Machine learning uncovers independently regulated modules in the <i>Bacillus subtilis</i> transcriptome

Rychel, Kevin; Sastry, Anand V.; Kim, Jaehyung

doi:10.1101/2020.04.26.062638

Cited by 14 publications

(35 citation statements)

References 65 publications

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“…Prior to running ICA, we ensure that transcriptomic data passes stringent quality control as described in each dataset's original publication (21)(22)(23). For RNA-Seq data, genes shorter than 100 nucleotides or with under 10 fragments per million-mapped reads are removed.…”

Section: Quality Control and Preprocessingmentioning

confidence: 99%

“…To transform an independent component into an iModulon, we iteratively remove the highest absolute weighted genes from an iModulon until the D'Agostino K 2 statistic of normality (35) of the remaining distribution falls below an organism-specific cutoff, and take all removed genes to be iModulon members. See the original publications for additional details (21)(22)(23).…”

Section: Computing Robust Imodulonsmentioning

confidence: 99%

“…Regulators are not necessarily part of their own iModulons. The computation of these correlations is described in the original dataset publications (21)(22)(23). In some cases, the correlation is best captured using a broken line (as in Fig.…”

Section: Imodulon Pages Present Information-dense Interactive Analytimentioning

confidence: 99%

“…We recently applied ICA to high quality transcriptomic datasets for three species of bacteria: Escherichia coli (21), Staphylococcus aureus (22), and Bacillus subtilis (23). In our work with E. coli, we termed the independent signals "iModulons", for independently modulated gene sets.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

Rychel¹,

Decker²,

Sastry³

et al. 2020

Preprint

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Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.com), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (E. coli, S. aureus, and B. subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.

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Section: Quality Control and Preprocessingmentioning

confidence: 99%

Section: Computing Robust Imodulonsmentioning

confidence: 99%

Section: Imodulon Pages Present Information-dense Interactive Analytimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

Rychel¹,

Decker²,

Sastry³

et al. 2020

Preprint

Self Cite

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“…In each of these strains the SOS-response regulator LexA was no longer activated by hydrogen peroxide treatment, indicating that the mutations reduced DNA damage from reactive oxygen species through constitutive activation of OxyR. I-modulons have also been successfully extracted for additional species, disentangling the transcriptional trajectory of a Bacillus subtilis sporulation time-course (Rychel et al, 2020) , and identifying media-specific transcriptional responses in Staphylococcus aureus (Poudel et al, 2020) .…”

Section: Introductionmentioning

confidence: 99%

Decomposition of transcriptional responses provides insights into differential antibiotic susceptibility

Sastry

Dillon

Poudel

et al. 2020

Preprint

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Responses of bacteria to antibiotic treatments depend on their environments. Differences between in vitro testing conditions and the physiological environments inside patients have resulted in poor antibiotic susceptibility predictions, contributing to treatment failures in the clinic. Here, we investigate how media composition affects antibiotic susceptibility in the laboratory strain E. coli K-12 MG1655, and contextualize these changes through machine learning of transcriptomics data. We show that complex transcriptional changes induced by different media or antibiotic treatment can be traced back to a few key regulators. Integration of results from machine learning with biochemical knowledge reveals fundamental shifts in respiration and iron availability that may explain media-dependent differential susceptibility to antibiotics.

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iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

Rychel

Decker

Sastry

et al. 2020

Nucleic Acids Research

Self Cite

108

View full text Add to dashboard Cite

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (Escherichia coli, Staphylococcus aureus and Bacillus subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.

show abstract

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

Cited by 14 publications

References 65 publications

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

Decomposition of transcriptional responses provides insights into differential antibiotic susceptibility

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

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