Bio Genome Intervals Proximity Operations

Proximity Operations

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

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