iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia Article Swipe
Anand V. Sastry
,
Yuan Yuan
,
Saugat Poudel
,
Kevin Rychel
,
Reo Yoo
,
Cameron Lamoureux
,
Gaoyuan Li
,
Jeffrey T. A. Burrows
,
Siddharth Chauhan
,
Zachary B. Haiman
,
Tahani Al Bulushi
,
Yara Seif
,
Bernhard Ø. Palsson
,
Daniel C. Zielinski
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1012546
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pcbi.1012546
Public gene expression databases are a rapidly expanding resource of organism responses to diverse perturbations, presenting both an opportunity and a challenge for bioinformatics workflows to extract actionable knowledge of transcription regulatory network function. Here, we introduce a five-step computational pipeline, called iModulonMiner, to compile, process, curate, analyze, and characterize the totality of RNA-seq data for a given organism or cell type. This workflow is centered around the data-driven computation of co-regulated gene sets using Independent Component Analysis, called iModulons, which have been shown to have broad applications. As a demonstration, we applied this workflow to generate the iModulon structure of Bacillus subtilis using all high-quality, publicly-available RNA-seq data. Using this structure, we predicted regulatory interactions for multiple transcription factors, identified groups of co-expressed genes that are putatively regulated by undiscovered transcription factors, and predicted properties of a recently discovered single-subunit phage RNA polymerase. We also present a Python package, PyModulon, with functions to characterize, visualize, and explore computed iModulons. The pipeline, available at https://github.com/SBRG/iModulonMiner , can be readily applied to diverse organisms to gain a rapid understanding of their transcriptional regulatory network structure and condition-specific activity.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pcbi.1012546
- OA Status
- gold
- Cited By
- 17
- References
- 58
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4403678800Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pcbi.1012546Digital Object Identifier
- Title
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iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendiaWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-23Full publication date if available
- Authors
-
Anand V. Sastry, Yuan Yuan, Saugat Poudel, Kevin Rychel, Reo Yoo, Cameron Lamoureux, Gaoyuan Li, Jeffrey T. A. Burrows, Siddharth Chauhan, Zachary B. Haiman, Tahani Al Bulushi, Yara Seif, Bernhard Ø. Palsson, Daniel C. ZielinskiList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pcbi.1012546Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1371/journal.pcbi.1012546Direct OA link when available
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Computational biology, Software, Gene expression, Computer science, Bioinformatics, Biology, Gene, Genetics, Programming languageTop concepts (fields/topics) attached by OpenAlex
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17Total citation count in OpenAlex
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2025: 15, 2024: 2Per-year citation counts (last 5 years)
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58Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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