PhageWeb – Web Interface for Rapid Identification and Characterization of Prophages in Bacterial Genomes

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

Household Solid WasteManagement in the Dominican Republic: Case of the Municipality of Puñal, Santiago

One of the biggest problems that the Dominican Republic has had in recent decades is the efficient management of solid domestic waste. This problem has worsened in recent years due to the decrease in available areas for the construction of sanitary landfills, the lack of recycling culture in the population, the deficiency in waste collection, and the scarce legal controls aimed at preserving water, air and soil among other factors. The objective of this study is to explore the management of solid waste by the population and the municipality of Puñal, province of Santiago, to evaluate and analyze the situation and generation of solid waste, municipal solid waste management services, and the attitudes of the population regarding recycling projects and waste management. A total of 275 households from 29 localities in the municipality of Puñal were surveyed, which allowed for a significant population sample. According to the results, the most significant type of waste produced by families is organic waste, followed by plastic waste and paper. Of the total organic waste produced in the municipality, 53% of solids wastes are handled through the municipal waste collection system, while 47% is used as plant fertilizers or animal feed. On the other hand, most households receive the municipal waste collection service and pay for this service, through which the municipal government collects, processes, and deposits the waste in different landfills. However, a more efficient waste collection system and the development of programs and projects that allow households to manage the solid waste efficiently they produce would be necessary

One-Class SVM to identify candidates to reference genes based on the augment of RNA-seq data with generative adversarial networks

Reference Genes (RG) are constitutive genes required for the maintenance of basic cellular functions. Different high-throughput technologies are used to identify these types of genes, including RNA sequencing (RNA-seq), which allows measuring gene expression levels in a specific tissue or an isolated cell. In this paper, we present a new approach based on Generative Adversarial Network (GAN) and Support Vector Machine (SVM) to identify in-silico candidates for reference genes. The proposed method is divided into two main steps. First, the GAN is used to increase a small number of reference genes found in the public RNA-seq dataset of Escherichia coli. Second, a one-class SVM based on novelty detection is evaluated using some real reference genes and synthetic ones generated by the GAN architecture in the first step. The results show that increasing the dataset using the proposed GAN architecture improves the classifier score by 19%, making the proposed method have a recall score of 85% on the test data. The main contribution of the proposed methodology was to reduce the amount of candidate reference genes to be tested in the laboratory by up to 80%.This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior - Brasil (CAPES), under the Program PROCAD-AMAZÔNIA, process no 88881.357580/2019-01

Evaluation of the Common Molecular Basis in Alzheimer’s and Parkinson’s Diseases

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

Comparison of Two Bacterial Characterization Techniques for the Genomic Analysis of River Microbiomes

This study compares the feasibility of matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry with whole genome sequencing (WGS) for identifying bacteria in river surface water samples. We collected samples from four rivers in the Dominican Republic and used both techniques to characterize bacterial profiles. MALDI-TOF demonstrated high precision, with 86.2% similarity to WGS results, except for a few discordant cases due to database limitations. MALDI-TOF provided cost-effective and rapid identification, making it a promising alternative to WGS in resource-constrained regions. In particular, good effectiveness of MALDI-TOF in identifying bacteria with a high probability of being resistant to antibiotics was observed, which allows this technology to be used in the monitoring processes of this type of microorganism for their rapid, accurate, and low-cost identification. We found this technology to be advantageous for environmental bacterial profiling, with potential applications in understanding waterborne pathogenic bacteria. Our findings underline the relevance of MALDI-TOF in microbiology and its potential to expand its capabilities in bacterial identification and protein profiling