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name: bio-genome-intervals-proximity-operations description: Find nearest features, search within windows, and extend intervals using closest, window, flank, and slop operations. Use when performing TSS proximity analysis, assigning enhancers to genes, defining promoter regions, or finding nearby genomic features. tool_type: mixed primary_tool: bedtools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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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

Operation	Parameter	Description
closest -d	Distance	Report distance in last column
closest -io	Ignore overlap	Skip overlapping features
closest -D	Direction	Report signed distance (a/b/ref)
window -w	Window	Symmetric window size
window -l/-r	Left/Right	Asymmetric window
slop -b	Both	Extend both ends
slop -s	Strand	Strand-aware extension
flank -l/-r	Left/Right	Flank size by side

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

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