Mining all publicly available expression data to compute dynamic microbial transcriptional regulatory networks

Abstract

We are firmly in the era of biological big data. Millions of omics datasets are publicly accessible and can be employed to support scientific research or build a holistic view of an organism. Here, we introduce a workflow that converts all public gene expression data for a microbe into a dynamic representation of the organism's transcriptional regulatory network. This five-step process walks researchers through the mining, processing, curation, analysis, and characterization of all available expression data, using Bacillus subtilis as an example. The resulting reconstruction of the B. subtilis regulatory network can be leveraged to predict new regulons and analyze datasets in the context of all published data. The results are hosted at https://imodulondb.org/, and additional analyses can be performed using the PyModulon Python package. As the number of publicly available datasets increases, this pipeline will be applicable to a wide range of microbial pathogens and cell factories.

Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

“…After filtering the profiles based on quality criteria (see Methods), we compiled a transcriptomic compendium containing 364 samples (83 new + 281 public expression profiles) ( Figure 1d and Supplementary Figure S1c ). All the samples were shown to have Pearson’s correlation coefficient (PCC) of 0.97 between replicates 4 . To eliminate batch effects, each individual experiment was normalized to a reference condition prior to calculating the iModulons 4 .…”

“…All the samples were shown to have Pearson’s correlation coefficient (PCC) of 0.97 between replicates 4 . To eliminate batch effects, each individual experiment was normalized to a reference condition prior to calculating the iModulons 4 .…”

“…The RNAseq reads were processed and quality control was done. Further, the independent component analysis (ICA) was applied to generate the iModulons that were characterized to get the regulatory networks of P. aeruginosa (Adapted from Sastry et al 4 ). b) ICA calculates the independently modulated sets of genes (iModulons).…”

“…Thus, elucidation of these regulatory mechanisms would be beneficial to designing new or combinatorial therapies against P. aeruginosa infections. Today, machine learning approaches can be used to establish TRN structure in bacteria if there is sufficient transcriptomic data available for analysis 4 .…”

Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius

“…The final high-quality S. acidocaldarius compendium contained 95 RNA-seq datasets (Figure 1B, 1C). As part of the quality control procedure previously described (Sastry et al, 2021b), we performed manual curation of experimental metadata to identify which samples were biological replicates. We also examined the literature to identify each sample's strain, media, additional treatments, environmental parameters/changes, and growth stage, if reported.…”