Bioportainer Workbench: a versatile and user-friendly system that integrates implementation, management, and use of bioinformatics resources in Docker environments

Menegídio, Fabiano Bezerra; Barbosa, David Aciole; Gonçalves, Rafael Dos S; Nishime, Marcio M; Jabès, Daniela; Oliveira, Regina Costa de; Nunes, Luiz R.

doi:10.1093/gigascience/giz041

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Containers can be consistently interchanged and used in different computing environments, irrespective of the differences in user hardware and/or operating systems. These features of the containers ensure replicability and reproducibility of data analyses across different facilities [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations

Wang,

Jiang,

Liang

et al. 2024

BMC Genomics

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The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biological problems. However, the ultimate goal is to construct a customized model for predicting the drug resistance of MTB and eventually for the biological phenotypes based on genotypes. Here, we constructed a DL training framework to standardize and modularize each step during the training process using the latest tensorflow 2 API. A systematic and comprehensive evaluation of each module in the three currently representative models, including Convolutional Neural Network, Denoising Autoencoder, and Wide & Deep, which were adopted by CNNGWP, DeepAMR, and WDNN, respectively, was performed in this framework regarding module contributions in order to assemble a novel model with proper dedicated modules. Based on the whole-genome level mutations, a de novo learning method was developed to overcome the intrinsic limitations of previous models that rely on known drug resistance-associated loci. A customized DL model with the multilayer perceptron architecture was constructed and achieved a competitive performance (the mean sensitivity and specificity were 0.90 and 0.87, respectively) compared to previous ones. The new model developed was applied in an end-to-end user-friendly graphical tool named TB-DROP (TuBerculosis Drug Resistance Optimal Prediction: https://github.com/nottwy/TB-DROP), in which users only provide sequencing data and TB-DROP will complete analysis within several minutes for one sample. Our study contributes to both a new strategy of model construction and clinical application of deep learning-based drug-resistance prediction methods.

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Section: Discussionmentioning

confidence: 99%

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations

Wang,

Jiang,

Liang

et al. 2024

BMC Genomics

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“…The Docker project is providing a new and promising virtualization strategy that consumes a considerably low amount of disk space (when compared to Virtual Machines) and offers the advantage of being platform-agnostic, since it relies on the configuration of containers, which can be consistently interchanged and deployed on different computing environments, regardless the specificities of their hardware and/or operating system [33]. In recent years, this type of technology has been increasingly employed to generate bioinformatics-related software and services to a large variety of research facilities, employing the concepts of Platform as a Service ( PaaS ) and Software as a Service (SaaS) , as a strategy to assist in replicability and reproducibility of data analysis across laboratories [62,63,64,65,66,67,68]. In this context, ParaDB is the first initiative that tries to develop a Docker system with a biological database, carrying genomic information of pathogenic microorganisms, thus introducing the concept of Database-as-a-Service ( DBaaS ), as a strategy to guarantee long term availability of biological data and resources.…”

Section: Discussionmentioning

confidence: 99%

ParaDB: A manually curated database containing genomic annotation for the human pathogenic fungi Paracoccidioides spp.

et al. 2019

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Background The genus Paracoccidioides consists of thermodymorphic fungi responsible for Paracoccidioidomycosis (PCM), a systemic mycosis that has been registered to affect ~10 million people in Latin America. Biogeographical data subdivided the genus Paracoccidioides in five divergent subgroups, which have been recently classified as different species. Genomic sequencing of five Paracoccidioides isolates, representing each of these subgroups/species provided an important framework for the development of post-genomic studies with these fungi. However, functional annotations of these genomes have not been submitted to manual curation and, as a result, ~60–90% of the Paracoccidioides protein-coding genes (depending on isolate/annotation) are currently described as responsible for hypothetical proteins, without any further functional/structural description. Principal findings The present work reviews the functional assignment of Paracoccidioides genes, reducing the number of hypothetical proteins to ~25–28%. These results were compiled in a relational database called ParaDB, dedicated to the main representatives of Paracoccidioides spp. ParaDB can be accessed through a friendly graphical interface, which offers search tools based on keywords or protein/DNA sequences. All data contained in ParaDB can be partially or completely downloaded through spreadsheet, multi-fasta and GFF3-formatted files, which can be subsequently used in a variety of downstream functional analyses. Moreover, the entire ParaDB environment has been configured in a Docker service, which has been submitted to the GitHub repository, ensuring long-term data availability to researchers. This service can be downloaded and used to perform fully functional local installations of the database in alternative computing ecosystems, allowing users to conduct their data mining and analyses in a personal and stable working environment. Conclusions These new annotations greatly reduce the number of genes identified solely as hypothetical proteins and are integrated into a dedicated database, providing resources to assist researchers in this field to conduct post-genomic studies with this group of human pathogenic fungi.

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“…The EggNOG hits also helped to assign the biological function to the genes, identifying GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) terms. EnTAP functional annotation process was carried out using a Dugong container environment (Menegidio et al 2018) in BioPortainer Workbench (Menegidio et al 2019).…”

Section: Diet and Liver Fatty Acid Pro Lementioning

confidence: 99%

Post-feeding molecular responses of cobia (Rachycentron canadum): RNA-sequencing as a tool to evaluate postprandial effects in hepatic lipid metabolism

Araújo

Barbosa

Honji

et al. 2022

Preprint

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We used transcriptome sequencing to investigate the hepatic postprandial responses of Rachycentron canadum (cobia), an important commercial fish species. In total, 150 cobia juveniles (50 per tank, triplicate) were fed ad libitum with a commercial diet for 7 days, fasted for 24 h and fed for 10 min. Liver samples were sampled 10 min before and 30 min, 1, 2, 4, 8, 12, and 24 h after the feed event. Posteriorly, it was evaluated liver fatty acid profile, transcriptome sequencing, and differential gene expression focusing on fatty acid synthesis and oxidation pathways. In general, liver fatty acid profile reflected the diet composition. Docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and n-3 long chain polyunsaturated fatty acids (n-3 LC-PUFA) levels increased at 8 to 12 h and decreased at 24 h after the feed event. It was observed a high number of differentially expressed genes (DEGs) comparing fish fasted by 8 h with those fasted by 30 min and 24 h, while a reduced number of DEGs was observed comparing individuals fasted by 30 min compared with those fasted by 24 h. Similarly, the main differences in the expression of genes related to the fatty acid biosynthesis and oxidation pathways were noticed in individuals fasted by 8 h compared with those fasted by 30 min and 24 h. The results suggested that the adequate time to sample the individuals ranged between 8 and 12 h after meal, since apparently, after 24 h, differential gene expression was not necessarily influenced by feed intake.

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Bioportainer Workbench: a versatile and user-friendly system that integrates implementation, management, and use of bioinformatics resources in Docker environments

Cited by 7 publications

References 20 publications

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations

ParaDB: A manually curated database containing genomic annotation for the human pathogenic fungi Paracoccidioides spp.

Post-feeding molecular responses of cobia (Rachycentron canadum): RNA-sequencing as a tool to evaluate postprandial effects in hepatic lipid metabolism

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