BACKGROUND: Blackberries (Rubus spp.) are fruits rich in secondary components (anthocyanins, proanthocyanidins, phenolic acids, carotenoids and others), recognized for their health benefits. OBJECTIVE: To evaluate the content of different types of phenolic compounds and their antioxidant activity in several extracts of three varieties of blackberry fruit (Rubus adenotrichos) (red thorned, thornless and sweet), using different blackberry standards. METHODS:The varieties of blackberry fruit were analyzed in three stages of maturation (green, red and black). The evaluation of the phenolic compounds was carried out by applying commercial standards and own standards to the same samples, following the Folin-Ciocalteu, differential pH, DMAC, and ORAC procedures. RESULTS:The red thorned variety presented the best results with a concentration of polyphenols of 183.0 ± 0.5 mg GAE/g DS, antioxidant capacity of 3322 ± 10 mol TEAC/g DS, a value of 15.4 ± 0.3 mg of cyanidin-3-glucoside eq/g DS of anthocyanins, and a value of 9.26 ± 0.03 mg 4´-O-methylgallocatechin eq/g DS for of proanthocyanidin content. CONCLUSIONS: Our results show the limitation of a currently used standard, gallic acid and 4´-O-methylgallocatechin, for quantification of total polyphenols and proanthocyanidin respectively, and outline the development and validation of a more robust and accurate standard for blackberry fruit analysis.
Promoter identification is a fundamental step in understanding bacterial gene regulation mechanisms. However, accurate and fast classification of bacterial promoters continues to be challenging. New methods based on deep convolutional networks have been applied to identify and classify bacterial promoters recognized by sigma (σ) factors and RNA polymerase subunits which increase affinity to specific DNA sequences to modulate transcription and respond to nutritional or environmental changes. This work presents a new multiclass promoter prediction model by using convolutional neural networks (CNNs), denoted as PromoterLCNN, which classifies Escherichia coli promoters into subclasses σ70, σ24, σ32, σ38, σ28, and σ54. We present a light, fast, and simple two-stage multiclass CNN architecture for promoter identification and classification. Training and testing were performed on a benchmark dataset, part of RegulonDB. Comparative performance of PromoterLCNN against other CNN-based classifiers using four parameters (Acc, Sn, Sp, MCC) resulted in similar or better performance than those that commonly use cascade architecture, reducing time by approximately 30–90% for training, prediction, and hyperparameter optimization without compromising classification quality.
The increase in microbial sequenced genomes from pure cultures and metagenomic samples reflects the current attainability of whole-genome and shotgun sequencing methods. However, software for genome visualization still lacks automation, integration of different analyses, and customizable options for non-experienced users. In this study, we introduce GenoVi, a Python command-line tool able to create custom circular genome representations for the analysis and visualization of microbial genomes and sequence elements. It is designed to work with complete or draft genomes, featuring customizable options including 25 different built-in color palettes (including 5 color-blind safe palettes), text formatting options, and automatic scaling for complete genomes or sequence elements with more than one replicon/sequence. Using a Genbank format file as the input file or multiple files within a directory, GenoVi (i) visualizes genomic features from the GenBank annotation file, (ii) integrates a Cluster of Orthologs Group (COG) categories analysis using DeepNOG, (iii) automatically scales the visualization of each replicon of complete genomes or multiple sequence elements, (iv) and generates COG histograms, COG frequency heatmaps and output tables including general stats of each replicon or contig processed. GenoVi’s potential was assessed by analyzing single and multiple genomes of Bacteria and Archaea. Paraburkholderia genomes were analyzed to obtain a fast classification of replicons in large multipartite genomes. GenoVi works as an easy-to-use command-line tool and provides customizable options to automatically generate genomic maps for scientific publications, educational resources, and outreach activities. GenoVi is freely available and can be downloaded from https://github.com/robotoD/GenoVi.
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