BioCypher adapter for the CollecTRI dataset

This repository contains the code for the BioCypher adapter for the CollecTRI dataset. The adapter is a Python module that converts the CollecTRI dataset into the BioCypher format. It also serves as a tutorial for end-to-end knowledge graph construction using BioCypher.

Process

1. Download and cache the resource

2. Run BioCypher to create knowledge graph

3. Deploy knowledge graph and web frontend

Tutorial

1. Create repository: using the template repository is the easiest way to get started. The template repository contains the basic structure of a BioCypher adapter. Clone the template repository and rename it and the adapter to your project's name. Also adjust the pyproject.toml file to reflect your project's name and version.

2. Find the data: the CollecTRI dataset is available as a flat file at https://rescued.omnipathdb.org/CollecTRI.csv. With this link, we can set up a BioCypher Resource object to download and cache the data. We implement this and all other steps of the build pipeline in the create_knowledge_graph.py script. Check there for the full code.

bc = BioCypher()
collectri = Resource(
    name="collectri",
    url_s="https://rescued.omnipathdb.org/CollecTRI.csv",
    lifetime=0,  # CollecTRI is a static resource
)
paths = bc.download(collectri)

3. Adjust the adapter based on the contents of the dataset. This is the most labour-intensive step, as it involves systematising the dataset and mapping it to a suitable ontology, as well as designing the ETL (extract-transform-load) process in the adapter module. The CollecTRI dataset is comparatively simple, which makes it a good example. You can find a detailed description of the process below (adapter design and ontolgy mapping).

When building the adapter, it can be helpful to use the Pandas functionality of BioCypher to preview the KG components. Using the add() and to_df() methods, we can check whether the adapter is working as expected.

bc.add(adapter.get_nodes())
bc.add(adapter.get_edges())
dfs = bc.to_df()
for name, df in dfs.items():
    print(name)
    print(df.head())

4. Run BioCypher to create the knowledge graph. This step is straightforward, using the information provided by the mapping configuration and the process provided by the adapter created in the previous step. For compatibility with the Docker compose workflow, we use the write_nodes() and write_edges() methods to generate CSV files for import into Neo4j, as well as the import call statement and a summary of the build process.

bc.write_nodes(adapter.get_nodes())
bc.write_edges(adapter.get_edges())

# Write admin import statement
bc.write_import_call()

# Print summary
bc.summary()

5. Run Docker compose to deploy the knowledge graph. Running the standard docker-compose.yaml configuration will build the graph, import it into Neo4j, and deploy a Neo4j instance to be accessed on https://localhost:7474. The graph can then be browsed and queried.

docker compose up -d

You can also include the ChatGSE frontend in the deployment by running the docker-compose-chatgse.yaml configuration. This will also deploy a ChatGSE instance to be accessed on https://localhost:8501. In the Knowledge Graph tab, you can use natural language queries to generate Cypher queries and run them on the graph. For connecting, you need to change the Neo4j host IP from localhost to deploy, which is the name of the Docker service running the Neo4j instance. You should be able to answer questions like "Which transcription factors activate TP53?", "Which genes are regulated by transcription factors starting with 'ZNF'?", or "Which are DNA-binding transcription factors?"

docker compose -f docker-compose-chatgse.yaml up -d

To stop the deployment, run

docker compose down --volumes

or

docker compose -f docker-compose-chatgse.yaml down --volumes

Removing the volumes is necessary to ensure a clean deployment when running docker compose up again. Otherwise, the graph will contain duplicate nodes and edges.

Adapter design

We can look at the downloaded dataset (using the path from the previous step) to get an idea of its contents:

import pandas as pd
df = pd.read_csv(paths[0])
print(df.head())
#   source target  weight  ...                                          resources                                               PMID       sign.decision
# 0    MYC   TERT       1  ...  ExTRI;HTRI;TRRUST;TFactS;NTNU.Curated;Pavlidis...  10022128;10491298;10606235;10637317;10723141;1...                PMID
# 1   SPI1  BGLAP       1  ...                                              ExTRI                                           10022617  default activation
# 2    AP1    JUN       1  ...                          ExTRI;TRRUST;NTNU.Curated  10022869;10037172;10208431;10366004;11281649;1...                PMID
# 3  SMAD3    JUN       1  ...                   ExTRI;TRRUST;TFactS;NTNU.Curated                                  10022869;12374795                PMID
# 4  SMAD4    JUN       1  ...                   ExTRI;TRRUST;TFactS;NTNU.Curated                                  10022869;12374795                PMID
print(df.columns)
# Index(['source', 'target', 'weight', 'TF.category', 'resources', 'PMID',
#        'sign.decision'],
#       dtype='object')

We can then use this knowledge to design the adapater, i.e., the ETL process. Briefly, the adapter extracts sources and targets, which are both genes, and establishes relationships that embody the regulons. These relationships are enriched by the curation information contained in the table.

We use Enums to define the types of nodes and edges and their properties. This helps in organising the process and also allows the use of auto-completion in downstream tasks. We have two node types, gene and transcription factor, and one relationship type, transcriptional regulation. We also define the properties of the nodes and edges, which are none for genes, category for transcription factors, and weight, resources, references, and sign_decision for the relationship. (Note that we rename some of the original attributes to make them more intuitive, e.g., PMID to references, or machine-compatible, e.g., sign.decision to sign_decision. This conversion is handled by the adapter and needs to be reflected in our schema configuration.)

The adapter then uses the pandas library to read the dataset and extract the relevant information. We use the _preprocess_data() method to load the dataframe and extract unique genes and TFs. Since each row of the dataset is one relationship, we can simply iterate over the rows to create the relationships directly.

The last component the adapter needs is two public methods, get_nodes() and get_edges(), which return generators of nodes and edges, respectively. These methods are used in the build script (create_knowledge_graph.py) to create the knowledge graph.

Ontology mapping

In addition, we use the information to create an ontology mapping in the schema_config.yaml file, which reflect the ontological grounding of the data. Since CollecTRI deals with transcriptional regulation in a gene-gene context, we only need to define gene nodes and some regulatory interaction between them. For this simple case, we resort to the shallow default ontology, Biolink, which already contains Gene entities and regulatory relationships. This also means we do not need to specify the ontology in the biocypher_config.yaml file, as Biolink is the default.

We use the existing entity type gene, and we extend the existing pairwise gene to gene association relationship to transcriptional regulation using inheritance. For clarity, we also introduce a transcription factor entity type, which inherits from gene; this way, we can query for transcription factors specifically while retaining the ability to query for all genes.

gene:
    represented_as: node
    preferred_id: hgnc.symbol
    properties:
        name: str

transcription factor:
    is_a: gene
    represented_as: node
    preferred_id: hgnc.symbol
    properties:
        name: str
        category: str

transcriptional regulation:
    is_a: pairwise gene to gene interaction
    represented_as: edge
    source: transcription factor
    target: gene
    properties:
        weight: float
        resources: str
        references: str
        sign_decision: str

Note that, since we pass BioCypherNode and BioCypherEdge objects to the BioCypher instance, which already include the correct labels of the ontology classes we map to (gene, transcription factor, and transcriptional regulation), we do not need to specify the input_label fields of each class.

We do, however, add some optional components to the schema configuration, mainly to make interaction with the LLM framework BioChatter easier. For instance, we provide explicit properties for each class, which are used to generate the schema_info.yaml file, an extended schema configuration for BioChatter integration. We also include the name property as a shortcut to the gene symbol without added prefix (which usually is good practice to ensure uniqueness of identifiers, in this case the hgnc.symbol). This way, we (and the LLM) can use the name property to refer to genes by their symbol, e.g., MYC instead of hgnc.symbol:MYC.

We also rename weight to activation_or_inhibition, since it is a binary attribute that only has two values, 1 and -1, which we also modify to become a string with the categories activation and inhibition. This makes the attribute more intuitive to human and LLM users.