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Related Experiment Video

Updated: May 16, 2026

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Nestly--a framework for running software with nested parameter choices and aggregating results.

Connor O McCoy1, Aaron Gallagher, Noah G Hoffman

  • 1Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. cmccoy@fhcrc.org

Bioinformatics (Oxford, England)
|December 11, 2012
PubMed
Summary

Scientists can now streamline bioinformatics workflows with nestly, a Python package for organizing parameter combinations and aggregating results. This tool simplifies running complex computational tasks and managing experimental data efficiently.

Related Experiment Videos

Last Updated: May 16, 2026

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Software Development

Background:

  • Executing software applications with diverse parameter and input combinations is a frequent yet complex task in bioinformatics.
  • Current methods often necessitate intricate custom scripting, lacking standardization and efficiency.
  • A specialized tool is needed to organize, streamline, and formalize this process for researchers.

Purpose of the Study:

  • To introduce nestly, a novel Python package designed to simplify the execution of bioinformatics tools with nested parameter and input combinations.
  • To provide a formalized and efficient solution for managing complex computational experiments.
  • To enhance reproducibility and ease of analysis in scientific research.

Main Methods:

  • Developed nestly as a Python package with three core components.
  • Implemented a module for generating nested directory structures based on parameter choices.
  • Created the `nestrun` script for executing commands across all parameter combinations and the `nestagg` script for aggregating results into CSV files.
  • Integrated a module for defining nested dependencies within the SCons build system for incremental builds.

Main Results:

  • Successfully created a Python package, nestly, that automates the organization and execution of bioinformatics pipelines.
  • Demonstrated the ability to systematically explore parameter spaces and collect results.
  • Enabled efficient aggregation of results into a structured CSV format for further analysis.

Conclusions:

  • Nestly provides a robust and user-friendly solution for managing complex computational tasks in bioinformatics.
  • The package significantly reduces the need for complex scripting, saving researchers time and effort.
  • Nestly enhances the reproducibility and efficiency of scientific workflows, facilitating data analysis and discovery.