BioFrame: Enhancing Reproducibility and Accessibility in Genomics through Web-Based Workflow Design
Al Hinai, M. S.; Naureen, Z.; Gilani, S. A. A.
Show abstract
Since, Bioinformatic research is getting attraction that can be seen with sudden increase in development of tools as well as publications. However, there are challenges in bioinformatics which are making the results difficult to obtain while facing reproducibility, scalability, and accessibility of computational workflows. Though command line tools are powerful but majority of the bioinformatic researchers have little or no expertise in the extensive computational skills. To address these issues, we present here the easy to use and getting results with ease through our tool, BioFrame. BioFrame employs a containerized microservices architecture orchestrated via Docker Compose, separating concerns across presentation, orchestration, execution, and storage layers. The bioinformatic tools are encapsulated here as versioned container images with declarative metadata schemas, enabling dynamic tool discovery and parameter validation. Therefore, through user designed workflows one can easily get directed acyclic graphs while using the web interface.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.