A broad panel of potentially amplifiable microsatellite loci and a multiplex system were developed for the Amazonian symbol fish species Arapaima gigas, which is currently in high danger of extinction due to the disorderly fishing exploitation. Several factors have contributed to the increase of this threat, among which we highlight the lack of genetic information about the structure and taxonomic status of the species, as well as the lack of accurate tools for evaluation of the effectivity of current management programs. Based on Arapaima gigas’ whole genome, available at the NCBI database (ID: 12404), a total of 95,098 unique perfect microsatellites were identified, including their proposed primers. From this panel, a multiplex system containing 12 tetranucleotide microsatellite markers was validated. These tools are valuable for research in as many areas as bioinformatics, ecology, genetics, evolution and comparative studies, since they are able to provide more accurate information for fishing management, conservation of wild populations and genetic management of aquaculture.
Emerging deep learning-based applications in precision medicine include computational histopathological analysis. However, there is a lack of the required training image datasets to generate classification and detection models. This phenomenon occurs mainly due to human factors that make it difficult to obtain well-annotated data. The present study provides a curated public collection of histopathological images (DeepHP) and a convolutional neural network model for diagnosing gastritis. Images from gastric biopsy histopathological exams were used to investigate the performance of the proposed model in detecting gastric mucosa with Helicobacter pylori infection. The DeepHP database comprises 394,926 histopathological images, of which 111 K were labeled as Helicobacter pylori positive and 283 K were Helicobacter pylori negative. We investigated the classification performance of three Convolutional Neural Network architectures. The models were tested and validated with two distinct image sets of 15% (59K patches) chosen randomly. The VGG16 architecture showed the best results with an Area Under the Curve of 0.998%. The results showed that CNN could be used to classify histopathological images from gastric mucosa with marked precision. Our model evidenced high potential and application in the computational pathology field.
A broad panel of potentially amplifiable microsatellite loci and a multiplex system were developed for the Amazonian symbol fish species Arapaima gigas, which is currently in high danger of extinction due to the disorderly fishing exploitation. Several factors have contributed to the increase of this threat, among which we highlight the lack of genetic information about the structure and taxonomic status of the species, as well as the lack of accurate tools for evaluation of the effectivity of current management programs. Based on Arapaima gigas’ whole genome, available at the NCBI database (ID: 12404), a total of 95,098 unique perfect microsatellites were identified, including their proposed primers. From this panel, a multiplex system containing 12 tetranucleotide microsatellite markers was validated. These tools are valuable for research in as many areas as bioinformatics, ecology, genetics, evolution and comparative studies, since they are able to provide more accurate information for fishing management, conservation of wild populations and genetic management of aquaculture.
ClinVar is a web platform that stores ∼789,000 genetic associations with complex diseases. A partial set of these cataloged genetic associations has challenged clinicians and geneticists, often leading to conflicting interpretations or uncertain clinical impact significance. In this study, we addressed the (re)classification of genetic variants by AmazonForest, which is a random-forest-based pathogenicity metaprediction model that works by combining functional impact data from eight prediction tools. We evaluated the performance of representation learning algorithms such as autoencoders to propose a better strategy. All metaprediction models were trained with ClinVar data, and genetic variants were annotated with eight functional impact predictors cataloged with SnpEff/SnpSift. AmazonForest implements the best random forest model with a one hot data-encoding strategy, which shows an Area Under ROC Curve of ≥0.93. AmazonForest was employed for pathogenicity prediction of a set of ∼101,000 genetic variants of uncertain significance or conflict of interpretation. Our findings revealed ∼24,000 variants with high pathogenic probability (RFprob≥0.9). In addition, we show results for Alzheimer’s Disease as a demonstration of its application in clinical interpretation of genetic variants in complex diseases. Lastly, AmazonForest is available as a web tool and R object that can be loaded to perform pathogenicity predictions.
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