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AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines.

Sehyun Oh1,2, Kai Gravel-Pucillo1,2, Marcel Ramos1,2

  • 1Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, New York, USA.

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|December 13, 2024
PubMed
Summary
This summary is machine-generated.

The AnVILWorkflow R package simplifies accessing large-scale genomics data and analysis tools on the AnVIL platform. This package empowers researchers by enabling convenient execution of complex bioinformatics workflows directly within R.

Keywords:
AnVILCloud computingGenomicsR/BioconductorWorkflows

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Advancements in sequencing and data collection generate massive biological datasets.
  • The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) offers cloud-based access to genomics data and tools.
  • Executing complex bioinformatics workflows on AnVIL can be challenging for researchers lacking extensive bioinformatics expertise.

Purpose of the Study:

  • To introduce the AnVILWorkflow R package, designed to simplify the execution of AnVIL-hosted bioinformatics workflows.
  • To lower the barrier for researchers to utilize AnVIL's scalable computing resources and large-scale genomics datasets.
  • To enable seamless integration of R and non-R tools within reproducible analysis pipelines.

Main Methods:

  • Development of the AnVILWorkflow R package with intuitive functions for cloud environment setup, data formatting, and result retrieval.
  • Demonstration of AnVILWorkflow utility through three distinct use cases: bulk RNA-seq analysis (Salmon), metagenomics analysis (bioBakery), and digital pathology image processing (PathML).
  • Leveraging R syntax for user-friendly interaction with AnVIL resources.

Main Results:

  • AnVILWorkflow provides simplified access to AnVIL's data and analysis tools.
  • The package facilitates the execution of complex bioinformatics workflows, including bulk RNA-seq, metagenomics, and digital pathology.
  • Key features include user-friendly data/workflow browsing, integration of R/non-R tools, and access to scalable computing without direct management.

Conclusions:

  • AnVILWorkflow significantly lowers the barrier for researchers to leverage AnVIL's capabilities, particularly for exploratory analyses and bulk processing.
  • The package empowers a broader research community to utilize advanced genomics tools and datasets using familiar R syntax.
  • AnVILWorkflow enhances reproducibility and accessibility in large-scale genomics data analysis.