drugstone 1.0.0

The python package for the https://drugst.one/ platform.

This is the python package for the drugst.one platform. It offers tools for network-based drug repurposing and is a programmatic interface to Drugst.one.

• Website: https://drugst.one/
• Source code: https://github.com/drugst-one/python-package
• Bug reports: https://github.com/drugst-one/python-package/issues

Installation

Install drugstone.

pip install drugstone

Import drugstone in python

import drugstone

Drugstone officially supports Python 3.6+.

Supported features

Drugstone offers a toolbox for drug repurposing applications.

• Search for drugs, interacting with a list of genes
• Search for drug targets, for a list of genes
• Visualize data in common formats like JSON or CSV
• create interaction graphs for drug and gene interactions

Available Datasets

Protein-protein interactions (ppi_dataset):

NeDRex, BioGRID, IID, IntAct, STRING, APID

Protein-drug interactions (pdi_dataset):

NeDRex, DrugBank, Drug Central, ChEMBL, DGIdb

Please note that some of the datasets require you to accept their terms and conditions before usage. DrugBank can only be used if the license has been agreed to and since NeDRex includes DrugBank data, only a part of NeDRex is available without agreeing to our license.

The terms and conditions can be read by calling

drugstone.print_license()

and can be accepted after reading with

drugstone.accept_license().

Available backends

In the the main Drugst.One database is updated automatically weekly. This is the database used by default (https://api.drugst.one/). For more stable results, change the api endpoint to our stable API (https://api.stable.drugst.one/) using the function below. The Drugst.One stable version is updated yearly (on the first of January).

drugstone.set_api('https://api.stable.drugst.one/')

Start a new task

With Drugstone it is easy and convenient to search for drugs or drug-targets, starting with a list of genes.

genes = [
    "CFTR", "TGFB1", "SCNN1B", 
    "DCTN4", "SCNN1A", "SCNN1G",
    "CLCA4", "TNFRSF1A", "FCGR2A"
]

parameters = {
    "target": "drug",
    "algorithm": "trustrank"
}

task = drugstone.new_task(genes, parameters)

r = task.get_result()

genes = r.get_genes()
drugs = r.get_drugs()

# save directly to files
r.download_json()
r.download_graph()

Start multiple tasks

You can start multiple tasks at once, either with completely independent parameters or with same parameters and different algorithms.

Multiple algorithms

By defining an algorithms value in the parameters dictionary, you can pass a list of algorithm values. For every algorithm, a task will be started, with otherwise same parameter values.

genes = [
    "CFTR", "TGFB1", "SCNN1B", 
    "DCTN4", "SCNN1A", "SCNN1G",
    "CLCA4", "TNFRSF1A", "FCGR2A"
]

parameters = {
    "target": "drug",
    "algorithms": ["trustrank", "closeness", "degree"]
}

tasks = drugstone.new_tasks(genes, parameters)   
r = task.to_dict()                
r.download_json()

Independent parameters

new_tasks() accepts a list of parameter dictionaries. For every dictionary a task will be started.

genes = [
    "CFTR", "TGFB1", "SCNN1B", 
    "DCTN4", "SCNN1A", "SCNN1G",
    "CLCA4", "TNFRSF1A", "FCGR2A"
]

p1 = {
    "target": "drug",
    "ppiDataset": 'nedrex',
    "pdiDataset": "drugcentral"
}

p2 = {
    "target": "drug",
    "ppiDataset": 'IID',
    "pdiDataset": "chembl"
}

p3 = {
    "target": "drug",
    "ppiDataset": 'apid',
    "pdiDataset": "dgidb"
}

tasks = drugstone.new_tasks(genes, [p1, p2, p3]) 
r = tasks.get_result() 
r.to_dict()                 
r.download_json()  
# only with Python 3.6                 
r.create_upset_plot()                 

Union and intersection of tasks

You can get the union or intersection of tasks. That returns a TaskResult with the according result.

genes = [
    "CFTR", "TGFB1", "SCNN1B", 
    "DCTN4", "SCNN1A", "SCNN1G",
    "CLCA4", "TNFRSF1A", "FCGR2A"
]

parameters = {
    "target": "drug",
    "algorithms": ["trustrank", "closeness", "degree"]
}

tasks = drugstone.new_tasks(genes, parameters)    

u = tasks.get_union()                  
u.download_json()

i = tasks.get_intersection()
i.download_json()

Use your own network

You can add custom nodes and edges to the network, which may be considered for the drug-target or the drug search. Also, it is possible to ONLY consider your provided PPI-network and exclude PPI-edges provdided by Drugst.One. Please note that this is an experimental feature and is still being tested.

import drugstone

seeds = [
    "CFTR", "TGFB1"
]

parameters = {
    "target": "drug",
    "algorithm": "trustrank",
    "pdiDataset": 'nedrex',     # drug nodes and edges will still be taken from Drugst.One
    "custom_edges": [   # add here your edges for you custom PPI
        {"from": "CFTR", "to": "SCNN1G"},
        {"from": "CFTR", "to": "SCNN1B"},
        {"from": "TGFB1", "to": "DCTN4"},
        {"from": "SCNN1B", "to": "SCNN1A"},
        {"from": "SCNN1B", "to": "CLCA4"},
        {"from": "TNFRSF1A", "to": "FCGR2A"}
    ],
    "exclude_drugstone_ppi_edges": True, # this parameter removes all Drugst.One PPI edges (only if edges in 'custom_edges' are provided)
    "identifier": "symbol",
    "include_indirect_drugs": True,
    "network_nodes": ["CFTR", "TGFB1", "SCNN1B",    # add here your nodes for you custom PPI
                      "DCTN4", "SCNN1A", "SCNN1G",
                      "CLCA4", "TNFRSF1A", "FCGR2A"]
}
task = drugstone.new_task(seeds, parameters)
r = task.get_result()
genes = r.get_genes()
drugs = r.get_drugs()

Combine a drug-target search with a drug search

This will perform a drug-target search for the seed genes and then use the drug-target search results and the seed genes to perform a drug-search. Finally, a Task with the drug-search results will be returned.

genes = [
    "CFTR", "TGFB1", "SCNN1B", 
    "DCTN4", "SCNN1A", "SCNN1G",
    "CLCA4", "TNFRSF1A", "FCGR2A"
]

parameters = {
    "algorithm": "trustrank"
}

task = drugstone.deep_search(genes, parameters)  
r = tasks.get_result() 
r.to_dict()                 
r.download_json()
# only with Python 3.6                 
r.create_upset_plot()  

Available Parameters

parameters = {
    "identifier": "symbol", #("entrez" | "uniprot" | "ensg" will be supported in future versions)
    "algorithm": "adjacentDrugs", "trustrank" | "multisteiner" | "keypathwayminer" | "closeness" | "degree" | "proximity" | "betweenness",
    "ppiDataset": "NeDRex",
    "pdiDataset": "NeDRex",
    "resultSize": 20,
    "target": "drug" | "drug-target",
    "includeIndirectDrugs": True | False,
    "includeNonApprovedDrugs": True | False,
    "maxDeg": sys.maxsize, # filter out nodes with high degrees
    "hubPenalty": 0.0, # penalize hub nodes
    "filterPaths": True | False, # include only shortest connections in the result

    "damping_factor": 0.85, # only in trustrank

    "num_trees": 5, # only in multisteiner
    "tolerance": 10, # only in multisteiner

    "k": 5, # only in keypathwayminer
} 

For more information about the algorithms, please refer to https://drugst.one/doc#implementation_algorithms.

For more information abouyt the available dataset types, please refer to https://drugst.one/doc#implementation_datasources.

class Task

Represents a task.

get_result() -> TaskResult
Returns a TaskResult for the result of the task.

get_info() -> dict
Returns a dict with information about the task.

get_parameters() -> dict
Returns a dict with the parameters of the task.

class TaskResult

Represents the results of a task.

get_genes() -> dict
Returns a dict with the genes.

get_drugs() -> dict
Returns a dict with the drugs.

to_dict() -> dict
Returns a dict with the result.

to_pandas_dataframe() -> DataFrame
Returns a pandas DataFrame of the result.

download_json(path: str, name: str) -> None
Downloads a json file with the result.

download_genes_csv(path: str, name: str) -> None
Downloads a csv file with the genes of the result.

download_drugs_csv(path: str, name: str) -> None
Downloads a csv file with the drugs of the result.

download_edges_csv(path: str, name: str) -> None
Downloads a csv file with the edges of the result.

download_graph(path: str, name: str) -> None
Downloads a html file with a graph of the nodes.

class Tasks

Wraps a list of Task objects.

get_result() -> TasksResult
Returns a TasksResult for the list of tasks.

get_union() -> TaskResult
Returns a TaskResult with the union of the tasks.

get_intersection() -> TaskResult
Returns a TaskResult with the intersection of the tasks.

class TasksResult

Represents the results of a list of Task objects.

get_tasks_list() -> List[Task]
Returns the list of tasks.

to_dict() -> dict
Returns a dict with the results of the tasks.

download_json(path: str, name: str) -> None
Downloads a json file with the results.

create_upset_plot() -> None
Opens a new window with an upset plot of the results.

Miscellaneous

map nodes to Drugst.One proteins

This will fetch all available information for the given nodes from the Drugst.One database as a list of dictionaries. Each node contains a key 'drugstoneType', where the value is 'protein' if the given node can be mapped to a protein. If a node can not be mapped, the 'drugstoneType' will be 'other'.
Be aware of the parameter dictionary with the key 'identifier', available options are one of 'symbol' (HUGO symbol), 'uniprot' (Uniprot AC), 'ensg' or 'ensembl' (Ensembl Gene ID), 'entrez' or 'ncbigene' (Entrez ID), depending on your input.

import drugstone

nodes = [
    "CFTR", "TGFB1", "SCNN1B", "justatest"
]

parameters = {'identifier': 'symbol'}

drugstone_nodes = drugstone.map_nodes(nodes, parameters)

build network

This will fetch all available edges for a given list of proteins. Returned will be a list of node entities. Each of these nodes contains the key 'hasEdgesTo' with a list of all node ids this node has an edge to.
Be aware of the parameter dictionary with the key 'identifier', available options are one of 'symbol' (HUGO symbol), 'uniprot' (Uniprot AC), 'ensg' or 'ensembl' (Ensembl Gene ID), 'entrez' or 'ncbigene' (Entrez ID), depending on your input.

import drugstone

# optional
drugstone.print_license()
drugstone.accept_license()

genes = [
    "CFTR","TGFB1","TNFRSF1A","FCGR2A","ENG","DCTN4","CLCA4","STX1A","SCNN1G","SCNN1A","SCNN1B", "justatest"
]

parameters = {'identifier': 'symbol', 'ppiDataset': 'IID'}

network = drugstone.build_network(genes, parameters)

Copyright: 2024 - Institute for Computational Systems Biology by Prof. Dr. Jan Baumbach.