Neurodegenerative diseases (NDDs) are on the rise in the world. Therefore, it is a critical issue to reveal the precise pathophysiological mechanisms and novel therapeutic strategies to deal with such conditions. Passing through different mechanisms, non-coding RNAs (ncRNAs) play a pivotal role in NDDs through various mechanisms, by changing the expression of some genes, interference with protein translation and alterations in some signaling pathways. It urges the need to introduce novel strategies and therapeutic agents with multi-targeting potentials. Phytochemicals are hopeful antioxidants and anti-inflammatory agents with promising modulatory roles on dysregulated signaling pathways and protein translation during NDDs. In this study, the role of ncRNAs (e.g. lncRNAs, miRNA, siRNAs and piRNAs) was highlighted in NDDs. This study also aimed to investigate the role of phytochemicals (phenolic compounds, alkaloids, terpenoids and sulfur compounds) in the modulation of ncRNAs during NDDs such as Alzheimer's disease, Parkinson's disease, epilepsy, depression and amyotrophic lateral sclerosis.

The entire world is facing the stiff challenge of COVID-19 pandemic. To overcome the spread of this highly infectious disease, several short-sighted strategies were adopted such as the use of broad-spectrum antibiotics and antifungals. However, the misuse and/or overuse of antibiotics have accentuated the emergence of the next pandemic: antimicrobial resistance (AMR). It is believed that pathogens while transferring between humans and the environment carry virulence and antibiotic-resistant factors from varied species. It is presumed that all such genetic factors are quantifiable and predictable, a better understanding of which could be a limiting step for the progression of AMR. Herein, we have reviewed how genomics-based understanding of host-pathogen interactions during COVID-19 could reduce the non-judicial use of antibiotics and prevent the eruption of an AMR-based pandemic in future.

Enhancers, a class of distal cis-regulatory elements located in the non-coding region of DNA, play a key role in gene regulation. It is difficult to identify enhancers from DNA sequence data because enhancers are freely distributed in the non-coding region, with no specific sequence features, and having a long distance with the targeted promoters. Therefore, this study presents a stacking ensemble learning method to accurately identify enhancers and classify enhancers into strong and weak enhancers. Firstly, we obtain the fusion feature matrix by fusing the four features of Kmer, PseDNC, PCPseDNC and Z-Curve9. Secondly, five K-Nearest Neighbor (KNN) models with different parameters are trained as the base model, and the Logistic Regression algorithm is utilized as the meta-model. Thirdly, the stacking ensemble learning strategy is utilized to construct a two-layer model based on the base model and meta-model to train the preprocessed feature sets. The proposed method, named iEnhancer-SKNN, is a two-layer prediction model, in which the function of the first layer is to predict whether the given DNA sequences are enhancers or non-enhancers, and the function of the second layer is to distinguish whether the predicted enhancers are strong enhancers or weak enhancers. The performance of iEnhancer-SKNN is evaluated on the independent testing dataset and the results show that the proposed method has better performance in predicting enhancers and their strength. In enhancer identification, iEnhancer-SKNN achieves an accuracy of 81.75%, an improvement of 1.35% to 8.75% compared with other predictors, and in enhancer classification, iEnhancer-SKNN achieves an accuracy of 80.50%, an improvement of 5.5% to 25.5% compared with other predictors. Moreover, we identify key transcription factor binding site motifs in the enhancer regions and further explore the biological functions of the enhancers and these key motifs. Source code and data can be downloaded from https://github.com/HaoWuLab-Bioinformatics/iEnhancer-SKNN.

A major near-term medical impact of the genomic technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Next-generation sequencing technologies have accelerated the characterization of a tumor, leading to the comprehensive discovery of all the major alterations in a given cancer genome, followed by the translation of this information using computational and immunoinformatics approaches to cancer diagnostics and therapeutic efforts. In the current article, we review various components of cancer immunoinformatics applied to a series of fields of cancer research, including computational tools for cancer mutation detection, cancer mutation and immunological databases, and computational vaccinology.

Integrated studies (multi-omics studies) comprising genetic, proteomic and epigenetic data analyses have become an emerging topic in biomedical research. Protein methylation is a posttranslational modification that plays an essential role in various cellular activities. The prediction of methylation sites (arginine and lysine) is vital to understand the molecular processes of protein methylation. However, current experimental techniques used for methylation site predictions are tedious and expensive. Hence, computational techniques for predicting methylation sites in proteins are necessary. For predicting methylation sites, various computational methods have been proposed in recent years. Most existing methods require structural and evolutionary information for retrieving features, acquiring this information is not always convenient. Thus, we proposed a novel method, called multi-factorial feature extraction and site prognosis model (MufeSPM), for the prediction of protein methylation sites based on information theory features (Renyi, Shannon, Havrda-Charvat and Arimoto entropy), amino acid composition and physicochemical properties acquired from protein methylation data. A random forest algorithm was used to predict methylation sites in protein sequences. This paper also studied the impact of different features and classifiers on arginine and lysine methylation data sets. For the R methylation data set, MufeSPM yielded 82.45%($\pm $ 3.47) accuracy, and for the K methylation data set, it provided an average accuracy of 71.94%($\pm $ 2.12). Additionally, the area under the receiver operating characteristic curve for different classifiers in predicting methylation site was provided. The experimental results signify that MufeSPM performs better than the state-of-the-art predictors.

Magnetotactic bacteria (MTB) are worth studying because of magnetosome biomineralization. Magnetosome biogenesis in MTB is controlled by multiple genes known as magnetosome-associated genes. Recent advances in bioinformatics provide a unique opportunity for studying functions of magnetosome-associated genes and networks that they are involved in. Furthermore, various types of bioinformatics analyses can also help identify genes associated with magnetosome biogenesis. To predict novel magnetosome-associated genes in the extended CtrA regulon, we analyzed expression data of Magnetospirillum magneticum AMB-1 in the GSE35625 dataset in NCBI GEO. We identified 10 potential magnetosome-associated genes using a combinational approach of differential expression analysis, Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis, protein-protein interaction network analysis and weighted gene co-expression network analysis. Meanwhile, we also discovered and compared two co-expression modules that most known magnetosome-associated genes belong to. Our comparison indicated the importance of energy on regulating co-expression module structures for magnetosome biogenesis. At the last stage of our research, we predicted at least four real magnetosome-associated genes out of 10 potential genes, based on a comparison of evolutionary trees between known and potential magnetosome-associated genes. Because of the discovery of common subtrees that the stressed species are enriched in, we proposed a hypothesis that multiple types of environmental stress can trigger magnetosome evolution in different waters, and therefore its evolution can recur at different times in various locations on earth. Overall, our research provides useful information for identifying new MTB species and understanding magnetosome biogenesis.