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joshi, j.

Publications and source records attributed to joshi, j..

3 recordsLinked to original sources

GalaxyMemMD: an end-to-end Galaxy toolset for reproducible membrane embedding, molecular dynamics simulation, and analysis of membrane proteins

Membrane proteins play crucial roles in cellular signaling, transport, and pharmacological regulation, yet their structural and functional characterization remains challenging due to experimental limitations. Molecular dynamics (MD) simulations have become indispensable for probing membrane protein behavior at atomic resolution; however, executing these simulations at scale remains a significant bottleneck. Existing tools excel at system preparation for individual cases, but none offer a complete, high-throughput pipeline spanning embedding, simulation, and analysis across multiple membrane compositions, and rarely integrate modern structure prediction and molecular docking frameworks. Here, we present GalaxyMemMD, a fully automated, open-source Galaxy framework that provides a direct, scalable alternative to existing preparation-only tools. GalaxyMemMD integrates every stage membrane embedding, system preparation, MD execution, and downstream analysis into a single reproducible pipeline. Leveraging Galaxy's HPC-ready job scheduling, GalaxyMemMD natively supports high-throughput parallel simulations across multiple membrane compositions and protein variants, including AlphaFold-derived structural inputs and downstream molecular docking workflows, without repeated manual setup. We validate GalaxyMemMD across six structurally diverse membrane proteins four GPCRs, one ion channel, and one beta-barrel demonstrating consistent system preparation, while the benchmark analysis shows stable embeddings, and reproducible trajectory-level production workflow. Across these benchmarks, GalaxyMemMD matches the setup quality of established tools, therefore serves as both a practical alternative and a capability extension for the membrane protein simulation community.

bioinformatics↗

R2G2: A Python-R Framework for Seamless Integration of R/Bioconductor Tools into Galaxy

R is widely used in statistical computing, data analysis, and bioinformatics. A key contributor to its success in bioinformatics and computational biology is the open-source project Bioconductor. As of its latest release (3.20), the Bioconductor community offers 2,289 software packages for biomedical research, including genomic, transcriptomic, and proteomic analyses. Given Rs growing importance, integrating R and Bioconductor tools into platforms like Galaxy enhances accessibility, reproducibility, and scalability in bioinformatics workflows. The Galaxy Toolshed provides multiple tools leveraging R and Bioconductor packages. Additionally, various open-source public Galaxy servers, such as usegalaxy.org and usegalaxy.eu, already host several R and Bioconductor-based tools, highlighting the importance of past integration efforts. However, given the vast number of available packages, the full potential of R and Bioconductor within the Galaxy ecosystem remains underutilized. Galaxys web-based interface makes these powerful tools more accessible to researchers without programming expertise, fostering broader collaboration. Despite its advantages, integrating R packages into Galaxy can be complex. It requires XML wrappers to define inputs, outputs, and parameters, which can be time-consuming. Managing dependencies from CRAN and Bioconductor, resolving installation issues, and ensuring compatibility across different package versions further complicates the process. Many tools also require custom scripting, creating a steep learning curve for non-programmers. To address these challenges, we have developed a tool that automates the generation of Galaxy wrappers for R packages. This eliminates the need for manual XML writing, reduces complexity, and saves time. Our tool provides an intuitive interface for creating Galaxy-compatible tools without programming expertise and automates dependency management for seamless execution. Bioconductor has revolutionized bioinformatics, with thousands of researchers relying on its tools. Automating its integration into Galaxy removes technical barriers, democratizing access to advanced bioinformatics tools and workflows. Our solution bridges the gap between R-based analysis and user-friendly, scalable tools, ultimately advancing research accessibility and scientific discovery.

bioinformatics↗

Genomics to Notebook (g2nb): extending the electronic notebook to address the challenges of bioinformatics analysis

We present Genomics to Notebook (g2nb), an environment that combines the JupyterLab notebook system with widely-used bioinformatics platforms. Galaxy, GenePattern, and the JavaScript versions of IGV and Cytoscape are currently available within g2nb. The analyses and visualizations within those platforms are presented as cells in a notebook, making thousands of genomics methods available within the notebook metaphor and allowing notebooks to contain workflows utilizing multiple software packages on remote servers, all without the need for programming. The g2nb environment is, to our knowledge, the only notebook-based system that incorporates multiple bioinformatics analysis platforms into a notebook interface.

bioinformatics↗