A heterogeneous disease such as cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), the survival of the patients varies significantly and shows different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score and found that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we compared the effect of feature selection and similarity measures for subtype detection. For further evaluation, we used the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes. The results obtained are consistent with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.

This study developed a computational tool with a graphical interface and a web-service that allows the identification of phage regions through homology search and gene clustering. It uses G+C content variation evaluation and tRNA prediction sites as evidence to reinforce the presence of prophages in indeterminate regions. Also, it performs the functional characterization of the prophages regions through data integration of biological databases. The performance of PhageWeb was compared to other available tools (PHASTER, Prophinder, and PhiSpy) using Sensitivity (Sn) and Positive Predictive Value (PPV) tests. As a reference for the tests, more than 80 manually annotated genomes were used. In the PhageWeb analysis, the Sn index was 86.1% and the PPV was approximately 87%, while the second best tool presented Sn and PPV values of 83.3 and 86.5%, respectively. These numbers allowed us to observe a greater precision in the regions identified by PhageWeb while compared to other prediction tools submitted to the same tests. Additionally, PhageWeb was much faster than the other computational alternatives, decreasing the processing time to approximately one-ninth of the time required by the second best software. PhageWeb is freely available at http://computationalbiology.ufpa.br/phageweb.

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most common neurodegenerative disorders related to aging. Though several risk factors are shared between these two diseases, the exact relationship between them is still unknown. In this paper, we analyzed how these two diseases relate to each other from the genomic, epigenomic, and transcriptomic viewpoints. Using an extensive literature mining, we first accumulated the list of genes from major genome-wide association (GWAS) studies. Based on these GWAS studies, we observed that only one gene (HLA-DRB5) was shared between AD and PD. A subsequent literature search identified a few other genes involved in these two diseases, among which SIRT1 seemed to be the most prominent one. While we listed all the miRNAs that have been previously reported for AD and PD separately, we found only 15 different miRNAs that were reported in both diseases. In order to get better insights, we predicted the gene co-expression network for both AD and PD using network analysis algorithms applied to two GEO datasets. The network analysis revealed six clusters of genes related to AD and four clusters of genes related to PD; however, there was very low functional similarity between these clusters, pointing to insignificant similarity between AD and PD even at the level of affected biological processes. Finally, we postulated the putative epigenetic regulator modules that are common to AD and PD.

The increasing complexity of food safety concerns, driven by the rising risks of contamination from pathogens, chemical hazards, and environmental influences, has highlighted the need for more effective detection and prevention strategies. Metagenomics, a powerful molecular tool, is transforming the food industry by providing a comprehensive understanding of the microbial communities in fresh produce, poultry, and meat. Advances in microbial detection techniques, such as shotgun sequencing, metabarcoding, and long-read sequencing technologies, have led to faster and more accurate pathogen detection, reducing the risk of outbreaks and ensuring public health protection. Despite its promise, several challenges remain in implementing metagenomics on a broader scale, including the high cost of sequencing technologies, the complexity of analyzing large datasets, and the lack of standardized procedures across platforms. These limitations hinder its adoption, particularly for smaller operations or in regions with fewer resources. This review examines the applications of metagenomics in food safety, focusing on its impact on meat, poultry, and fresh produce, while discussing the obstacles to its widespread use and potential solutions to overcome these barriers.

Foodborne diseases (FBDs) represent a significant public health concern, particularly in regions like Central America and the Caribbean (CAC), where surveillance gaps due to a lack of resources, knowledge, and technical abilities hinder control over outbreaks. This review investigates the bacterial pathogens responsible for FBDs, their prevalence, management challenges, and prevention strategies. This systematic review followed PRISMA guidelines, focusing on bacterial FBDs in CAC from 2000 to 2024. PubMed and Google Scholar were used as primary databases, supported by other sources to identify relevant studies. Inclusion criteria encompassed studies focusing on bacterial pathogens, prevalence, risk factors, and surveillance practices. Out of the 509 studies initially identified, 35 met the inclusion criteria. The most prevalent pathogens were Salmonella spp., Escherichia coli, Campylobacter spp., and Aliarcobacter spp., with contamination often associated with poultry, eggs, and vegetables. Key challenges included inadequate surveillance systems, limited resources, and inconsistent reporting practices. A more significant investment in pathogen monitoring, documentation, and education, along with technologies like whole-genome sequencing (WGS), is crucial. Institutional and governmental funding is vital to improve surveillance and strengthen regional risk analysis.

Bacteria carrying antibiotic resistance genes (ARGs) are naturally prevalent in lotic ecosystems such as rivers. Their ability to spread in anthropogenic waters could lead to the emergence of multidrug-resistant bacteria of clinical importance. For this study, three regions of the Isabela river, an important urban river in the city of Santo Domingo, were evaluated for the presence of ARGs. The Isabela river is surrounded by communities that do not have access to proper sewage systems; furthermore, water from this river is consumed daily for many activities, including recreation and sanitation. To assess the state of antibiotic resistance dissemination in the Isabela river, nine samples were collected from these three bluedistinct sites in June 2019 and isolates obtained from these sites were selected based on resistance to beta-lactams. Physico-chemical and microbiological parameters were in accordance with the Dominican legislation. Matrix-assisted laser desorption ionization-time of flight mass spectrometry analyses of ribosomal protein composition revealed a total of 8 different genera. Most common genera were as follows: Acinetobacter (44.6%) and Escherichia (18%). Twenty clinically important bacterial isolates were identified from urban regions of the river; these belonged to genera Escherichia (n = 9), Acinetobacter (n = 8), Enterobacter (n = 2), and Klebsiella (n = 1). Clinically important multi-resistant isolates were not obtained from rural areas. Fifteen isolates were selected for genome sequencing and analysis. Most isolates were resistant to at least three different families of antibiotics. Among beta-lactamase genes encountered, we found the presence of bla_TEM, bla_OXA, bla_SHV, and bla_KPC through both deep sequencing and PCR amplification. Bacteria found from genus Klebsiella and Enterobacter demonstrated ample repertoire of antibiotic resistance genes, including resistance from a family of last resort antibiotics reserved for dire infections: carbapenems. Some of the alleles found were KPC-3, OXA-1, OXA-72, OXA-132, CTX-M-55, CTX-M-15, and TEM-1.