Bedtools Migration Guide

Bedtools Migration Guide#

This guide maps bedtools commands to their GIQL equivalents. If you’re familiar with bedtools and want to replicate specific operations in GIQL, use this reference to find the corresponding query patterns.

Quick Reference Table#

Bedtools Command

GIQL Equivalent

Recipe

intersect -a A -b B

SELECT DISTINCT a.* FROM a, b WHERE a.interval INTERSECTS b.interval

Default: Report overlaps between A and B

intersect -a A -b B -wa

SELECT a.* FROM a, b WHERE a.interval INTERSECTS b.interval

-wa: Write original A entry for each overlap

intersect -a A -b B -wb

SELECT b.* FROM a, b WHERE a.interval INTERSECTS b.interval

-wb: Write original B entry for each overlap

intersect -a A -b B -wa -wb

SELECT a.*, b.* FROM a, b WHERE a.interval INTERSECTS b.interval

-wa -wb: Write both A and B entries

intersect -a A -b B -v

SELECT a.* FROM a LEFT JOIN b ... WHERE b.chrom IS NULL

-v: Report A entries with NO overlap in B

intersect -a A -b B -u

SELECT DISTINCT a.* FROM a JOIN b ...

-u: Report A entries with ANY overlap (unique)

intersect -a A -b B -c

SELECT a.*, COUNT(b.name) ... GROUP BY ...

-c: Count B overlaps for each A feature

intersect -a A -b B -wo

SELECT a.*, b.*, (overlap calculation) ...

-wo: Write overlap amount in base pairs

intersect -a A -b B -loj

SELECT a.*, b.* FROM a LEFT JOIN b ...

-loj: Left outer join

closest -a A -b B -k N

CROSS JOIN LATERAL NEAREST(b, reference=a.interval, k=N)

-k: Find k nearest features

closest -a A -b B -d

SELECT ..., DISTANCE(a.interval, b.interval) ...

-d: Report distance

cluster -i A

SELECT *, CLUSTER(interval) AS cluster_id FROM a

Basic clustering

cluster -i A -d N

SELECT *, CLUSTER(interval, N) AS cluster_id FROM a

-d: Cluster with distance parameter

merge -i A

SELECT MERGE(interval) FROM a

Basic merge

merge -i A -d N

SELECT MERGE(interval, N) FROM a

-d: Merge with distance parameter

merge -i A -c 1 -o count

SELECT MERGE(interval), COUNT(*) FROM a

-c -o count: Count merged features

bedtools intersect#

Default: Report overlaps between A and B#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed

GIQL:

SELECT DISTINCT a.*
FROM features_a a, features_b b
WHERE a.interval INTERSECTS b.interval

-wa: Write original A entry for each overlap#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -wa

GIQL:

SELECT a.*
FROM features_a a, features_b b
WHERE a.interval INTERSECTS b.interval

-wb: Write original B entry for each overlap#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -wb

GIQL:

SELECT b.*
FROM features_a a, features_b b
WHERE a.interval INTERSECTS b.interval

-wa -wb: Write both A and B entries#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -wa -wb

GIQL:

SELECT a.*, b.*
FROM features_a a, features_b b
WHERE a.interval INTERSECTS b.interval

-v: Report A entries with NO overlap in B#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -v

GIQL:

SELECT a.*
FROM features_a a
LEFT JOIN features_b b ON a.interval INTERSECTS b.interval
WHERE b.chrom IS NULL

-u: Report A entries with ANY overlap (unique)#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -u

GIQL:

SELECT DISTINCT a.*
FROM features_a a
INNER JOIN features_b b ON a.interval INTERSECTS b.interval

-c: Count B overlaps for each A feature#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -c

GIQL:

SELECT a.*, COUNT(b.name) AS overlap_count
FROM features_a a
LEFT JOIN features_b b ON a.interval INTERSECTS b.interval
GROUP BY a.chrom, a.start, a.end, a.name, a.score, a.strand

-wo: Write overlap amount in base pairs#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -wo

GIQL:

SELECT
    a.*,
    b.*,
    (LEAST(a.end, b.end) - GREATEST(a.start, b.start)) AS overlap_bp
FROM features_a a, features_b b
WHERE a.interval INTERSECTS b.interval

-wao: Write overlap amount for ALL A features#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -wao

GIQL:

SELECT
    a.*,
    b.*,
    CASE
        WHEN b.chrom IS NULL THEN 0
        ELSE LEAST(a.end, b.end) - GREATEST(a.start, b.start)
    END AS overlap_bp
FROM features_a a
LEFT JOIN features_b b ON a.interval INTERSECTS b.interval

-loj: Left outer join#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -loj

GIQL:

SELECT a.*, b.*
FROM features_a a
LEFT JOIN features_b b ON a.interval INTERSECTS b.interval

-s: Same strand overlaps only#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -s

GIQL:

SELECT a.*
FROM features_a a, features_b b
WHERE a.interval INTERSECTS b.interval
  AND a.strand = b.strand

-S: Opposite strand overlaps only#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -S

GIQL:

SELECT a.*
FROM features_a a, features_b b
WHERE a.interval INTERSECTS b.interval
  AND a.strand != b.strand
  AND a.strand IN ('+', '-')
  AND b.strand IN ('+', '-')

-f: Minimum overlap fraction of A#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -f 0.5

GIQL:

SELECT a.*
FROM features_a a, features_b b
WHERE a.interval INTERSECTS b.interval
  AND (
      LEAST(a.end, b.end) - GREATEST(a.start, b.start)
  ) >= 0.5 * (a.end - a.start)

-r: Reciprocal overlap#

Bedtools:

bedtools intersect -a file_a.bed -b file_b.bed -f 0.5 -r

GIQL:

WITH overlap_calcs AS (
    SELECT
        a.*,
        (LEAST(a.end, b.end) - GREATEST(a.start, b.start)) AS overlap_bp,
        (a.end - a.start) AS a_length,
        (b.end - b.start) AS b_length
    FROM features_a a, features_b b
    WHERE a.interval INTERSECTS b.interval
)
SELECT chrom, start, end, name, score, strand
FROM overlap_calcs
WHERE overlap_bp >= 0.5 * a_length
  AND overlap_bp >= 0.5 * b_length

bedtools closest#

-k: Find k nearest features#

Bedtools:

bedtools closest -a peaks.bed -b genes.bed -k 3

GIQL:

SELECT
    peaks.name AS peak,
    nearest.name AS gene,
    nearest.distance
FROM peaks
CROSS JOIN LATERAL NEAREST(genes, reference=peaks.interval, k=3) AS nearest
ORDER BY peaks.name, nearest.distance

-d: Report distance#

Bedtools:

bedtools closest -a peaks.bed -b genes.bed -d

GIQL:

SELECT
    a.name AS peak,
    b.name AS gene,
    DISTANCE(a.interval, b.interval) AS distance
FROM peaks a
CROSS JOIN genes b
WHERE a.chrom = b.chrom
ORDER BY a.name, distance

Or using NEAREST for just the closest:

SELECT
    peaks.name AS peak,
    nearest.name AS gene,
    nearest.distance
FROM peaks
CROSS JOIN LATERAL NEAREST(genes, reference=peaks.interval, k=1) AS nearest

-s: Same strand only#

Bedtools:

bedtools closest -a peaks.bed -b genes.bed -s -k 3

GIQL:

SELECT
    peaks.name,
    nearest.name AS gene,
    nearest.distance
FROM peaks
CROSS JOIN LATERAL NEAREST(
    genes,
    reference=peaks.interval,
    k=3,
    stranded=true
) AS nearest
ORDER BY peaks.name, nearest.distance

bedtools cluster#

Basic clustering#

Bedtools:

bedtools cluster -i features.bed

GIQL:

SELECT
    *,
    CLUSTER(interval) AS cluster_id
FROM features
ORDER BY chrom, start

-d: Cluster with distance parameter#

Bedtools:

bedtools cluster -i features.bed -d 1000

GIQL:

SELECT
    *,
    CLUSTER(interval, 1000) AS cluster_id
FROM features
ORDER BY chrom, start

-s: Strand-specific clustering#

Bedtools:

bedtools cluster -i features.bed -s

GIQL:

SELECT
    *,
    CLUSTER(interval, stranded=true) AS cluster_id
FROM features
ORDER BY chrom, strand, start

bedtools merge#

Basic merge#

Bedtools:

bedtools merge -i features.bed

GIQL:

SELECT MERGE(interval)
FROM features

-d: Merge with distance parameter#

Bedtools:

bedtools merge -i features.bed -d 1000

GIQL:

SELECT MERGE(interval, 1000)
FROM features

-s: Strand-specific merge#

Bedtools:

bedtools merge -i features.bed -s

GIQL:

SELECT MERGE(interval, stranded=true)
FROM features

-c -o count: Count merged features#

Bedtools:

bedtools merge -i features.bed -c 1 -o count

GIQL:

SELECT
    MERGE(interval),
    COUNT(*) AS feature_count
FROM features

-c -o mean: Average score#

Bedtools:

bedtools merge -i features.bed -c 5 -o mean

GIQL:

SELECT
    MERGE(interval),
    AVG(score) AS avg_score
FROM features

-c -o collapse: Collect names#

Bedtools:

bedtools merge -i features.bed -c 4 -o collapse

GIQL:

SELECT
    MERGE(interval),
    STRING_AGG(name, ',') AS feature_names
FROM features

Key Differences from Bedtools#

  1. SQL-based syntax: GIQL uses SQL syntax, which may be more familiar to users with database experience and allows integration with other SQL features.

  2. Explicit joins: Instead of implicit A/B file relationships, GIQL uses explicit JOIN syntax, making the relationship between tables clearer.

  3. Flexible output: SQL’s SELECT clause gives you full control over which columns to return and how to format them.

  4. Built-in aggregation: SQL’s GROUP BY and aggregate functions (COUNT, AVG, SUM, etc.) are available directly, without needing separate post-processing.

  5. Database integration: GIQL queries can be run against database tables, enabling integration with other data and persistence of results.

  6. Multi-backend support: The same GIQL query can run on DuckDB, SQLite, or other supported backends without modification.

Contents