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macpie: Scalable workflow for high-throughput transcriptomic profiling.

Nenad Bartonicek1,2, Xin Liu1,3,4, Laura Twomey1

  • 1Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia.

Computational and Structural Biotechnology Journal
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces macpie, an R package simplifying high-throughput transcriptomic profiling (HTTr) data analysis from plate-based screens. It offers a streamlined pipeline for reproducible and scalable transcriptome-wide screening.

Keywords:
Data integrationHigh-throughput transcriptomicsPlate-based screensSoftware

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput transcriptomic profiling (HTTr) is crucial for studying cellular responses to various stimuli.
  • Plate-based HTTr technologies like MAC-Seq, TempO-seq, and PLATE-seq offer scalability but present computational challenges for data analysis and integration.
  • Existing analytical tools often lack comprehensive features for handling the complexities of plate-based HTTr data.

Purpose of the Study:

  • To develop an accessible and efficient R package for analyzing plate-based high-throughput transcriptomic profiling data.
  • To provide a unified analytical framework that streamlines the entire workflow from data preprocessing to advanced analyses.
  • To enhance the reproducibility and scalability of transcriptome-wide screening studies.

Main Methods:

  • Development of the 'macpie' R package, built upon the tidySeurat framework.
  • Implementation of a comprehensive analytical pipeline including preprocessing, quality control, pathway enrichment, and chemical feature extraction.
  • Integration of multimodal data analysis capabilities.
  • Utilization of Docker and Nextflow for ensuring reproducibility and facilitating ease of use.
  • Incorporation of parallelization for enhanced scalability.

Main Results:

  • The 'macpie' package effectively streamlines the analysis of plate-based HTTr data.
  • It enables seamless integration of data from different modalities.
  • The package ensures reproducibility and scalability through the use of Docker and Nextflow.
  • It simplifies complex analytical tasks, making HTTr data analysis more accessible.

Conclusions:

  • 'macpie' provides a robust and user-friendly solution for analyzing high-throughput transcriptomic profiling data from plate-based screens.
  • The package facilitates deeper insights into chemical and genetic perturbations through efficient data modeling and integration.
  • 'macpie' empowers researchers to conduct scalable and reproducible transcriptome-wide screening studies with greater ease.