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AxioParse: streamlining Axiom Microbiome assay data processing and dataset generation.

Pranav Kirti1, Pirooz Eghtesady1, Mathieu Garand1

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Summary
This summary is machine-generated.

A new pipeline, AxioParse, streamlines microbiome data analysis from the Applied Biosystems Axiom Microbiome Array. This open-source tool automates preprocessing, making data compatible with QIIME2 and R for enhanced research reproducibility.

Keywords:
AxiomMicrobiomePythonassaymicroarraypipeline

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • The Applied Biosystems Axiom Microbiome Array facilitates high-throughput detection of diverse microorganisms.
  • Native software outputs from the array require significant preprocessing for compatibility with standard bioinformatics tools.
  • A need exists for open-source pipelines to automate this data preparation.

Purpose of the Study:

  • To develop an automated, open-source pipeline for processing Axiom Microbiome Array data.
  • To enhance compatibility of microbiome array data with common downstream analysis platforms.
  • To improve the reproducibility and accessibility of microbiome research utilizing this array.

Main Methods:

  • Developed AxioParse, a Python-based pipeline utilizing the Dagster orchestration framework.
  • Implemented automated data cleaning, taxonomic mapping, and formatting functionalities.
  • Ensured output compatibility with QIIME2 and R analysis environments.

Main Results:

  • AxioParse successfully automates the preprocessing of Axiom Microbiome Array data.
  • The pipeline reduces manual processing efforts, saving time and resources.
  • Generated datasets are readily usable in QIIME2 and R, facilitating complex analyses.

Conclusions:

  • AxioParse addresses a critical gap in microbiome data analysis workflows.
  • The pipeline enhances the utility of the Axiom Microbiome Array for researchers.
  • AxioParse promotes reproducible and efficient microbiome research.