Data Center
MTB Signed TRN
MTB Signed Transcriptional Regulatory Network
The following file is a supplementary material for Peterson et al.(submitted)
The compiled signed transcriptional regulatory network contains TF-gene interactions with both physical (detected with ChIP-seq experiments; Minch et al. 2015) and functional evidence (detected with TFOE transcriptional profiling; Rustad et al. 2014). Activation and repression are indicated with 1 and -1.
OTB supplement: Systems analysis of TB progression in RhCMV/MTB vaccinated NHP. Whole blood multi-omics profiling of MTB-challenged Rhesus macaques after vaccination with RhCMV and/or BCG.
Currently, data files in this section are private and download link will be available after login.
| Title | Body | Release Date | Sample Type |
|---|---|---|---|
| RhCMV-TB Metabolomics Data |
Whole blood metabolomics profiling of MTB-challenged Rhesus macaques after vaccination with RhCMV and/or BCG. Each row is a metabolite and each column is a monkey at the defined time point |
2018 | Metabolomics |
| RhCMV-TB Proteomics Data |
Whole blood proteomics profiling of MTB-challenged Rhesus macaques after vaccination with RhCMV and/or BCG |
2018 | Protein |
| RhCMV-TB Transcriptomic Data |
Whole blood transcriptomes of MTB-challenged Rhesus macaques after vaccination with RhCMV and/or BCG Log2 standardized counts (using edgeR) are reported. Each row is a gene and each column is a monkey at the defined time point. |
2018 | RNA |
| RhCMV-TB Meta Data |
Whole blood omics profiling of MTB-challenged Rhesus macaques after vaccination with RhCMV and/or BCG Meta-data information is listed. Columns are the monkey ID, which trial the monkey is from, the disease score after challenge, and the vaccination group. |
2018 | Meta-data |
How to use TFOE Searchable Worksheet
Using the TFOE transcriptional regulation network spreadsheet tool
Tige Rustad, Senior Scientist at Seattle Biomed Email: tige.rustad@seattlebiomed.org
What is the TFOE network?
- TFOE- transcription factor over expression
- 206 transcription factors (TFs) cloned into an inducible tagged expression vector
- Microarrays to identify genes differentialy expressed (DE)
DE =the set of genes repressed OR induced - ChIP-seq to identify DNA bound by each TF
- These two parallel approaches were published in:
Rustad & Minch et al. 2014. Mapping and manipulating the Mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network. Genome Biology 15:502 Minch & Rustad et al. 2014. The DNA binding network of Mycobacterium tuberculosis. Nature Communications
What does the spreadsheet do?
- Users input a gene or list of genes of interest (GOI). The spreadsheet then:
- Identifies all TFs that alter expression of any of your GOI
- Shows how many of your GOI are altered by each TF
- Calculates the significance of the overlap between your list of genes and the set of genes altered by each TF
- Identifies TFs that bind in upstream of the GOI
Getting started
- Download and open the most recent version of the spreadsheet at : http://networks.systemsbiology.net/mtb/content/TFOE-Searchable-Data-File
- The spreadsheet should open to the tab 'ListQuery'
- The spreadsheet consists of six tabs:
ListQuery- input GOI and see an overview of resultsTFOE.Readout- Expression changes in your GOI for each TFChIP.Readout- DNA binding proximal to your GOITFOE.data- Expression changes triggered by TFOE and p-value of each changeChIP.data- For each gene indicates which TFs bind directly upstream or upstream of gene earlier in the operonAnnot- Gene annotation pulled from Tuberculist and PATRIC
Step One: Choose your gene(s) of interest
Choose your genes of interest- at least one, up to 1,000
- For this example we will use the 101 reaeration induced genes from Sherrid et al.
You can access this list here: Reaeration Response.xlsx - Genes need to be in the format from the original sequencing of the H37Rv geneome (i.e. "Rv####" with a terminal 'c' if the gene is on the Crick strand)
Step Two: Input your genes of interest (GOI)
Paste your genes into the blue cells
#TFOEis the number of TFs that change expression that gene- You can change the
fold changeandp-valueconsidered significant by changing the values in the red cells. Default is log2 of 1 (two fold) and pvalue ≤ 0.01 - The total number of genes in your list and the number of those genes that are perturbed by at least one TF are indicated
Identify overlaps between GOI and each TF
- Calculates size and p-value of the overlap of your GOI with:
- All genes DE by a TF
- The TF induced genes
- The TF repressed genes
- The genes adjacent to TF DNA binding sites
Compare GOI and genes bound/DE by each TF
Data types for each comparison
IndNumber of your GOI induced/repressed/DETF.IndTotal number of genes induced/repressed/DEInd.RandOverlap expected by random chanceInd.SigP-value of an overlap of the given size or larger
Step Three: Sort the output
Sort the TFs as desired
Each column title has an arrow that pulls up sorting options
- Sorting by the significance or size of the overlap is generally the most useful view
- The order of TFs in the Output table determines the order of TFs in the 'Readout' tabs
Step Four: TFOE.Readout
- ‘Readout’ tabs show which genes are DE for each TF
- Values are log2 values (bolded indicates p-value ≤ 0.01)
- The ‘Sig’ values at the top show the p-value of the overlap between GOI and all DE genes
To investigate the MTB transcriptional landscape in a systematic manner, we developed a high-throughput approach to identify the genes controlled by nearly all predicted MTB TFs. We individually cloned and conditionally overexpressed 206 MTB TFs to induce the regulatory signature of each one. Using this approach we identified the sets of genes affected by TF overexpression (TFOE) and assembled them into an easily searchable map of transcriptional regulation in MTB.
Accessing large datasets like the TFOE expression data can be difficult when the data spreads over thousands of genes and hundreds of regulators. To address the difficulties usually associated with accessing large data sets, we have designed a simple Excel spreadsheet for querying TFOE data to find regulators of specific genes or sets of genes. (Rustad et al. Genome Biol. 2014)
Last update: 29/02/2015
Whole blood metabolomics profiling of MTB-challenged Rhesus macaques after vaccination with RhCMV and/or BCG. Each row is a metabolite and each column is a monkey at the defined time point
| Title | Gene | BioProject | GEO Series | Platform | Accession | Sample Method | Sample Type | References | Release Date | Repository | Contact |
|---|---|---|---|---|---|---|---|---|---|---|---|
| RhCMV-TB Metabolomics Data | Metabolomics | 2018 | |||||||||
| RhCMV-TB Proteomics Data | Protein | 2018 | |||||||||
| RhCMV-TB Transcriptomic Data | RNA-seq | RNA | 2018 | ||||||||
| RhCMV-TB Meta Data | in progress | NA | NA | Meta-data | in preparation | 2018 | in progress | ||||
| TFOE_8468_3911 | PRJNA254351 | GSE59086 | GPL14824 | GSM1427062 | Tiling Array | RNA | 25232098 | 4-Jul-14 | GEO | ||
| TFOE_8127_3911 | PRJNA254351 | GSE59086 | GPL14824 | GSM1427061 | Tiling Array | RNA | 25232098 | 4-Jul-14 | GEO | ||
| TFOE_2756_3911 | PRJNA254351 | GSE59086 | GPL14824 | GSM1427060 | Tiling Array | RNA | 25232098 | 4-Jul-14 | GEO | ||
| TFOE_7945_3862c_B | PRJNA254351 | GSE59086 | GPL14824 | GSM1427059 | Tiling Array | RNA | 25232098 | 4-Jul-14 | GEO | ||
| TFOE_6830_3862c | PRJNA254351 | GSE59086 | GPL14824 | GSM1427058 | Tiling Array | RNA | 25232098 | 4-Jul-14 | GEO | ||
| TFOE_4985_3862c_A | PRJNA254351 | GSE59086 | GPL14824 | GSM1427057 | Tiling Array | RNA | 25232098 | 4-Jul-14 | GEO |