We performed a comprehensive investigation on co-transcriptional patterns of miRNAs and host genes based on 4707 patients across 21 cancer types. We found that only 11.6% of miRNA-host pairs were co-transcribed consistently and strongly across cancer types. Most miRNA-host pairs showed a strong coexpression only in some specific cancer types, demonstrating a high heterogenous pattern. For two particular types of intergenic miRNAs, readthrough and divergent miRNAs, readthrough miRNAs showed higher coexpression with their host genes than divergent ones. miRNAs located within non-coding genes had tighter co-transcription with their hosts than those located within protein-coding genes, especially exonic and junction miRNAs. A few precursor miRNAs changed their dominate form between 5′ and 3′ strands in different cancer types, including miR-486, miR-99b, let-7e, miR-125a, let-7g, miR-339, miR-26a, miR-16, and miR-218, whereas only two miRNAs with multiple host genes switched their co-transcriptional partner in different cancer types (miR-219a-1 with SLC39A7/HSD17B8 and miR-3615 with RAB37/SLC9A3R1). miRNAs generated from distinct precursors (such as miR-125b from miR-125b-1 or miR-125b-2) were more likely to have cancer-dependent main contributors. miRNAs and hosts were less co-expressed in KIRC than other cancer types, possibly due to its frequent VHL mutations. Our findings shed new light on miRNA biogenesis and cancer diagnosis and treatments.

A hybrid CNN-LSTM model for pre-miRNA classification

AbstractmiRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.

miRNAFinder: A Comprehensive Web Resource for Plant Pre-microRNA Classification

microRNAs (miRNAs) are known as one of the small non-coding RNA molecules, which control the expressions of genes at the RNA level. They typically range 20-24 nucleotides in length and can be found in the plant and animal kingdoms and in some viruses. Computational approaches have overcome the limitations in the experimental methods and have performed well in identifying miRNAs. Compared to mature miRNAs, precursor miRNAs (pre-miRNAs) are long and have a hairpin loop structure with structural features. Therefore, most in-silico tools are implemented for the pre-miRNAs identification. This study presents a multilayer perceptron (MLP) based classifier implemented using 180 features under sequential, structural, and thermodynamic feature categories for plant pre-miRNA identification. This classifier has a 92% accuracy, 94% specificity, and 90% sensitivity. We have further tested this model with other small non-coding RNA types and obtained 78% accuracy. Furthermore, we introduce a novel dataset to train and test machine learning models, addressing the overlapping data issue in positive training and testing datasets presented in PlantMiRNAPred, a study done by Xuan et al. for the classification of real and pseudo plant pre-miRNAs. The new dataset and the classifier are deployed on a web server which is freely accessible via http://mirnafinder.shyaman.me/.

AASRA: An Anchor Alignment-Based Small RNA Annotation Pipeline

Small noncoding RNAs deep sequencing (sncRNA-Seq) has become a routine for sncRNA detection and quantification. However, the software packages currently available for sncRNA annotation can neither recognize sncRNA variants in the sequencing reads, nor annotate all known sncRNA simultaneously. Here, we report a novel anchor alignment-based small RNA annotation (AASRA) software package (https://github.com/biogramming/AASRA). AASRA represents an all-in-one sncRNA annotation pipeline, which allows for high-speed, simultaneous annotation of all known sncRNA species with the capability to distinguish mature from precursor miRNAs, and to identify novel sncRNA variants in the sncRNA-Seq sequencing reads.

Predicted SARS-CoV-2 miRNAs associated with epigenetic viral pathogenesis and the detection of new possible drugs for Covid-19

Objective: The outbreak of COVID-19 caused by SARS-CoV-2 has promptly spread worldwide. This study aimed to predict mature miRNA sequences in the SARS-CoV-2 genome, their effects on protein-protein interactions in the affected cells, gene-drug relationships to detect possible drug candidates. Methods: Viral hairpin structure prediction and classification of the hairpins, mutational examination of precursor miRNA candidate sequences, Minimum Free Energy (MFE) and regional entropy analysis, mature miRNA sequences, target gene prediction, gene ontology enrichment and Protein-Protein Interaction (PPI) analysis, gene-drug interactions were performed. Results: A total of 62 candidate hairpins were detected by VMir analysis. Three hairpin structures classified as true precursor miRNAs by miRBoost. Five different mutations were detected in precursor miRNA sequences in 100 SARS-CoV-2 viral genomes. Mutations slightly elevated MFE values and entropy in precursor miRNAs. Gene ontology terms associated with fibrotic pathways and immune system were found to be enriched in PANTHER, KEGG and Wiki pathway analysis. PPI analysis showed a network between 60 genes. CytoHubba analysis showed SMAD1 as a hub gene in the network. The targets of the predicted miRNAs, FAM214A, PPM1E, NUFIP2 and FAT4 were downregulated in SARS-CoV-2 infected A549 cells. Conclusion: miRNAs in the SARS-CoV-2 virus genome may contribute to the emergence of the Covid-19 clinic by activating pathways associated with the fibrosis in the cells infected by the virus and modulating the innate immune system. The hub protein between these pathways may be the SMAD1, which has an effective role in TGF signal transduction.

PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction

Rice microRNAs (miRNAs) are important post-transcriptional regulation factors and play vital roles in many biological processes, such as growth, development, and stress resistance. Identification of these molecules is the basis of dissecting their regulatory functions. Various machine learning techniques have been developed to identify precursor miRNAs (pre-miRNAs). However, no tool is implemented specifically for rice pre-miRNAs. This study aims at improving prediction performance of rice pre-miRNAs by constructing novel features with high discriminatory power and developing a training model with species-specific data. PlantMirP-rice, a stand-alone random forest-based miRNA prediction tool, achieves a promising accuracy of 93.48% based on independent (unseen) rice data. Comparisons with other competitive pre-miRNA prediction methods demonstrate that plantMirP-rice performs better than existing tools for rice and other plant pre-miRNA classification.

Guanine content of microRNAs is associated with their tumor suppressive and oncogenic roles in lung and breast cancers

Background: microRNAs (miRNAs, miRs) are small noncoding RNAs that negatively regulate gene expression at the post-transcriptional level and fine-tune gene functions. A global repression of miRNAs expression in different types of human tumors, after exposure to cigarette-smoke, or to the hormone estrogen, have been shown to be associated with guanine (G) enrichment in the terminal loops (TLs) of their precursors. Methods: we integrated the G content of miRNA mature forms and precursor miRNA TLs with their described function in the literature, using the PubMed database. Gene Ontology term analysis was used to describe the pathways in which the G-enriched miRNA targets are involved. Results : herein we show an association between the relative G enrichment of precursor miRNAs’ TLs and their tendency to act as tumor suppressor miRs in human lung and breast cancers. Another association was observed between the high G content of the miRNAs 5-mature forms and their tendency to act as oncomiRs. Conclusion: the results support previous findings showing that the G sequence content is an important feature determining miRNA expression and function, and opens the way for future cancer investigations in this direction.

Genome-wide development of miRNA-based SSR markers in Cleistogenes songorica with their transferability analysis to gramineae and non- gramineae species

SSR markers are commonly used for many genetic applications, such as map construction, fingerprinting and genetic diversity analysis due to their high reproducibility, levels of polymorphism and abundance. As endogenous, small RNAs, miRNAs have essential roles in plant development and gene expression under diverse stress conditions, including various biotic and abiotic stress conditions. In the present study, we predicted 110 pre-miRNAs sequences from 287 precursor miRNAs and used them as queries for SSR marker development. Among 110 primer pairs, 85 were successfully amplified and examined for transferability to other gramineae and non-gramineae species. The results showed that all 82 primer pairs yielded unambiguous and strong amplification, and across the 23 studied Cleistogenes accessions, a total of 385 alleles were polymorphic. The number of alleles produced per primer varied from 3 to 11, with an average of 4.69 per locus. The expected heterozygosity (He) ranged from 0.44 to 0.88, with an average of 0.74 per locus, and the PIC (Polymorphism Information Content) values ranged from 0.34 to 0.87, with an average of 0.69 per locus. In this study, 1422 miRNA target genes were predicted and analyzed using the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases. The results showed that this miRNA-based microsatellite marker system can be very useful for genetic diversity and marker-assisted breeding studies.

Guanine content of microRNAs is associated with their tumor suppressive and oncogenic roles in lung and breast cancers

BackgroundmicroRNAs (miRNAs, miRs) are small noncoding RNAs that negatively regulate gene expression at the post-transcriptional level and fine-tune gene functions. A global repression of miRNAs expression in different types of human tumors, after exposure to cigarette-smoke, or to the hormone estrogen, have been shown to be associated with guanine (G) enrichment in the terminal loops (TLs) of their precursors.Methodswe integrated the G content of miRNA mature forms and precursor miRNA TLs with their described function in the literature, using the PubMed database. Gene Ontology term analysis was used to describe the pathways in which the G-enriched miRNA targets are involved.Resultswe show here an association between the relative G enrichment of precursor miRNAs TLs and their tendency to act as tumor suppressor miRs in human lung and breast cancers. Another association was observed between the high G content of the miRNAs 5-mature forms and their tendency to act as oncomiRs.Conclusionsthe results support previous findings showing that the G sequence content is an important feature determining miRNA expression and function, and open the way for future cancer investigations in this direction.