Genomic Interval Query Language (GIQL)
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docs | syntax | transpiler
GIQL is an extended SQL dialect that allows you to declaratively express genomic interval operations.
The giql Python package transpiles GIQL queries into standard SQL syntax for execution on any database or analytics engine.
Note: This project is in active development — APIs, syntax, and behavior may change.
To install the transpiler:
pip install giqlThe giql package transpiles GIQL queries to standard SQL.
from giql import transpile
sql = transpile(
"SELECT * FROM peaks WHERE interval INTERSECTS 'chr1:1000-2000'",
tables=["peaks"],
)
print(sql)SELECT
*
FROM peaks
WHERE
(
"chrom" = 'chr1' AND "start" < 2000 AND "end" > 1000
)Each table referenced in a GIQL query exposes a genomic "pseudo-column" that maps to separate logical chromosome, start, end, and strand columns. You can customize the column mappings.
from giql import Table, transpile
sql = transpile(
"SELECT * FROM variants WHERE position INTERSECTS 'chr1:1000-2000'",
tables=[
Table(
"variants",
genomic_col="position",
chrom_col="chromosome",
start_col="start_pos",
end_col="end_pos",
)
],
)
print(sql)The transpiled SQL can be executed with fast genome-unaware databases or in-memory analytic engines like DuckDB.
You can also use oxbow to efficiently stream specialized genomics formats into DuckDB.
import duckdb
import oxbow as ox
from giql import transpile
conn = duckdb.connect()
# Load a streaming data source as a DuckDB relation
peaks = ox.from_bed("peaks.bed", bed_schema="bed6+4").to_duckdb(conn)
sql = transpile(
"SELECT * FROM peaks WHERE interval INTERSECTS 'chr1:1000-2000'",
tables=["peaks"],
)
# Execute and return the output as a dataframe
df = con.execute(sql).fetchdf()git clone https://github.com/abdenlab/giql.git
cd giql
uv syncTo build the documentation locally:
uv run --group docs sphinx-build docs docs/_build
# The built docs will be in docs/_build/html/For serve the docs locally with automatic rebuild:
uv run --group docs sphinx-autobuild docs docs/_build