Introduction

The chp_client is a lightweight Python client for the NCATS Connections Hypothesis Provider (CHP). It is meant to be an easy-to-use wrapper utility to both run and build TRAPI queries the CHP web service will understand. Many of the CHP queries have been inspired by direct input from Translator ARAs and such ARAs may have their own dedicated CHP API client that returns results that they expect. However, there is also a default client that can handle generic CHP requests.

Requirements

Python >= 3.6
bmt
requests
submodules:
1. trapi_model
2. reasoner-validator

Installation

pip3 install git+https://github.com/di2ag/chp_client#egg=chp_client

or,

pip3 install -r requirements.txt
python3 setup.py install

Active Instances

Our API Domain is:

chp.thayer.dartmouth.edu

We also have subdomains accounted for particular disease types to narrow the scope for internal reasoning:

breast.chp.thayer.dartmouth.edu
brain.chp.thayer.dartmouth.edu
lung.chp.thayer.dartmouth.edu

All endpoints have /query, /predicates, /curies and /versions functionality.

To access Trapi 1.0 please prepend your /query with /v1.0.

Example of main endpoint trapi 1.1 query

chp.thayer.dartmouth.edu/query

Example of main endpoint trapi 1.0 query

chp.thayer.dartmouth.edu/v1.0/query

Quick Start

Once you have installed the CHP client, useage is as simple as:

In [1]: from chp_client import get_client
In [2]: from chp_client.query import build_standard_query, build_wildcard_query, build_onehop_query
In [3]: default_client = get_client()

Now that you have an instance of the client, you can determine which query graph edge predicates are currently supported by CHP with:

In[4]: default_client.predicates()
{
  "biolink:Gene": {
    "biolink:Disease": [
      "biolink:gene_associated_with_condition"
    ],
    "biolink:Drug": [
      "biolink:interacts_with"
    ]
  },
  "biolink:Drug": {
    "biolink:Disease": [
      "biolink:treats"
    ],
    "biolink:Gene": [
      "biolink:interacts_with"
    ]
  },
  "biolink:Disease": {
    "biolink:PhenotypicFeature": [
      "biolink:has_phenotype"
    ]
  }
}

And you can check the current versions of our repos:

In [5]: versions = default_client.versions()
{
  "chp": "2.3.1",
  "chp_client": "1.1.3",
  "chp_data": "1.0.2",
  "pybkb": "2.0.0"
}

In the next section we will look at how to build CHP queries.

Building Supported CHP Queries

As CHP is TRAPI compliant a large subset of queries can be built with a wide variety of structures. In order to scope the query building problem, we have currently limited the structures of queries that can be asked and have detailed their respective semantics. Note: As the Translator and Biolink models develop we intend to ease these restrictions.

One Hop Queries

Our one hop structure allows associations to be built between genes and drugs, genes and diseases and drugs and disease where one node is a wildcard. Returned are nodes that contributed to our underlying probablistic semantics of P(survival_time > x | evidence).

One Hop Drug to Gene Wildcard

To build and query CHP for the above query:

   In [6]: q = build_onehop_query(
       ..: q_object_category = 'gene',
       ..: q_subject='CHEMBL:CHEMBL88',
       ..: q_subject_category='drug',
       ..: trapi_version='1.1',
       ..: )
   In [7]: response = client.query(q.to_dict())

An example response from this type of query graph is below:

"knowledge_graph": {
  "nodes": {
    "CHEMBL:CHEMBL88": {
      "name": "CYCLOPHOSPHAMIDE",
      "categories": [
        "biolink:Drug"
      ],
      "attributes": []
    },
    "ENSEMBL:ENSG00000141510": {
      "name": "TP53",
      "categories": [
        "biolink:Gene"
      ],
      "attributes": []
    },
    "ENSEMBL:ENSG00000129003": {
      "name": "VPS13C",
      "categories": [
        "biolink:Gene"
      ],
      "attributes": []
    },
    "ENSEMBL:ENSG00000155657": {
      "name": "TTN",
      "categories": [
        "biolink:Gene"
      ],
      "attributes": []
    },
    "ENSEMBL:ENSG00000181143": {
      "name": "MUC16",
      "categories": [
        "biolink:Gene"
      ],
      "attributes": []
    },
    "ENSEMBL:ENSG00000196632": {
      "name": "WNK3",
      "categories": [
        "biolink:Gene"
      ],
      "attributes": []
    },
    "ENSEMBL:ENSG00000127481": {
      "name": "UBR4",
      "categories": [
        "biolink:Gene"
      ],
      "attributes": []
    },
    "ENSEMBL:ENSG00000169855": {
      "name": "ROBO1",
      "categories": [
        "biolink:Gene"
      ],
      "attributes": []
    },
    "ENSEMBL:ENSG00000005810": {
      "name": "MYCBP2",
      "categories": [
        "biolink:Gene"
      ],
      "attributes": []
    },
    "ENSEMBL:ENSG00000118473": {
      "name": "SGIP1",
      "categories": [
        "biolink:Gene"
      ],
      "attributes": []
    },
    "ENSEMBL:ENSG00000198626": {
      "name": "RYR2",
      "categories": [
        "biolink:Gene"
      ],
      "attributes": []
    }
  },
  "edges": {
    "e0": {
      "predicate": "biolink:interacts_with",
      "relation": null,
      "subject": "CHEMBL:CHEMBL88",
      "object": "ENSEMBL:ENSG00000141510",
      "attributes": [
        {
          "attribute_type_id": "Contribution",
          "original_attribute_name": null,
          "value": -0.015305072994577756,
          "value_type_id": "biolink:has_evidence",
          "attribute_source": null,
          "value_url": null,
          "description": null
        }
      ]
    },
    "e1": {
      "predicate": "biolink:interacts_with",
      "relation": null,
      "subject": "CHEMBL:CHEMBL88",
      "object": "ENSEMBL:ENSG00000129003",
      "attributes": [
        {
          "attribute_type_id": "Contribution",
          "original_attribute_name": null,
          "value": -0.011937490158826738,
          "value_type_id": "biolink:has_evidence",
          "attribute_source": null,
          "value_url": null,
          "description": null
        }
      ]
    },
    "e2": {
      "predicate": "biolink:interacts_with",
      "relation": null,
      "subject": "CHEMBL:CHEMBL88",
      "object": "ENSEMBL:ENSG00000155657",
      "attributes": [
        {
          "attribute_type_id": "Contribution",
          "original_attribute_name": null,
          "value": -0.011265657498001666,
          "value_type_id": "biolink:has_evidence",
          "attribute_source": null,
          "value_url": null,
          "description": null
        }
      ]
    },
    "e3": {
      "predicate": "biolink:interacts_with",
      "relation": null,
      "subject": "CHEMBL:CHEMBL88",
      "object": "ENSEMBL:ENSG00000181143",
      "attributes": [
        {
          "attribute_type_id": "Contribution",
          "original_attribute_name": null,
          "value": -0.0107876150199956,
          "value_type_id": "biolink:has_evidence",
          "attribute_source": null,
          "value_url": null,
          "description": null
        }
      ]
    },
    "e4": {
      "predicate": "biolink:interacts_with",
      "relation": null,
      "subject": "CHEMBL:CHEMBL88",
      "object": "ENSEMBL:ENSG00000196632",
      "attributes": [
        {
          "attribute_type_id": "Contribution",
          "original_attribute_name": null,
          "value": -0.010529562080332809,
          "value_type_id": "biolink:has_evidence",
          "attribute_source": null,
          "value_url": null,
          "description": null
        }
      ]
    },
    "e5": {
      "predicate": "biolink:interacts_with",
      "relation": null,
      "subject": "CHEMBL:CHEMBL88",
      "object": "ENSEMBL:ENSG00000127481",
      "attributes": [
        {
          "attribute_type_id": "Contribution",
          "original_attribute_name": null,
          "value": -0.010210255955874721,
          "value_type_id": "biolink:has_evidence",
          "attribute_source": null,
          "value_url": null,
          "description": null
        }
      ]
    },
    "e6": {
      "predicate": "biolink:interacts_with",
      "relation": null,
      "subject": "CHEMBL:CHEMBL88",
      "object": "ENSEMBL:ENSG00000169855",
      "attributes": [
        {
          "attribute_type_id": "Contribution",
          "original_attribute_name": null,
          "value": -0.01018732959144447,
          "value_type_id": "biolink:has_evidence",
          "attribute_source": null,
          "value_url": null,
          "description": null
        }
      ]
    },
    "e7": {
      "predicate": "biolink:interacts_with",
      "relation": null,
      "subject": "CHEMBL:CHEMBL88",
      "object": "ENSEMBL:ENSG00000005810",
      "attributes": [
        {
          "attribute_type_id": "Contribution",
          "original_attribute_name": null,
          "value": -0.009817363716694328,
          "value_type_id": "biolink:has_evidence",
          "attribute_source": null,
          "value_url": null,
          "description": null
        }
      ]
    },
    "e8": {
      "predicate": "biolink:interacts_with",
      "relation": null,
      "subject": "CHEMBL:CHEMBL88",
      "object": "ENSEMBL:ENSG00000118473",
      "attributes": [
        {
          "attribute_type_id": "Contribution",
          "original_attribute_name": null,
          "value": -0.009675879792795597,
          "value_type_id": "biolink:has_evidence",
          "attribute_source": null,
          "value_url": null,
          "description": null
        }
      ]
    },
    "e9": {
      "predicate": "biolink:interacts_with",
      "relation": null,
      "subject": "CHEMBL:CHEMBL88",
      "object": "ENSEMBL:ENSG00000198626",
      "attributes": [
        {
          "attribute_type_id": "Contribution",
          "original_attribute_name": null,
          "value": 0.008687240281302784,
          "value_type_id": "biolink:has_evidence",
          "attribute_source": null,
          "value_url": null,
          "description": null
        }
      ]
    }
  }
}

Extracting the Statiscally Important Genes

CHP uses a method called Contributions Analysis to analyze gene importance and thereby rank genes. When the probability of the subgraph query (i.e. everything except the gene wildcards) is calculated, it carries with it a probabilistic mass. Contribution analysis tracks the amount of mass each component in our model contributed to the total mass (i.e. that final probability value of the query subgraph). As gene mutations play a significant role in our model, we are able to track each gene mutation's contribution to the total probabillistic mass, allowing us to yield our ranked gene results. The interpretation of these ranked genes should be that they are the genes that contributed most to supporting the given query.

The edge attribute displays the contribution for the wildcard gene nodes.

One Hop Gene to Drug Wildcard

Similar to the previous query, we can find associations between drugs and genes where drugs is the wildcard.

   In [8]: q = build_onehop_query(
       ..: q_object_category = 'gene',
       ..: q_subject='CHEMBL:CHEMBL88',
       ..: q_subject_category='drug',
       ..: trapi_version='1.1',
       ..: )
   In [9]: response = client.query(q.to_dict())

One Hop Drug Wildcard to Disease

   In [10]: q = build_onehop_query(
        ..: q_subject_category = 'disease',
        ..: q_subject='MONDO:0007254',
        ..: q_object_category='drug',
        ..: trapi_version='1.1',
        ..: )
   In [11]: response = client.query(q.to_dict())

Standard Probabilistic Queries

Query format

Our standard query is a straight probabilistic query of the form P(Outcome | Gene Mutations, Disease, Therapeutics, ...). We can see a graphical representation of a query below:

Notice, that the CHP can handle multi-hop queries. We reason in the full contents of the query graph and return the appropriate response. In this case the respose will be a edge binding to the the disease to the phenotype in this query graph. Where the resultant calculated knowledge graph of this query graph will have a biolink:has_confidence_level attribute denoting the calculated probability for this query. We can build this query with the provided query module of the client with the following code:

In [12]: from chp_client.query import build_query

In [13]: q = build_standard_query(
    ...: genes=['ENSEMBL:ENSG00000121879'],
    ...: drugs=['CHEMBL:CHEMBL88'],
    ...: disease='MONDO:0007254',
    ...: outcome='EFO:0000714',
    ...: outcome_name='survival_time',
    ...: outcome_op='>',
    ...: outcome_value=700,
    ...: trapi_version='1.1',
   )
   
In [14]: response = client.query(q.to_dict())

An example response from this type of query graph is below:

{
  "message": {
    "query_graph": {
      "nodes": {
        "n0": {
          "ids": [
            "MONDO:0007254"
          ],
          "categories": [
            "biolink:Disease"
          ],
          "constraints": []
        },
        "n1": {
          "ids": [
            "ENSEMBL:ENSG00000121879"
          ],
          "categories": [
            "biolink:Gene"
          ],
          "constraints": []
        },
        "n2": {
          "ids": [
            "CHEMBL:CHEMBL88"
          ],
          "categories": [
            "biolink:Drug"
          ],
          "constraints": []
        },
        "n3": {
          "ids": [
            "EFO:0000714"
          ],
          "categories": [
            "biolink:PhenotypicFeature"
          ],
          "constraints": []
        }
      },
      "edges": {
        "e0": {
          "predicates": [
            "biolink:gene_associated_with_condition"
          ],
          "relation": null,
          "subject": "n1",
          "object": "n0",
          "constraints": []
        },
        "e1": {
          "predicates": [
            "biolink:treats"
          ],
          "relation": null,
          "subject": "n2",
          "object": "n0",
          "constraints": []
        },
        "e2": {
          "predicates": [
            "biolink:has_phenotype"
          ],
          "relation": null,
          "subject": "n0",
          "object": "n3",
          "constraints": [
            {
              "name": "survival_time",
              "id": "EFO:0000714",
              "operator": ">",
              "value": 700,
              "unit_id": null,
              "unit_name": null,
              "not": false
            }
          ]
        }
      }
    },
    "knowledge_graph": {
      "nodes": {
        "MONDO:0007254": {
          "name": "MONDO:0007254",
          "categories": [
            "biolink:Disease"
          ],
          "attributes": []
        },
        "ENSEMBL:ENSG00000121879": {
          "name": "PIK3CA",
          "categories": [
            "biolink:Gene"
          ],
          "attributes": []
        },
        "CHEMBL:CHEMBL88": {
          "name": "CYCLOPHOSPHAMIDE",
          "categories": [
            "biolink:Drug"
          ],
          "attributes": []
        },
        "EFO:0000714": {
          "name": "EFO:0000714",
          "categories": [
            "biolink:PhenotypicFeature"
          ],
          "attributes": []
        }
      },
      "edges": {
        "e0": {
          "predicate": "biolink:gene_associated_with_condition",
          "relation": null,
          "subject": "ENSEMBL:ENSG00000121879",
          "object": "MONDO:0007254",
          "attributes": []
        },
        "e1": {
          "predicate": "biolink:treats",
          "relation": null,
          "subject": "CHEMBL:CHEMBL88",
          "object": "MONDO:0007254",
          "attributes": []
        },
        "e2": {
          "predicate": "biolink:has_phenotype",
          "relation": null,
          "subject": "MONDO:0007254",
          "object": "EFO:0000714",
          "attributes": [
            {
              "attribute_type_id": "Probability of Survival",
              "original_attribute_name": null,
              "value": 0.6562500000000003,
              "value_type_id": "biolink:has_confidence_level",
              "attribute_source": null,
              "value_url": null,
              "description": null
            }
          ]
        }
      }
    },
    "results": [
      {
        "edge_bindings": {
          "e0": [
            {
              "id": "e0"
            }
          ],
          "e1": [
            {
              "id": "e1"
            }
          ],
          "e2": [
            {
              "id": "e2"
            }
          ]
        },
        "node_bindings": {
          "n0": [
            {
              "id": "MONDO:0007254"
            }
          ],
          "n1": [
            {
              "id": "ENSEMBL:ENSG00000121879"
            }
          ],
          "n2": [
            {
              "id": "CHEMBL:CHEMBL88"
            }
          ],
          "n3": [
            {
              "id": "EFO:0000714"
            }
          ]
        }
      }
    ]
  },
  "max_results": 10,
  "trapi_version": "1.1",
  "biolink_version": null,
  "pk": "e7634da0-e977-482a-b8ba-fc65044a1f39"

Extracting Query Probability

You can extract the probability of the query manually from the TRAPI response data from CHP, but we have also provided a helper method inside the client to assist in extracting this probability.

In [15]: client.get_outcome_prob(response)
Out[16]: 0.6562500000000003

The interpretation of this probability is that ~ 65% of all patients in our dataset with a somatic mutation in RAF1 gene that took Cyclophosphamide survived for more than 700 days after initial diagnosis.

Other Examples

No Evidence

In [17]: q = build_standard_query(
    ...: disease='MONDO:0007254',
    ...: outcome='EFO:0000714',
    ...: outcome_name='survival_time',
    ...: outcome_op='>',
    ...: outcome_value=700,
    ...: trapi_version='1.1',
   )

All the evidence

In [18]: q = build_standard_query(
    ...: genes=['ENSEMBL:ENSG00000121879', 'ENSEMBL:ENSG00000141510', 'ENSEMBL:ENSG00000196557', 'ENSEMBL:ENSG00000132155'],
    ...: drugs=['CHEMBL:CHEMBL88'],
    ...: disease='MONDO:0007254',
    ...: outcome='EFO:0000714',
    ...: outcome_name='survival_time',
    ...: outcome_op='>',
    ...: outcome_value=1000,
    ...: trapi_version='1.1',
   )

Build your own Instance for NCATS Deployment Pipeline

In [19]: # First git clone chp_api somewhere that you want
In [20]: git clone https:/github.com/NCATSTranslator/chp_api.git
In [21]: # Go into the chp_api directory and run docker-compose
In [22]: cd chp_api
In [23]: docker-compose -f docker-compose.prod.yml build 
In [24]: docker-compose -f docker-compose-prod.yml up -d
In [25]: docker-compose -f docker-compose.prod.yml exec web python3 manage.py makemigrations
In [26]: docker-compose -f docker-compose.prod.yml exec web python3 manage.py migrate --noinput
In [27]: docker-compose -f docker-compose.prod.yml exec web python3 manage.py collectstatic --no-input --clear

Contacting API Directly

In [28]: import requests
In [29]: import json
In [30]: # Display predicates
In [31]: r = requests.get('http://chp.thayer.dartmouth.edu/predicates/')
In [32]: json_formatted_str = json.dumps(json.loads(r.content), indent=2)
In [33]: # Display all available curies
In [34]: r = requests.get('http://chp.thayer.dartmouth.edu/curies/')
In [35]: json_formatted_str = json.dumps(json.loads(r.content), indent=2)
In [36]: # Query CHP
In [37]: r = requests.post('http://chp.thayer.dartmouth.edu/query/', json=payload)
In [38]: chp_res = json.dumps(json.loads(r.content), indent=2)

Name		Name	Last commit message	Last commit date
Latest commit History 112 Commits
.github/ISSUE_TEMPLATE		.github/ISSUE_TEMPLATE
chp_client		chp_client
docs		docs
media		media
notebooks		notebooks
samples		samples
submodules		submodules
tests		tests
utils		utils
.gitignore		.gitignore
.gitmodules		.gitmodules
LICENSE		LICENSE
README.md		README.md
other_requirements.txt		other_requirements.txt
requirements.txt		requirements.txt
setup.py		setup.py
terms of service		terms of service

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Table of Contents

Introduction

Requirements

Installation

Active Instances

Quick Start

Building Supported CHP Queries

One Hop Queries

One Hop Drug to Gene Wildcard

Extracting the Statiscally Important Genes

One Hop Gene to Drug Wildcard

One Hop Drug Wildcard to Disease

Standard Probabilistic Queries

Query format

Extracting Query Probability

Other Examples

No Evidence

All the evidence

Build your own Instance for NCATS Deployment Pipeline

Contacting API Directly

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Table of Contents

Introduction

Requirements

Installation

Active Instances

Quick Start

Building Supported CHP Queries

One Hop Queries

One Hop Drug to Gene Wildcard

Extracting the Statiscally Important Genes

One Hop Gene to Drug Wildcard

One Hop Drug Wildcard to Disease

Standard Probabilistic Queries

Query format

Extracting Query Probability

Other Examples

No Evidence

All the evidence

Build your own Instance for NCATS Deployment Pipeline

Contacting API Directly

Other Notable Links

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