bedrock-bio

Open-Access Computational Biology Datasets

Description

Efficiently access a curated library of open-access computational biology datasets. Tables support predicate pushdown and projection to the cloud storage backend, enabling quick, iterative access to otherwise massive, unwieldy tables.

bedrock_bio consists of five user-facing functions:

• list_namespaces(): returns a list of available namespace (data source) identifiers
• describe_namespace('<name>'): returns a namespace's name, citation, license, context, and its tables
• list_tables(namespace): returns a list of table identifiers, optionally filtered to one namespace
• describe_table('<name>'): returns a table's context, column definitions, and partition columns (with their allowed values)
• load_table('<name>'): returns a lazy DuckDB relation for a table

DuckDB methods (filter, select, limit) can be used on the relation returned by load_table to push down row filters and column selections to the storage backend. Filtering on the partition columns returned by describe_table gives the fastest reads.

Installation

To install the latest release from PyPI:

pip install bedrock-bio

Or install the current development version from GitHub:

pip install git+https://github.com/bedrock-bio/bedrock-bio-client.git@main#subdirectory=python

Examples

import bedrock_bio as bb

List available tables:

bb.list_tables()

Describe a table to see its metadata, citation, and columns:

bb.describe_table('ukb_ppp.pqtls')

Lazily load a table, filter on partition columns (for fastest reads), select columns, and collect into an in-memory data frame:

df = (
    bb.load_table('ukb_ppp.pqtls')
      .filter("ancestry = 'EUR' AND protein_id = 'A0FGR8' AND panel = 'Inflammation'")
      .select('chromosome, position, effect_allele, other_allele, beta, neg_log_10_p_value')
      .df()
)

Dataset Requests

To request the addition of a new table to the library, open an issue.