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GeTPRA¶

The GeTPRA framework

This project is to develop a framework that systematically predicts Gene-Transcript-Protein-Reaction Associations (GeTPRA) in human metabolims and updates a human genome-scale metabolic model (GEM) accordingly. This source code implements the GeTPRA framework.

Features This source code executes following steps in the GeTPRA framework in order:

Get reactions from biochemical database using EC number
Standardize metabolite information
Compartmentalize metabolic reactions
Generate GeTPRA
Check ‘Exist in Recon 2M.1’
Check ‘Blocked reaction’
Check ‘Experimental evidence available’

See below for the implementation of EFICAz and Wolf PSort (optional features)

Installation Procedure Note: This source code was developed in Linux, and has been tested in Ubuntu 14.04.5 LTS (i7-4770 CPU @ 3.40GHz)

Clone the repository
Create and activate virtual environment

$ virtualenv venv
$ source venv/bin/activate

Install packages at the root of the repository

$ pip install pip --upgrade
$ pip install -r requirements.txt

Input files for the GeTPRA framework Following working input files can be found in: getpra/input_data/getpra_inputs/. These files were used for the data presented in the manuscript.

Gene-transcript ID annotation file format - Download gene-transcript ID annotation file from [Ensembl BioMarts](http://www.ensembl.org/biomart/martview)

NCBI gene ID        Gene stable ID  Transcript stable ID    RefSeq mRNA ID  UCSC Stable ID
      ENSG00000119392 ENST00000309971 NM_001003722    uc004bvj.4
      ENSG00000119392 ENST00000372770 NM_001499       uc004bvi.4
      ENSG00000126067 ENST00000373237 NM_002794       uc001bzf.4
      ENSG00000126067 ENST00000373237 NM_001199779    uc001bzf.4
      ENSG00000126067 ENST00000621781 NM_001199780    uc021olh.3

Download procedure

Go to [Ensembl BioMarts](http://www.ensembl.org/biomart/martview)
Click Dataset on the left menu

Select Ensembl Genes 89 in the drop-down menu CHOOSE DATABASE

Select Human genes (GRCh38.p10) in the drop-down menu CHOOSE DATASET
Click Filters on the left menu

Click GENE: in the main menu (center)

Check Gene type and select protein_coding
Click Attributes on the left menu

Check Features in the center
Click both GENE: and EXTERNAL: in the main menu (center)
GENE: -> Ensembl -> Uncheck Gene stable ID and Transcript stable ID

Check following items in order:
- EXTERNAL: -> External References (max 3) -> NCBI gene ID
- GENE: -> Ensembl -> Gene stable ID
- GENE: -> Ensembl -> Transcript stable ID
- EXTERNAL: -> External References (max 3) -> RefSeq mRNA ID
- EXTERNAL: -> External References (max 3) -> UCSC Stable ID
Click the button Results on the top left
Click the button Go in the top center

File names in the source:
- Ensembl_GRCh38_EnsemblDB_v84.txt
- Ensembl_GRCh38_EnsemblDB_v85.txt
- Ensembl_GRCh38_EnsemblDB_v86.txt
- Ensembl_GRCh38_EnsemblDB_v87.txt
- Ensembl_GRCh38_EnsemblDB_v88.txt

`chem_xref.tsv` from `MetaNetX` - Download [chem_xref.tsv](http://www.metanetx.org/cgi-bin/mnxget/mnxref/chem_xref.tsv) from [MetaNetX](http://www.metanetx.org/) - File name in the source: chem_xref.tsv

`gene2ensembl.gz` from `NCBI FTP` - Download [gene2ensembl.gz](ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2ensembl.gz) from [NCBI FTP](ftp://ftp.ncbi.nlm.nih.gov/) - File name in the source: gene2ensembl.gz

`appris_data.principal.txt` from `APPRIS` - Download [appris_data.principal.txt](http://apprisws.bioinfo.cnio.es/pub/current_release/datafiles/homo_sapiens/GRCh38/appris_data.principal.txt) for annotation of principal isoform from [APPRIS](http://appris.bioinfo.cnio.es/) - File name in the source: appris_data.principal.txt

`subcellular_location.csv` from `The Human Protein Atlas` - Download [subcellular_location.csv](http://www.proteinatlas.org/download/subcellular_location.csv.zip) with subcellular localization information from [The Human Protein Atlas](http://www.proteinatlas.org/) - File name in the source: subcellular_location.csv

Recon model - Prepare a human genome-scale metabolic model with consistent TPR associations and that shows biologically reasonable simulation performance - File name in the source: Recon2M.1_Entrez_Gene.xml

EFICAz output file as input file - File name in the source: 20170110_EFICAz_result.txt. - EFICAz can be run with a different set of peptide sequences (see below).

WoLF PSort output file as input file - File name in the source: 20170110_WoLFPSort_result.txt - WoLF PSort can be run with a different set of peptide sequences (see below).

BRENDA data - Set user email address and password before implementing the GeTPRA framework. The framework programmatically fetches BRENDA data from BRENDA through its API.

##Implementation Note: All the arguments shown below should be provided when implementing the framework

Note: Make sure to provide own information for -brenda_email and -brenda_pw

Note: Implementation of this source code takes long (~ 8 h)

$ python run_GeTPRA_framework.py \
    -output_dir ./getpra_results/ \
    -ec ./input_data/getpra_inputs/20170110_EFICAz_result.txt \
    -sl ./input_data/getpra_inputs/20170110_WoLFPSort_result.txt \
    -brenda_email user_email_address \
    -brenda_pw user_password \
    -mnx_xref ./input_data/getpra_inputs/chem_xref.tsv \
    -ensembl ./input_data/getpra_inputs/Ensembl_GRCh38_EnsemblDB_v88.txt \
    -appris ./input_data/getpra_inputs/appris_data.principal.txt \
    -model ./input_data/getpra_inputs/Recon2M.1_Entrez_Gene.xml \
    -hpa ./input_data/getpra_inputs/subcellular_location.csv \
    -ncbi_id_information ./input_data/getpra_inputs/gene2ensembl.gz

##Output files from the GeTPRA framework - Raw output files from the GeTPRA framework, which were used for the publication, are available in: getpra/getpra_results_publication_version/ - New output files upon implementation of the framework are generated in: getpra/getpra_results/. This folder is automatically created.

Implementation of EFICAz and Wolf PSort (optional) Output files of EFICAz and Wolf PSort serve as input files for the GeTPRA framework.

Peptide sequences of metabolic genes as inputs for EFICAz and Wolf PSort - Get peptide sequences of metabolic genes by implementing ./input_data/get_peptide_sequences.py

$ python ./input_data/get_peptide_sequences.py \
-output_dir ./input_data/getpra_inputs/ \
-model ./input_data/getpra_inputs/Recon2M.1_Entrez_Gene.xml \
-ensembl ./input_data/getpra_inputs/Ensembl_GRCh38_EnsemblDB_v88.txt

File name in the source: Ensembl_peptide_seq_metabolic_genes.fa

>ENSP00000452494|ENST00000448914 TGGY >ENSP00000488240|ENST00000631435 GTGG >ENSP00000487941|ENST00000632684 GTGG >ENSP00000451515|ENST00000434970 PSY >ENSP00000451042|ENST00000415118 EI

Installation 1. Download [EFICAz2.5](http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.html)

Set environment variable for EFICAz2.5

$ export EFICAz25_PATH="[insert-destination-directory]/EFICAz2.5.1/"
$ export PATH="${PATH}:${EFICAz25_PATH}"

Download [WoLF PSort](https://github.com/fmaguire/WoLFPSort)
Set environment variable for WoLF PSort

$ export WoLFPSort_PATH="[insert-destination-directory]/WoLFPSort/"
$ export PATH="${PATH}:${WoLFPSort_PATH}"

Implementation of EFICAz and Wolf PSort - Predict EC numbers using [EFICAz2.5](http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.html)

$ python $EFICAz25_PATH/eficaz2.5 Ensembl_peptide_seq_metabolic_genes.fa

Predict subcellular localization using [WoLF PSort](https://github.com/fmaguire/WoLFPSort)

$ WoLFPSort_PATH/bin/runWolfPsortSummary animal < Ensembl_peptide_seq_metabolic_genes.fa

Output files from the above two implementations using peptide sequences of entire human genes are available in:
- EFICAz: getpra/input_data/20170110_EFICAz_result_using_all_human_genes.txt
- Wolf PSort: getpra/input_data/20170110_WoLFPSort_result_using_all_human_genes.txt

Extract metabolic genes from EFICAz and Wolf PSort output data obtained with entire human genes - Extract metabolic genes from the EFICAz and Wolf PSort output data

$ python ./input_data/get_EFICAz_WolfPSort_results.py \
-output_dir ./input_data/getpra_inputs/ \
-model ./input_data/getpra_inputs/Recon2M.1_Entrez_Gene.xml \
-ec ./input_data/getpra_inputs/20170110_EFICAz_result_using_all_human_genes.txt \
-sl ./input_data/getpra_inputs/20170110_WoLFPSort_result_using_all_human_genes.txt \
-ensembl ./input_data/getpra_inputs/Ensembl_GRCh38_EnsemblDB_v88.txt

Resulting output files in the source:
- EFICAz: getpra/input_data/Trimmed_EFICAz_result.txt
- Wolf PSort: getpra/input_data/Trimmed_WoLFPSort_result.txt

Publication Jae Yong Ryu 1, Hyun Uk Kim 1 & Sang Yup Lee. Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism., Proc. Natl. Acad. Sci. U.S.A., 2017, http://www.pnas.org/content/early/2017/10/23/1713050114

ReconManager¶

Recon-manager procedure

This project is to develop a framework that systematically predicts Gene-Transcript-Protein-Reaction Associations (GeTPRA) in human metabolims and updates a human genome-scale metabolic model (GEM) accordingly. Recon manager is a part of the project, which is a collection of scripts to generate Recon 2M.1 and simulate Recon models.

Features Recon manager contains scripts that implement following tasks independently:

Convert GPR to TPR associations
Update metabolite information
Calculate model statistics
Evaluate functionality of metabolic model
Reconstruct personal GEMs using tINIT

Installation Major dependencies - [gurobipy](http://www.gurobi.com/)

Procedure Note: This source code was developed in Linux, and has been tested in Ubuntu 14.04.5 LTS (i7-4770 CPU @ 3.40GHz)

Clone the repository
Create and activate virtual environment

$ virtualenv venv
$ source venv/bin/activate

Install packages at the root of the repository

$ pip install pip --upgrade
$ pip install -r requirements.txt

Install [gurobipy](http://www.gurobi.com/)
In our case, we installed gurobipy in the root of a server, and created its symbolic link in venv:
$ ln -s /usr/local/lib/python2.7/dist-packages/gurobipy/ $HOME/recon-manager/venv/lib/python2.7/site-packages/

Feature: Convert GPR to TPR associations Input arguments and corresponding files Following working input files can be found in: ./input_data/GPR_to_TPR_inputs. These files were used for the data presented in the manuscript.

-o : Output directory
-modelCOBRA-compliant SBML file (generic human GEM)
- File name in the source: Recon2M.1_Entrez_Gene.xml
-gene_transcript_informationA list of gene IDs and their matching transcript IDs
- File name in the source: Ensembl88_GRCh38_all_transcript_information.txt
File format

NCBI gene ID Gene stable ID Transcript stable ID RefSeq mRNA ID UCSC Stable ID 2733 ENSG00000119392 ENST00000309971 NM_001003722 uc004bvj.4 2733 ENSG00000119392 ENST00000372770 NM_001499 uc004bvi.4 5690 ENSG00000126067 ENST00000373237 NM_002794 uc001bzf.4 5690 ENSG00000126067 ENST00000373237 NM_001199779 uc001bzf.4 5690 ENSG00000126067 ENST00000621781 NM_001199780 uc021olh.3

Download procedure
1. Go to [Ensembl BioMarts](http://www.ensembl.org/biomart/martview)
2. Click Dataset on the left menu
  
  Select Ensembl Genes 89 in the drop-down menu CHOOSE DATABASE
  
  Select Human genes (GRCh38.p10) in the drop-down menu CHOOSE DATASET
3. Click Filters on the left menu
  
  Click GENE: in the main menu (center)
  
  Check Gene type and select protein_coding
4. Click Attributes on the left menu
  
  Check Features in the center
5. Click both GENE: and EXTERNAL: in the main menu (center)
  GENE: -> Ensembl -> Uncheck Gene stable ID and Transcript stable ID
  
  Check following items in order:
  
  EXTERNAL: -> External References (max 3) -> NCBI gene ID
  
  GENE: -> Ensembl -> Gene stable ID
  
  GENE: -> Ensembl -> Transcript stable ID
  
  EXTERNAL: -> External References (max 3) -> RefSeq mRNA ID
  
  EXTERNAL: -> External References (max 3) -> UCSC Stable ID
6. Click the button Results on the top left
7. Click the button Go in the top center

Implementation Note: Running this script takes ~ 4 m

$ python model_GPR_to_TPR_converter.py \
-o ./results/GPR_to_TPR_results/ \
-gene_transcript_information ./input_data/GPR_to_TPR_inputs/Ensembl88_GRCh38_all_transcript_information.txt \
-model ./input_data/GPR_to_TPR_inputs/Recon2M.1_Entrez_Gene.xml

Feature: Update metabolite information Input arguments and corresponding files Following working input files can be found in: ./input_data/metabolite_information_update_inputs. These files were used for the data presented in the manuscript.

-o : Output directory
-modelCOBRA-compliant SBML file (generic human GEM)
- File name in the source: Recon2M.1_Entrez_Gene.xml
-mnx_xrefInfo on chemical identifiers from [MetaNetX](http://www.metanetx.org/)
- File name in the source: chem_xref.tsv
- Click [chem_xref.tsv](http://www.metanetx.org/cgi-bin/mnxget/mnxref/chem_xref.tsv) for downloading
-mnx_propInfo on chemical structures from [MetaNetX](http://www.metanetx.org/)
- File name in the source: chem_prop.tsv
- Click [chem_prop.tsv](http://www.metanetx.org/cgi-bin/mnxget/mnxref/chem_prop.tsv) for downloading
-biggInfo on BiGG metabolites from [BiGG Models](http://bigg.ucsd.edu/)
- File name in the source: bigg_models_metabolites.txt
- Click [bigg_models_metabolites.txt](http://bigg.ucsd.edu/static/namespace/bigg_models_metabolites.txt) for downloading
-chebiInfo on ChEBI and InChI from [ChEBI](https://www.ebi.ac.uk/chebi/init.do)
- File name in the source: chebiId_inchi.tsv
- Click [chebiId_inchi.tsv](ftp://ftp.ebi.ac.uk/pub/databases/chebi/Flat_file_tab_delimited/chebiId_inchi.tsv) for downloading

Implementation Note: Running this script takes ~ 5 s

$ python model_update_metabolite_information.py \
-o ./results/metabolite_information_update_results/ \
-model ./input_data/metabolite_information_update_inputs/Recon2M.1_Entrez_Gene.xml \
-mnx_xref ./input_data/metabolite_information_update_inputs/chem_xref.tsv \
-mnx_prop ./input_data/metabolite_information_update_inputs/chem_prop.tsv \
-bigg ./input_data/metabolite_information_update_inputs/bigg_models_metabolites.txt \
-chebi ./input_data/metabolite_information_update_inputs/chebiId_inchi.tsv

Feature: Calculate model statistics Input arguments and corresponding files Following working input files can be found in: ./input_data/model_function_inputs. These files were used for the data presented in the manuscript.

-o : Output directory
-modelCOBRA-compliant SBML file (generic human GEM)
- File name in the source: Recon2M.1_Entrez_Gene.xml

-mediumA representative medium (RPMI-1640 medium)

File name in the source: RPMI1640_medium.txt

File format

EX_gly_LPAREN_e_RPAREN_     -0.05   1000
EX_arg_L_LPAREN_e_RPAREN_   -0.05   1000
EX_asn_L_LPAREN_e_RPAREN_   -0.05   1000
EX_asp_L_LPAREN_e_RPAREN_   -0.05   1000
EX_cys_L_LPAREN_e_RPAREN_   -0.05   1000
EX_glu_L_LPAREN_e_RPAREN_   -0.05   1000
EX_his_L_LPAREN_e_RPAREN_   -0.05   1000

Implementation Note: Running this script takes ~ 47 m

$ python model_metabolic_model_statistics.py \
-o ./results/model_statistics_results/ \
-medium ./input_data/model_function_inputs/RPMI1640_medium.txt \
-model ./input_data/model_function_inputs/Recon2M.1_Entrez_Gene.xml

Feature: Evaluate functionality of metabolic model Input arguments and corresponding files Following working input files can be found in: ./input_data/model_function_inputs. These files were used for the data presented in the manuscript.

-o : Output directory
-modelCOBRA-compliant SBML file (generic human GEM)
- File name in the source: Recon2M.1_Entrez_Gene.xml
-mediumA representative medium (RPMI-1640 medium)
- File name in the source: RPMI1640_medium.txt
-defined_mediumA defined minimal medium
- File name in the source: Defined_medium.txt
-es_genesA list of essential genes
- File name in the source: Essential_genes_from_wang_et_al.txt
-ne_genesA list of non-essential genes
- File name in the source: Non_essential_genes_from_wang_et_al.txt
-c_sourceA list of carbon sources
- File name in the source: atp_carbon_source.txt
-biomass : Reaction ID for biomass generation equation
-oxygen : Reaction ID for oxygen uptake
-atp : Reaction ID for ATP production

Implementation Note: Directly insert reaction IDs in terminal for -biomass, -oxygen and -atp

Note: Running this script takes ~ 7 m

$ python model_metabolic_function.py \
-o ./results/model_function_results/ \
-model ./input_data/model_function_inputs/Recon2M.1_Entrez_Gene.xml \
-medium ./input_data/model_function_inputs/RPMI1640_medium.txt \
-defined_medium ./input_data/model_function_inputs/Defined_medium.txt \
-es_genes ./input_data/model_function_inputs/Essential_genes_from_wang_et_al.txt \
-ne_genes ./input_data/model_function_inputs/Non_essential_genes_from_wang_et_al.txt \
-c_source ./input_data/model_function_inputs/atp_carbon_source.txt \
-biomass biomass_reaction \
-oxygen EX_o2_LPAREN_eRPAREN \
-atp DM_atp_c_

Feature: Reconstruct personal GEMs using [tINIT](http://msb.embopress.org/content/10/3/721.long) Input arguments and corresponding files Following working input files can be found in: ./input_data/tINIT_inputs. These files were used for the data presented in the manuscript.

-o : Output directory
-modelCOBRA-compliant SBML file (generic human GEM)
- File name in the source: Recon2M.1_Entrez_Gene.xml
-mediumA representative medium (RPMI-1640 medium)
- File name in the source: RPMI1640_medium.txt
-taskA list of metabolic tasks
- File name in the source: MetabolicTasks.csv
-present_reactionA list of reactions that should be present in model
- File name in the source: essential_reactions.txt
-present_metaboliteA list of metabolites that should be present in model
- File name in the source: essential_metabolites.txt
-iOmics data
- File name in the source: BLCA_T_TTL.csv
-biomass : Reaction ID for biomass generation equation

Implementation Note: Directly insert a reaction ID in terminal for -biomass

Note: Running this script for a Recon model takes ~ 8 m

$ python personal_GEM_tINIT.py \
-o ./results/tINIT_results/ \
-medium ./input_data/tINIT_inputs/RPMI1640_medium.txt \
-model ./input_data/tINIT_inputs/Recon2M.1_Entrez_Gene.xml \
-biomass biomass_reaction \
-task ./input_data/tINIT_inputs/MetabolicTasks.csv \
-present_reaction ./input_data/tINIT_inputs/essential_reactions.txt \
-present_metabolite ./input_data/tINIT_inputs/essential_metabolites.txt \
-i ./input_data/tINIT_inputs/BLCA_N_TTL.csv

Feature: Predict flux using transcript-level RNA-Seq data and GeTPRA Input arguments and corresponding files Following working input files can be found in: ./input_data/Flux_prediction. These files were used for the data presented in the manuscript.

-o : Output directory
-g_modelCOBRA-compliant SBML file (generic human GEM)
- File name in the source: Recon2M.2_BiGG_UCSC_Transcript.xml
-c_modelCOBRA-compliant SBML file (context-specific human GEM)
- File name in the source: LIHC_TCGA-BC-A10Q.xml
-getpraGeTPRA file
- File name in the source: GeTPRA.txt
-use_getpraOption for flux prediction using transcript-level data
- Insert`yes` for flux prediction using transcript-level data
- Insert`no` for flux prediction using gene-level data

Implementation Note: Running this script takes ~ 7 m

$ python flux_prediction.py \
-o ./results/Flux_prediction/ \
-i ./input_data/Flux_prediction/LIHC_TCGA-BC-A10Q.csv \
-getpra ./input_data/Flux_prediction/GeTPRA.txt \
-g_model ./input_data/Flux_prediction/Recon2M.2_BiGG_UCSC_Transcript.xml \
-c_model ./input_data/Flux_prediction/LIHC_TCGA-BC-A10Q.xml \
-use_getpra yes

Publication Jae Yong Ryu 1, Hyun Uk Kim 1 & Sang Yup Lee. Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism., Proc. Natl. Acad. Sci. U.S.A., 2017, http://www.pnas.org/content/early/2017/10/23/1713050114

DeepEC¶

DeepEC running procedure

Note: Size of the protein sequence input file should be adjusted according to the memory size of your computer. This source code was developed in Linux, and has been tested in Ubuntu 14.04 with Python 2.7, Python 3.4, Python 3.5 or Python 3.6. It should be noted that Python 3.7 is currently not supported.

Clone the repository

$ git clone https://bitbucket.org/kaistsystemsbiology/deepec.git

Create and activate a conda environment

$ conda env create -f environment.yml
$ conda activate deepec

Example

Run DeepEC

$ python deepec.py -i ./example/test.fa -o ./output

DeepDDI¶

DeepDDI running procedure This project is to develop a framework that systematically predicts drug-drug interactions (DDIs) and drug-food interactions (DFIs).

Features - DeepDDI predicts DDIs and DFIs using names of drug-drug or drug-food constituent pairs and their structural information as inputs - DeepDDI predicts 86 DDI types as outputs of human-readable sentences

Note: This source code was developed in Linux, and has been tested in Ubuntu 14.04

Create and activate virtual environment

$ virtualenv venv
$ source venv/bin/activate

Install packages at the root of the repository

$ pip install pip --upgrade
$ pip install -r requirements.txt

Install [rdkit](http://www.rdkit.org/)

In our case, we installed rdkit in the root of a server using apt-get (i.e., apt-get install rdkit), and created its symbolic link in venv:

$ ln -s /usr/lib/python2.7/dist-packages/rdkit/ ./venv/lib/python2.7/site-packages/

Input files for the DeepDDI

Following working input files can be found in: ./data/. These files were used for the data presented in the manuscript.

Example

$ python run_DeepDDI.py -i ./examples/input_structures.txt -o ./output_dir/

iBridge¶

iBrdige running procedure

MELI-3D¶

MELI-3D running procedure

13C-MFA¶

13C-MFA running procedure

Note:

This is a Matlab based simulator that helps to analyze fluxes in isotopically steady state using a 13C-labeled carbon source*

It was developed and tested in Windows 10 (64 bit), Matlab R2019b*
By running the provided p-files using Matlab, flux analysis using 13C can be performed*
For more detailed instructions, please refer to the provided ppt-file*

DeepTFactor¶

DeepTFactor running procedure

Note: This source code was developed in Linux, and has been tested in Ubuntu 16.04 with Python 3.6.

Clone the repository

$ git clone https://bitbucket.org/kaistsystemsbiology/deeptfactor.git

Create and activate virtual environment

$ conda env create -f environment.yml
$ conda activate deeptfactor

To use GPU for the computation, install an appropriate version of pytorch (and cuda).
- Please refer to <https://pytorch.org>

Example

Run DeepTFactor

$ python tf_running.py -i ./Dataset/example_tf.fasta -o ./result -g cpu
$ python tf_running.py -i ./Dataset/example_tf.fasta -o ./result -g cuda:1

RetroPrecursorSelection¶

Retro-precursor-selection running procedure

Note This source code works on Linux and has been tested on Ubuntu 16.04 with Python 3.6.

Clone the repository:

$ git clone https://wdjang@bitbucket.org/kaistsystemsbiology/retro-precursor-selection.git

Create and activate a conda environment (It takes less than 30 seconds):

$ conda env create -f environment.yml
$ conda activate SSA

Example

$ python run_ssa.py -i 'CCCCN'[SMILES of target product] -o output[Name of output directory]

Three output files are generated in a folder that is newly created after computation: molecule_type.txt, predicted_precursors.txt, reaction_center.txt

Run time of this source code is usually ~60 seconds.

DeepRFC¶

DeepRFC running procedure

Note: This source code was developed in Linux, and has been tested in Ubuntu 16.04 with Python 3.6. Anaconda was used as a package manager

Clone the repository

$ git clone https://bitbucket.org/kaistsystemsbiology/deeprfc.git

Create and activate a conda environment

$ conda env create -f environment.yml
$ conda activate deeprfc

If pip error occurs due to Pytorch version,

Remove followings from environment.yml
Torch==1.2.0+cu92
Torchvision==0.4.0+cu02
-Install an appropriate version of Pytorch, according to your computing envrionment.
Please refer to https://pytorch.org/get-started/locally/

Example

Run DeepRFC

$ python deeprfc.py -i ./example/test.txt -o ./output

Welcome to sysbiotech’s documentation!¶

GeTPRA¶

ReconManager¶

DeepEC¶

DeepDDI¶

iBridge¶

MELI-3D¶

13C-MFA¶

DeepTFactor¶

RetroPrecursorSelection¶

DeepRFC¶

Indices and tables¶