BACKGROUND: Advances in next-generation sequencing technologies have facilitated extensive analysis of B cell and T cell receptor (BCR/TCR, respectively) sequences from monoclonal hybridoma libraries, single B cells, and single T cells, generating vast amounts of important data pertaining to antigen recognition. However, existing workflows and bioinformatics tools often lack the flexibility and scalability needed to handle large clonal level datasets effectively. An initial system and hybridoma dependent version of this code was distributed as part of the NeuroMabSeq publication, but clonevdjseq aims to be a technical addendum for broader system compatibility and enhanced modeling. RESULTS: We present clonevdjseq, an integrated and accessible software solution leveraging nextflow and Django. Developed primarily for large hybridoma libraries, the workflow and pipeline is amenable to BCR/TCR sequence analysis of homogenous populations or clones of B and T cells, respectively. The clonevdjseq pipeline includes modules for read processing, amplicon denoising, and quality control of paired variable light/heavy chains of BCRs from B cells and hybridomas, or alpha(ɑ)/beta(β) and delta(δ)/gamma(γ) chains of TCRs in the case of T cell applications. The pipeline is built upon a robust, high-throughput library prep protocol, upon which processed data has been verified across thousands of monoclonal antibodies. The results of this effort has yielded sequences used to develop functional recombinant monoclonal antibodies and single chain variable fragments as a part of the NeuroMabSeq initiative where thousands of hybridoma samples were processed (Mitchell et al. in Sci Rep 13(1):16200, 2023) as well as provide additional modeling and extensibility to other modalities. The clonevdjseq software is accessible via Nextflow and also offers a database and web app as a final optional step in the processing for dissemination of results and data exploration. CONCLUSIONS: clonevdjseq offers a comprehensive and scalable solution for the processing and analysis of large monoclonal and oligoclonal VDJ datasets. Its modular design, dynamic pipeline, and robust database integration facilitate efficient data management and analysis. The platform is publicly available and aims to support the research community by providing an accessible and flexible tool for archiving and dissemination of BCR sequences from hybridomas, with applicability for other applications such as TCR sequences from single-cell T cell populations.

UNLABELLED: Microbial communities have an important role in natural ecosystems and have an impact on animal and human health. Intuitive graphic and analytical tools that can facilitate the study of these communities are in short supply. This article introduces Microbial Community Analysis GUI, a graphical user interface (GUI) for the R-programming language (R Development Core Team, 2010). With this application, researchers can input aligned and clustered sequence data to create custom abundance tables and perform analyses specific to their needs. This GUI provides a flexible modular platform, expandable to include other statistical tools for microbial community analysis in the future. AVAILABILITY: The mcaGUI package and source are freely available as part of Bionconductor at http://www.bioconductor.org/packages/release/bioc/html/mcaGUI.html

SUMMARY: OTUbase is an R package designed to facilitate the analysis of operational taxonomic unit (OTU) data and sequence classification (taxonomic) data. Currently there are programs that will cluster sequence data into OTUs and/or classify sequence data into known taxonomies. However, there is a need for software that can take the summarized output of these programs and organize it into easily accessed and manipulated formats. OTUbase provides this structure and organization within R, to allow researchers to easily manipulate the data with the rich library of R packages currently available for additional analysis. AVAILABILITY: OTUbase is an R package available through Bioconductor. It can be found at http://www.bioconductor.org/packages/release/bioc/html/OTUbase.html.

This paper shows that a novel hybrid algorithm, Breeding Swarms, performs equal to, or better than, Genetic Algorithms and Particle Swarm Optimizers when training recurrent neural networks. The algorithm was found to be robust and scale well to very large networks, ultimately outperforming Genetic Algorithms and Particle Swarm Optimization in 79 of 80 tested networks. This research shows that the Breeding Swarm algorithm is a viable option when choosing an algorithm to train recurrent neural networks.

In this paper we propose a novel hybrid (GA/PSO) algorithm, Breeding Swarms, combining the strengths of particle swarm optimization with genetic algorithms. The hybrid algorithm combines the standard velocity and position update rules of PSOs with the ideas of selection, crossover and mutation from GAs. We propose a new crossover operator, Velocity Propelled Averaged Crossover (VPAC), incorporating the PSO velocity vector. The VPAC crossover operator actively disperses the population preventing premature convergence. We compare the hybrid algorithm to both the standard GA and PSO models in evolving solutions to five standard function minimization problems. Results show the algorithm to be highly competitive, often outperforming both the GA and PSO.