BioContainers Registry: Searching Bioinformatics and Proteomics Tools, Packages, and Containers.

BioContainers is an open-source project that aims to create, store, and distribute bioinformatics software containers and packages. The BioContainers community has developed a set of guidelines to standardize software containers including the metadata, versions, licenses, and software dependencies. BioContainers supports multiple packaging and container technologies such as Conda, Docker, and Singularity. The BioContainers provide over 9000 bioinformatics tools, including more than 200 proteomics and mass spectrometry tools. Here we introduce the BioContainers Registry and Restful API to make containerized bioinformatics tools more findable, accessible, interoperable, and reusable (FAIR). The BioContainers Registry provides a fast and convenient way to find and retrieve bioinformatics tool packages and containers. By doing so, it will increase the use of bioinformatics packages and containers while promoting replicability and reproducibility in research.

Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines.

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, bioinformatics analysis is becoming increasingly complex and convoluted, involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are designed as single-tiered software application where the analytics tasks cannot be distributed, limiting the scalability and reproducibility of the data analysis. In this paper the key steps of metabolomics and proteomics data processing, including the main tools and software used to perform the data analysis, are summarized. The combination of software containers with workflows environments for large-scale metabolomics and proteomics analysis is discussed. Finally, a new approach for reproducible and large-scale data analysis based on BioContainers and two of the most popular workflow environments, Galaxy and Nextflow, is introduced to the proteomics and metabolomics communities.

Magnetic Platelets as a Platform for Drug Delivery and Cell Trapping.

The possibility of using magnetically labeled blood cells as carriers is a novel approach in targeted drug-delivery systems, potentially allowing for improved bloodstream delivery strategies. Blood cells already meet the requirements of biocompatibility, safety from clotting and blockage of small vessels. It would solve the important problem of the patient's immune response to embedded foreign carriers. The high efficiency of platelet loading makes them promising research objects for the development of personalized drug-delivery systems. We are developing a new approach to use platelets decorated with magnetic nanoparticles as a targeted drug-delivery system, with a focus on bloodstream delivery. Platelets are non-nuclear blood cells and are of great importance in the pathogenesis of blood-clotting disorders. In addition, platelets are able to attach to circulating tumor cells. In this article, we studied the effect of platelets labeled with BSA-modified magnetic nanoparticles on healthy and cancer cells. This opens up broad prospects for future research based on the delivery of specific active substances by this method.

snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data.

Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Software incompatibilities, and inconsistencies across computing environments, are recurrent challenges, leading to poor reproducibility. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with numerous user-modifiable thresholds, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.