SearcharxivSearch

arXiv · 2601.02283

An Automatic Pipeline for the Integration of Python-Based Tools into the Galaxy Platform: Application to the anvi'o Framework

Abstract

The integration of command-line tools into the Galaxy platform is crucial for making complex computational methods accessible to a broader audience and ensuring reproducible research. However, the manual development of tool wrappers is a time-consuming, error-prone, and knowledge-intensive process. This bottleneck significantly affects the rapid deployment of new and updated tools, creating a gap between tool development and its availability to the scientific community. We have developed a novel, automated approach that directly translates Python tool interfaces into Galaxy-compliant tool wrappers. Our method leverages the argparse library, a standard for command-line argument parsing in Python. By embedding structured metadata within the metavar attribute of input and output arguments, our system programmatically parses the tool's interface to extract all necessary information. This includes parameter types, data formats, help text, and input/output definitions. The system then uses this information to automatically generate a complete and valid Galaxy tool XML wrapper, requiring no manual intervention. To validate the scalability and effectiveness of our approach, we applied it to the anvi'o framework, a comprehensive and complex bioinformatics platform comprising hundreds of individual programs. Our method successfully parsed the argparse definitions for the entire anvi'o suite and generated functional Galaxy tool wrappers. The resulting integration allows for the seamless execution of anvi'o workflows within the Galaxy environment. This work presents a significant advancement in the automation of tool integration for scientific workflow systems. By establishing a convention-based approach using Python's argparse library, we have created a scalable and generalizable solution that dramatically reduces the effort required to make command-line tools available in Galaxy.

Explore related subjects

Keep this discovery

BibTeXRIS

Fabio Cumbo, Jayadev Joshi, Daniel Blankenberg. 2026-01-05. An Automatic Pipeline for the Integration of Python-Based Tools into the Galaxy Platform: Application to the anvi'o Framework. https://arxiv.org/abs/2601.02283

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Diversity of EML-type operators

The discovery of the EML operator, sufficient to evaluate the standard explicit purely transcendental elementary functions, has led to considerable interest and discussion across multiple scientific disciplines. However, most authors have focused on the binary EML itself, while numerous similar variants with slightly different properties are now known. This article attempts to close this gap by enumerating and classifying them. We also take this opportunity to clarify common misconceptions related to the EML operator. The principal goal, symbolic regression within an architecture as close as possible to proven neural networks which combine matrix multiplication with a single univariate non-linear activation function, remains beyond reach. Instead, we propose a M\"obius layer, with rational functions replacing matrix operations, and showcase the recently discovered activation function eml(x,1/x), which allows exp(x) and ln(x) to be recovered separately, and hence all elementary functions to be evaluated within a rational generalization of the neural network.

cs.SC

Physical Law Ecology: mapping multi-mechanism ecologies as the zeroth step of data-driven scientific discovery

Every data-driven equation discovery method assumes (implicitly and without verification) that the target system obeys a single governing law ($K{=}1$). Here we show that this assumption is the primary bottleneck limiting scientific discovery in multi-mechanism systems, and introduce Physical Law Ecology, a framework that makes $K^*$ (the number of coexisting independent mechanisms) itself the first quantity to be determined from data. The framework automatically mines a pool of topologically distinct candidate equations, constructs a continuous dominance weight field across parameter space, and discovers analytic evolution laws governing mechanism succession---with optional monotonicity constraints encoding irreversible physics. Across four unrelated systems (elastomer mechanics, pool boiling, galactic dynamics, and droplet evaporation), BIC consistently identifies $K^*{=}3$ independent governing topologies. Applied to 163 SPARC galaxies (3,269 spatially resolved measurements), the framework autonomously recovers three gravitational laws whose coexistence provides evidence against the single-universal-acceleration hypothesis of MOND ($p<10^{-34}$). In engineering applications, multi-law weighted prediction reduces error by 67-72\% over single-equation baselines while retaining full interpretability. By establishing the determination of $K^*$ as the zeroth step of scientific discovery-prior to and independent of equation search---this work opens a direction orthogonal to existing symbolic regression: not finding better equations, but mapping the ecology of mechanisms that govern complex systems.

cs.SC