Bio Genome Intervals Proximity Operations

Version Compatibility

Reference examples tested with: bedtools 2.31+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Proximity Operations

"Find nearest features or extend intervals" → Identify the closest genomic feature to each interval, or expand intervals by a fixed flank size.

• CLI: bedtools closest -a peaks.bed -b genes.bed, bedtools slop -b 1000
• Python: a.closest(b), a.slop(b=1000, g=genome) (pybedtools)

Operations for finding nearby features and extending intervals using bedtools and pybedtools.

Closest - Find Nearest Feature

CLI

# Find nearest gene to each peak
bedtools closest -a peaks.bed -b genes.bed > peaks_with_nearest.bed

# Report distance to nearest feature
bedtools closest -a peaks.bed -b genes.bed -d > with_distance.bed

# Ignore overlapping features (find next nearest)
bedtools closest -a peaks.bed -b genes.bed -io > nearest_non_overlap.bed

# Ignore features on different strands
bedtools closest -a peaks.bed -b genes.bed -s > same_strand.bed

# Ignore features on same strand (opposite strand only)
bedtools closest -a peaks.bed -b genes.bed -S > opposite_strand.bed

# Only upstream features (5' direction relative to A strand)
bedtools closest -a peaks.bed -b genes.bed -D a -iu > upstream_only.bed

# Only downstream features
bedtools closest -a peaks.bed -b genes.bed -D a -id > downstream_only.bed

# Report multiple ties
bedtools closest -a peaks.bed -b genes.bed -t all > all_ties.bed

# First tie only
bedtools closest -a peaks.bed -b genes.bed -t first > first_tie.bed

Python

import pybedtools

a = pybedtools.BedTool('peaks.bed')
b = pybedtools.BedTool('genes.bed')

# Basic closest
result = a.closest(b)

# With distance
result = a.closest(b, d=True)

# Ignore overlaps
result = a.closest(b, io=True)

# Same strand only
result = a.closest(b, s=True)

# Report all ties
result = a.closest(b, t='all')

result.saveas('closest.bed')

Window - Find Features Within Distance

CLI

# Find genes within 10kb of peaks
bedtools window -a peaks.bed -b genes.bed -w 10000 > genes_within_10kb.bed

# Asymmetric window (5kb upstream, 2kb downstream of A)
bedtools window -a peaks.bed -b genes.bed -l 5000 -r 2000 > asymmetric.bed

# Same strand only
bedtools window -a peaks.bed -b genes.bed -w 10000 -sm > same_strand.bed

# Strand-aware window (upstream/downstream relative to strand)
bedtools window -a peaks.bed -b genes.bed -l 5000 -r 2000 -sw > strand_aware.bed

Python

import pybedtools

a = pybedtools.BedTool('peaks.bed')
b = pybedtools.BedTool('genes.bed')

# Symmetric window
result = a.window(b, w=10000)

# Asymmetric window
result = a.window(b, l=5000, r=2000)

# Same strand
result = a.window(b, w=10000, sm=True)

result.saveas('window.bed')

Slop - Extend Interval Boundaries

CLI

# Extend both ends by 100bp (requires genome file)
bedtools slop -i peaks.bed -g genome.txt -b 100 > extended.bed

# Extend 5' end by 500bp, 3' end by 100bp
bedtools slop -i peaks.bed -g genome.txt -l 500 -r 100 > asymmetric.bed

# Strand-aware extension (upstream/downstream)
bedtools slop -i peaks.bed -g genome.txt -l 500 -r 100 -s > strand_aware.bed

# Extend by percentage
bedtools slop -i peaks.bed -g genome.txt -b 0.5 -pct > extend_50pct.bed

# Header passthrough
bedtools slop -i peaks.bed -g genome.txt -b 100 -header > with_header.bed

Python

import pybedtools

bed = pybedtools.BedTool('peaks.bed')

# Symmetric extension
result = bed.slop(g='genome.txt', b=100)

# Asymmetric extension
result = bed.slop(g='genome.txt', l=500, r=100)

# Strand-aware
result = bed.slop(g='genome.txt', l=500, r=100, s=True)

# Percentage
result = bed.slop(g='genome.txt', b=0.5, pct=True)

result.saveas('extended.bed')

Flank - Get Flanking Regions

CLI

# Get 100bp flanks on both sides (not original interval)
bedtools flank -i peaks.bed -g genome.txt -b 100 > flanks.bed

# Get upstream flank only
bedtools flank -i peaks.bed -g genome.txt -l 100 -r 0 > upstream.bed

# Get downstream flank only
bedtools flank -i peaks.bed -g genome.txt -l 0 -r 100 > downstream.bed

# Strand-aware flanking
bedtools flank -i peaks.bed -g genome.txt -l 500 -r 0 -s > upstream_strand.bed

# Percentage of interval size
bedtools flank -i peaks.bed -g genome.txt -b 0.5 -pct > flank_50pct.bed

Python

import pybedtools

bed = pybedtools.BedTool('peaks.bed')

# Both flanks
result = bed.flank(g='genome.txt', b=100)

# Upstream only (left)
result = bed.flank(g='genome.txt', l=100, r=0)

# Strand-aware upstream
result = bed.flank(g='genome.txt', l=500, r=0, s=True)

result.saveas('flanks.bed')

Shift - Move Intervals

CLI

# Shift all intervals downstream by 100bp
bedtools shift -i peaks.bed -g genome.txt -s 100 > shifted.bed

# Shift upstream (negative)
bedtools shift -i peaks.bed -g genome.txt -s -100 > shifted_up.bed

# Shift by percentage
bedtools shift -i peaks.bed -g genome.txt -s 0.5 -pct > shift_50pct.bed

# Shift with chromosome-specific values
bedtools shift -i peaks.bed -g genome.txt -s 100 -p 200 > shifted.bed  # plus strand +100, minus +200

Python

import pybedtools

bed = pybedtools.BedTool('peaks.bed')

# Shift downstream
result = bed.shift(g='genome.txt', s=100)

# Shift upstream
result = bed.shift(g='genome.txt', s=-100)

result.saveas('shifted.bed')

Common Patterns

Find Peaks Within 10kb of TSS

# Get TSS from genes (assumes BED6+ with strand)
awk -v OFS='\t' '{
    if ($6 == "+") print $1, $2, $2+1, $4, $5, $6;
    else print $1, $3-1, $3, $4, $5, $6;
}' genes.bed > tss.bed

# Find peaks within 10kb of TSS
bedtools window -a peaks.bed -b tss.bed -w 10000 > peaks_near_tss.bed

Create Promoter Regions

# 2kb upstream, 500bp downstream of TSS (strand-aware)
bedtools flank -i tss.bed -g genome.txt -l 2000 -r 0 -s | \
    bedtools slop -i stdin -g genome.txt -l 0 -r 500 -s > promoters.bed

# Or simpler with slop from TSS
bedtools slop -i tss.bed -g genome.txt -l 2000 -r 500 -s > promoters.bed

Find Nearest Gene Within 100kb

import pybedtools

peaks = pybedtools.BedTool('peaks.bed')
genes = pybedtools.BedTool('genes.bed')

# Find closest gene
closest = peaks.closest(genes, d=True)

# Filter to within 100kb
within_100kb = closest.filter(lambda x: abs(int(x.fields[-1])) <= 100000)
within_100kb.saveas('peaks_with_nearby_genes.bed')

Enhancer-Gene Assignment

Goal: Link putative enhancers to potential target genes based on genomic proximity within a defined distance window.

Approach: Use bedtools window with a large symmetric window (e.g., 1Mb) to find all TSS sites near each enhancer, producing an enhancer-gene pair list for downstream regulatory analysis.

import pybedtools

enhancers = pybedtools.BedTool('enhancers.bed')
tss = pybedtools.BedTool('tss.bed')

# Find all genes within 1Mb window
assignments = enhancers.window(tss, w=1000000)

# Convert to DataFrame for analysis
df = assignments.to_dataframe()

Genome File Format

# genome.txt format: chromosome<TAB>size
chr1	248956422
chr2	242193529
chr3	198295559
...

# Create from FASTA index
cut -f1,2 reference.fa.fai > genome.txt

# Download UCSC chromosome sizes
wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes

Key Parameters

Related Skills

• bed-file-basics - BED format fundamentals
• interval-arithmetic - intersect, subtract, merge
• gtf-gff-handling - Extract TSS from annotations
• chip-seq/peak-annotation - Peak annotation