Current Genomics最新文献

Background: The coronavirus disease has led to an exhaustive exploration of the SARS-CoV-2 genome. Despite the amount of information accumulated, the prediction of short RNA motifs encoding peptides mediating protein-protein or protein-drug interactions has received limited attention.

Objective: The study aims to predict short RNA motifs that are interspersed in the SARS-CoV-2 genome.

Methods: A method in which 14 trinucleotide families, each characterized by being composed of triplets with identical nucleotides in all possible configurations, was used to find short peptides with biological relevance. The novelty of the approach lies in using these families to search how they are distributed across genomes of different CoV genera and then to compare the distributions of these families with each other.

Results: We identified distributions of trinucleotide families in different CoV genera and also how they are related, using a selection criterion that identified short RNA motifs. The motifs were reported to be conserved in SARS-CoVs; in the remaining CoV genomes analysed, motifs contained, exclusively, different configurations of the trinucleotides A, T, G and A, C, G. Eighty-eight short RNA motifs, ranging in length from 12 to 49 nucleotides, were found: 50 motifs in the 1a polyprotein-encoding orf, 27 in the 1b polyprotein-encoding orf, 5 in the spike-encoding orf, and 6 in the nucleocapsid-encoding orf. Although some motifs (~27%) were found to be intercalated or attached to functional peptides, most of them have not yet been associated with any known functions.

Conclusion: Some of the trinucleotide family distributions in different CoV genera are not random; they are present in short peptides that, in many cases, are intercalated or attached to functional sites of the proteome.

The first complete chloroplast genome of rice (Oryza sativa) was published in 1989, ushering in a new era of studies of chloroplast genomics in Poaceae. Progresses in Next-Generation Sequencing (NGS) and Third-Generation Sequencing (TGS) technologiesand in the development of genome assembly software, have significantly advanced chloroplast genomics research. Poaceae is one of the most targeted families in chloroplast genome research because of its agricultural, ecological, and economic importance. Over the last 30 years, 2,050 complete chloroplast genome sequences from 40 tribes and 282 genera have been generated, most (97%) of them in the recent ten years. The wealth of data provides the groundwork for studies on species evolution, phylogeny, genetic transformation, and other aspects of Poaceae chloroplast genomes. As a result, we have gained a deeper understanding of the properties of Poaceae chloroplast genomes. Here, we summarize the achievements of the studies of the Poaceae chloroplast genomes and envision the challenges for moving the area ahead.

Background: Sepsis is an uncontrolled systemic inflammatory response. Long noncoding RNAs (lncRNAs) are involved in the pathogenesis of sepsis. However, little is known about the roles of lncRNAs in sepsis-induced myocardial dysfunction.

Objective: We aimed to determine the regulatory mechanism of lncRNAs in sepsis-induced myocardial dysfunction.

Methods: In this study, we analysed the lncRNA and mRNA expression profiles using microarray analysis. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, protein-protein interaction network, and gene set enrichment analysis were used to evaluate the data. We also constructed coding and noncoding coexpression and competing endogenous RNA networks to investigate the mechanisms.

Results: In vivo lipopolysaccharide -induced sepsis rat model was established. A total of 387 lncRNAs and 1,952 mRNAs were identified as significantly changed in the left ventricle. Kyoto Encyclopedia of Genes and Genomes analysis of mRNAs showed that the upregulated genes were mainly enriched in the "complement and coagulation cascade pathway" and "immune-related biological processes" terms. Eight significantly changed lncRNAs detected by RT-qPCR may be responsible for these processes. A competing endogenous RNA network was generated, and the results indicated that eight lncRNAs were related to the "calcium ion binding" process.

Conclusion: These results demonstrate that crosstalk between lncRNAs and mRNAs may play important roles in the development of sepsis-induced myocardial dysfunction.

Background: One major challenge in binning Metagenomics data is the limited availability of reference datasets, as only 1% of the total microbial population is yet cultured. This has given rise to the efficacy of unsupervised methods for binning in the absence of any reference datasets.

Objective: To develop a deep clustering-based binning approach for Metagenomics data and to evaluate results with suitable measures.

Methods: In this study, a deep learning-based approach has been taken for binning the Metagenomics data. The results are validated on different datasets by considering features such as Tetra-nucleotide frequency (TNF), Hexa-nucleotide frequency (HNF) and GC-Content. Convolutional Autoencoder is used for feature extraction and for binning; the K-means clustering method is used.

Results: In most cases, it has been found that evaluation parameters such as the Silhouette index and Rand index are more than 0.5 and 0.8, respectively, which indicates that the proposed approach is giving satisfactory results. The performance of the developed approach is compared with current methods and tools using benchmarked low complexity simulated and real metagenomic datasets. It is found better for unsupervised and at par with semi-supervised methods.

Conclusion: An unsupervised advanced learning-based approach for binning has been proposed, and the developed method shows promising results for various datasets. This is a novel approach for solving the lack of reference data problem of binning in metagenomics.