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Reconstructing the Transcriptional Regulatory Network of Probiotic L. reuteri is Enabled by Transcriptomics and Machine Learning

Authors: Josephs-Spaulding, Jonathan; Rajput, Akanksha; Hefner, Ying; Szubin, Richard; Balasubramanian, Archana; Li, Gaoyuan; Zielinski, Daniel; +4 Authors

Reconstructing the Transcriptional Regulatory Network of Probiotic L. reuteri is Enabled by Transcriptomics and Machine Learning

Abstract

LactoPRECISE: A comprehensive framework exploring Lactobacillus reuteri's genetic diversity, regulatory networks, and functional annotations for personalized medicine and microbial therapeutics. This repository presents a computational workflow to compute and characterize all iModulons for a selected organism. This occurs in five steps: Gather all publicly available RNA-seq data for the organism (Step 1) Process the RNA-seq data (Step 2) Inspect data to identify high-quality datasets (Step 3) Compute iModulons (Step 4) Characterize iModulons using PyModulon (Step 5) iModulons are independently-modulated groups of genes that are computed through Independent Component Analysis (ICA) of a gene expression dataset. To learn more about iModulons or explore published iModulons, visit iModulonDB or see our publications for Escherichia coli, Staphylococcus aureus, or Bacillus subtilis. A pre-print is being prepared for this tutorial workflow.

Keywords

Machine Learning, L. reuteri, Systems Biology, Transcriptome, Probiotic

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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