Reverse Engineering Directed Gene Regulatory Networks from Transcriptomics and Proteomics Data of Biomining Bacterial Communities

Supplementary files for the submission:
Reverse Engineering Directed Gene Regulatory Networks from
Transcriptomics and Proteomics Data of Biomining Bacterial
Communities with Approximate Bayesian Computation and
Steady-State Signalling Simulations

DOI: 10.15490/fairdomhub.1.investigation.286.1

Created at: 17th Apr 2019 at 17:49

Contents

Supplementary files

Proteomics and transcriptomics data tables, sample IDs and description, source code

RNA data

Data derived from RNAseq

TPM counts

TPM counts for protein coding genes, alongside functional annotations

  • ALL_tpm_with_funcats.tsv

Sample information

Information for RNA and protein samples and cultures they were derived from.

  • Sample_Overview.xlsx

Proteome data

Data derived from protein samples

LFQ intensities

LFQ intensities derived from proteomics measurements

  • raw_lfqs_merged_nonfiltered_funcats.tsv

Sample information

Information for RNA and protein samples and cultures they were derived from.

  • Sample_Overview.xlsx

Simulations for network engineering

Scripts for running network simulations

TwinSensNet

Source code for running simulations

  • TwinSensNet.zip
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Citation
Herold, M., & Buetti-Dinh, A. (2019). Reverse Engineering Directed Gene Regulatory Networks from Transcriptomics and Proteomics Data of Biomining Bacterial Communities. FAIRDOMHub. http://doi.org/10.15490/fairdomhub.1.investigation.286.1
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Created: 17th Apr 2019 at 17:49

Last updated: 17th Apr 2019 at 17:50

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