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. 2023 Apr 3;39(4):btad143.
doi: 10.1093/bioinformatics/btad143.

Phenonaut: multiomics data integration for phenotypic space exploration

Steven Shave  1   2 , John C Dawson  1 , Abdullah M Athar  2 , Cuong Q Nguyen  3 , Richard Kasprowicz  2 , Neil O Carragher  1
Affiliations

Phenonaut: multiomics data integration for phenotypic space exploration

Steven Shave et al. Bioinformatics. .

Abstract

Summary: Data integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high-content imaging, proteomics, metabolomics, and other omics data. We present Phenonaut, a Python software package designed to address the data workflow needs of migration, control, integration, and auditability in the application of literature and proprietary techniques for data source and structure agnostic workflow creation.

Availability and implementation: Source code: https://github.com/CarragherLab/phenonaut, Documentation: https://carragherlab.github.io/phenonaut, PyPI package: https://pypi.org/project/phenonaut/.

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Figures

Figure 1
Figure 1
Phenonaut exemplified using its packaged dataset loaders. Lower left: Multiomics dataset combinations for the Cancer Genome Atlas are profiled with hyperparameter optimized classifiers to predict 1 year donor survival rate. Lower right: Phenotypic metrics are profiled against A549 cell line CRISPR repeats in Connectivity Map and assessed via AUROC scores to as to their repeat enrichment in ranked hit lists
See this image and copyright information in PMC

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