NCResNet: Noncoding Ribonucleic Acid Prediction Based on a Deep Resident Network of Ribonucleic Acid Sequences.

NCResNet: Noncoding Ribonucleic Acid Prediction Based on a Deep Resident Network of Ribonucleic Acid Sequences.

Noncoding RNA (ncRNA) is a type of RNA that plays a vital role in many biological processes, diseases, and cancer, while not able to be translated into proteins. With the development of the next generation sequence technology, thousands of novel RNA with long open reading frames (ORFs, the length of the longest ORF> 303 nt) and the short ORFs (the longest ORF length ≤ 303 nt) have been found in a short time. How to identify ncRNAs rather than unannotated Novel RNA is an important step for the functional analysis of RNA, regulatory RNA, etc. However, most previous methods only utilize the information of the sequence features.

Meanwhile, most of them have focused on long-ORF sequences RNA, but are not adapted to the short-ORF sequences of RNA. In this paper, we propose a new reliable method called NCResNet. NCResNet employs 57 features a hybrid of four categories of inputs, including sequence, protein, structural RNA, and RNA physicochemical properties, and introduce additional features and feature an in-depth policy learning neural network model to adapt to this issue. Experiments on the benchmark dataset of 8 species show NCResNet have higher accuracy and higher Matthews correlation coefficient (MCC) as compared with the state-of-the-art other methods.

Especially, in four short ORF RNA sequence datasets, especially mouse, Saccharomyces cerevisiae, zebrafish, and cattle, NCResNet Achieves greater than 10 and a 15% improvement over state-of-the-art methods in terms of accuracy and MCC. Meanwhile, the long-ORF RNA sequence dataset, NCResNet also have better accuracy and MCC of state-of-the-art other methods in most test dataset. Code and data are available

NCResNet: Noncoding Ribonucleic Acid Prediction Based on a Deep Resident Network of Ribonucleic Acid Sequences.
NCResNet: Noncoding Ribonucleic Acid Prediction Based on a Deep Resident Network of Ribonucleic Acid Sequences.

A Comprehensive Analysis Identifying differentially expressed Circular Key Ribonucleic Acid and Methylation-Related Functions in pheochromocytomas and Paragangliomas.

We investigated differentially expressed RNAs circular (circRNAs) and their potential function in pheochromocytomas and paragangliomas (PCC / PGLs). circRNAs level of expression in tumor and adjacent normal tissue from seven patients PCC / PGL analyzed through RNA sequencing. Real-time PCR was performed to verify the main candidates identified in the sequencing data. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analysis (KEGG) is performed to predict the function of this circRNAs.

A total of 367 circRNAs found differentially expressed between tumor and normal samples. Three related histone methylation-circRNAs (hsa_circ_0000567, hsa_circ_0002897, and hsa_circ_0004473) and target their microRNAs (miRNA) have been identified and validated. We then mapped circRNA-miRNA-messenger RNA (mRNA) coding-noncoding gene co-expression (CNC) network to demonstrate the potential of binding relationship between circRNAs and target them at the PCC / PGLs.

Five mRNAs, miRNAs 88, and 132 associated with the pathogenesis circRNAs be used to map the network CNC, and we observed that the candidate’s interaction with their target miRNAs regulated histone methylation and subsequently mediated PCC / PGL pathogenesis. This study is the first to provide the entire profile of circRNAs were expressed differently in the PCC / PGLs. Our data indicate that the modified circRNAs can control the pathogenesis of PCC / PGLs to regulate histone methylation process, highlighting their role as potential biomarkers.

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Despite decades of intensive research, many questions remain on the formation and growth of the first cell on earth. Here, we use computer simulations to compare the process of self-assembly of ribonucleic acid in two environments: sealed in the cell membrane vesicles and in such large numbers. self-assembly was found to be preferred in the former environment, and the origin of such biointerface effects identified. These results will contribute to a better understanding of the origins of life on the primitive Earth.

Kathleen

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