kegg-database

You can retrieve biological pathways and map genes through direct HTTP requests to the KEGG REST API. This workflow fetches pathway data, gene sequences, and compound structures for academic research without requiring external bioinformatics packages.

KEGG ID conversion is the process of mapping internal KEGG database identifiers to external database identifiers like UniProt or NCBI Gene. It works by querying the REST API to fetch corresponding cross-references for downstream biological data analysis.

Yes, you can use Python to fetch drug interactions and compound structures from KEGG. The workflow supports direct HTTP and REST operations, allowing Python scripts to query the database and retrieve specific biological data programmatically.

No, this approach does not require external bioinformatics dependencies for pathway analysis. It relies on direct REST API access to the KEGG database, allowing you to execute queries using standard Python HTTP operations without additional libraries.

The best way to find human genes associated with a metabolic pathway is to query the KEGG REST API using the specific human pathway identifier. This retrieves all linked genes, which can then be processed for ID conversion to external databases like UniProt.

Limitations of using direct REST API access for large-scale gene-pathway mapping include potential rate limits and the need to manually handle HTTP responses. Users must parse raw text data from KEGG and manage query batching within their Python workflows.