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Reto Gerber

Publications and source records attributed to Reto Gerber.

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Omnibenchmark: transparent, reproducible, extensible and standardized orchestration of solo and collaborative benchmarks

Benchmarking involves designing, running and disseminating rigorous performance assessments of methods, most often for data analysis and software tools, but the process can also be applied to experimental systems. Ideally, a benchmarking system is used to facilitate the benchmarking process by providing a structured entrypoint to design, coordinate, execute, and store standardized benchmarks. We describe a novel benchmarking system, Omnibenchmark, that facilitates benchmark formalization and execution in both solo and community efforts. Omnibenchmark provides a flexible benchmark plan syntax (i.e., a configuration YAML file), dynamic workflow generation based on Snakemake, S3-compatible storage handling, and reproducible software environments using environment modules, Apptainer or Conda. Such a setup provides an unprecedented flexibility such that existing benchmark designs can be forked and extended, run separately or collaboratively, giving versioned and standardized result outputs and therefore much-needed transparency to the analysis and interpretation of benchmark results. Tutorials and installation instructions are available from https://omnibenchmark.org.

q-bio.OT

Building a continuous benchmarking ecosystem in bioinformatics

Benchmarking, which involves collecting reference datasets and demonstrating method performances, is a requirement for the development of new computational tools, but also becomes a domain of its own to achieve neutral comparisons of methods. Although a lot has been written about how to design and conduct benchmark studies, this Perspective sheds light on a wish list for a computational platform to orchestrate benchmark studies. We discuss various ideas for organizing reproducible software environments, formally defining benchmarks, orchestrating standardized workflows, and how they interface with computing infrastructure.

q-bio.OT

Ten simple rules for collaborating with wet lab researchers for computational researchers

Computational biologists are frequently engaged in collaborative data analysis with wet lab researchers. These interdisciplinary projects, as necessary as they are to the scientific endeavour, can be surprisingly challenging due to cultural differences in operations and values. In these Ten Simple Rules guide we aim to help dry lab researchers identify sources of friction; and provide actionable tools to facilitate respectful, open, transparent and rewarding collaborations.

q-bio.OT