--- name: bio-genome-intervals-proximity-operations-32c07fd7273d 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 --- # Proximity Operations Operations for finding nearby features and extending intervals using bedtools and pybedtools. ## Closest - Find Nearest Feature ### CLI ```bash # 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 ```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 ```bash # 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 ```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 ```bash # 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 ```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 ```bash # 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 ```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 ```bash # 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 ```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 ```bash # 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 ```bash # 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 ```python 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 ```python 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: chromosomesize 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 --- Source attribution: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/bio-genome-intervals-proximity-operations/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/bio-genome-intervals-proximity-operations/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Product Management Roles: Data Scientist, Business Intelligence Analyst, Researcher, Market Research Analyst, Product Manager Specialties: API, Backend Tools: Bedtools, Pybedtools Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: ca551cf858414974aed96e0ae8a0f20ee041c53211676ac01bce82627c8cd1a2