Trans-Phar (integration of Transcriptome-wide association study and Pharmacological database)
This software achieves in silico screening of chemical compounds, which have inverse effects in expression profiles compared with genetically regulated gene expression of common diseases, from large-scale pharmacological database (Connectivity Map [CMap] L1000 library).
Human Molecular Genetics 2021 Konuma T, Ogawa K, Okada Y. "Integration of genetically regulated gene expression and pharmacological library provides therapeutic drug candidates." https://doi.org/10.1093/hmg/ddab049
- R
- dichromat (R package) (installing by install.packages("dichromat"))
- python 3.X
- scipy
- numpy
- pandas
- math
- cycler
- kiwisolver
- FOCUS (Fine-mapping Of CaUsal gene Sets) as TWAS software
In order to get started with Trans-Phar, you can just clone this repo as follows;
git lfs clone https://github.com/konumat/Trans-Phar.git
cd ./Trans-Phar
#unzip QCed Cmap L1000 data
cd ./Cmap_QCeddata
for filename in $( ls *.gz ); do
echo ${filename}
gunzip ${filename}
done
cd ../You have to install [FOCUS (Fine-mapping Of CaUsal gene Sets) soft ware] (https://github.com/bogdanlab/focus) as follows. For detailed explanations, please visit [the original repository and installing tutorial] (https://github.com/bogdanlab/focus) and [wiki] (https://github.com/bogdanlab/focus/wiki).
When installing FOCUS, please make focus folder under the Trans-Phar folder.
git clone https://github.com/bogdanlab/focus.git
cd ./focus
python setup.py install
cd ../All you need is a text file with GWAS summary statistics. (A file extension is .sumstats)
| Column | Column name | Descriptions |
|---|---|---|
| 1 | CHR | Chromosome |
| 2 | SNP | rsID |
| 3 | BP | BP position |
| 4 | A1 | Effect allele |
| 5 | A2 | Other allele |
| 6 | MAF | Minor allele frequency (optional) |
| 7 | N | #Samples |
| 8 | BETA | Beta (effect allele) |
| 9 | P | P-value |
Please have a look at an example input at ./tutorial_input/Schizo.sumstats.
mkdir ./Input_GWASsummary
mkdir ./Input_GWASsummary_done
mkdir ./Output
#if you use tutorial GWAS summary data;
gunzip ./tutorial_input/Schizo.sumstats.gz
cp ./tutorial_input/Schizo.sumstats ./Input_GWASsummary- If you input ICD-10 code (for example, F20 for Schizophrenia as below), you will also get gold-standard drug (approved drugs for ICD-10 F20 in ChEMBL and TTD [Therapeutic Target Database]) in an output Q-Q plot data. ICD-10 codes which are not listed in ChEMBL and TTD are not applicable. The example command is as follows;
cd ./script
./Trans-Phar.sh F20or 2) If you need not get gold-standard Q-Q plot, you only enter the example command as follows;
cd ./script
./Trans-Phar.sh- The example TWAS result outputs are as follows (if you use tutorial GWAS data);
#TWAS results according to each 29 GTEx (v7) tissue and combined files from all 29 tissues at Output/Schizo/TWASresults.
#For Example
cd ../Output/Schizo/TWASresults/ALLTISSUE
less GTEx_Adipose_Subcutaneous.chr_all.focus_shaped.tsv #TWAS result file (shaped), file format is described in https://github.com/bogdanlab/focus/wiki/Fine-mapping-TWAS-associations
#TWAS result png files are also in Output/Schizo/TWASresults/ALLTISSUE- The example Spearman result outputs are as follows (if you use tutorial GWAS data);
#Output p-values for Negative Spearmans's correlation tests according to total 308,872 pairs of TWAS tissue - CMap cell - Compunds
#Data of TWAS tissue - CMap cell - Compunds whose P-value < 0.0001 are in Output/Schizo/Spearmanresults/spearman_eachpair_results and Output/Schizo/Spearmanresults/spearman_eachpair_coplots
#For Example
cd ../../Spearmanresults/spearman_totalresults
less ALLpairs_spearmanresults.txt
#Q-Q plot for distribution of these P-value is also in Output/Schizo/Spearmanresults/spearman_totalresults- The original FOCUS was written by Nicholas Mancuso et al.
This software is freely available for academic users. Usage for commercial purposes is not allowed. Please refer to the LICENCE page.