--- name: bio-genome-intervals-bed-file-basics description: BED file format fundamentals, creation, validation, and basic operations. Covers BED3 through BED12 formats, coordinate systems, sorting, and format conversion using bedtools and pybedtools. Use when working with genomic coordinates or preparing interval files for downstream tools. 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 --- # BED File Basics BED (Browser Extensible Data) format stores genomic intervals. Uses 0-based, half-open coordinates. ## BED Format Columns ``` BED3: chr start end BED4: chr start end name BED5: chr start end name score BED6: chr start end name score strand BED12: chr start end name score strand thickStart thickEnd rgb blockCount blockSizes blockStarts ``` ## Coordinate System BED uses 0-based, half-open coordinates: - Start: 0-based (first base is 0) - End: exclusive (not included) - Position 100-200 means bases at positions 100-199 ## Create BED Files ### From Text (CLI) ```bash # Create simple BED3 echo -e "chr1\t100\t200\nchr1\t300\t400" > regions.bed # Create BED6 with name and strand echo -e "chr1\t100\t200\tpeak1\t100\t+" > peaks.bed ``` ### From Python ```python import pybedtools # From string bed_string = '''chr1\t100\t200\tpeak1\t100\t+ chr1\t300\t400\tpeak2\t200\t-''' bed = pybedtools.BedTool(bed_string, from_string=True) # From list of tuples intervals = [ ('chr1', 100, 200, 'peak1', 100, '+'), ('chr1', 300, 400, 'peak2', 200, '-'), ] bed = pybedtools.BedTool(intervals) # From pandas DataFrame import pandas as pd df = pd.DataFrame({ 'chrom': ['chr1', 'chr1'], 'start': [100, 300], 'end': [200, 400], 'name': ['peak1', 'peak2'], 'score': [100, 200], 'strand': ['+', '-'] }) bed = pybedtools.BedTool.from_dataframe(df) # Save to file bed.saveas('output.bed') ``` ## Sort BED Files ### CLI ```bash # Sort by chromosome and position sort -k1,1 -k2,2n input.bed > sorted.bed # Using bedtools bedtools sort -i input.bed > sorted.bed # Sort by chromosome, start, then end sort -k1,1 -k2,2n -k3,3n input.bed > sorted.bed ``` ### Python ```python import pybedtools bed = pybedtools.BedTool('input.bed') sorted_bed = bed.sort() sorted_bed.saveas('sorted.bed') ``` ## Validate BED Files ### Check Format ```bash # Check column count awk -F'\t' '{print NF}' input.bed | sort -u # Check for invalid coordinates (start >= end) awk '$2 >= $3' input.bed # Check for negative coordinates awk '$2 < 0 || $3 < 0' input.bed # Validate with bedtools bedtools sort -i input.bed > /dev/null 2>&1 && echo "Valid" || echo "Invalid" ``` ### Python Validation ```python import pybedtools def validate_bed(filepath): try: bed = pybedtools.BedTool(filepath) for interval in bed: if interval.start < 0 or interval.end < 0: return False, 'Negative coordinates' if interval.start >= interval.end: return False, f'Invalid interval: {interval.start} >= {interval.end}' return True, 'Valid' except Exception as e: return False, str(e) valid, msg = validate_bed('input.bed') print(f'{msg}') ``` ## Read BED Files ### Python ```python import pybedtools # Load BED file bed = pybedtools.BedTool('input.bed') # Iterate over intervals for interval in bed: print(f'{interval.chrom}:{interval.start}-{interval.end}') print(f'Name: {interval.name}, Score: {interval.score}, Strand: {interval.strand}') # Count intervals n_intervals = bed.count() # Convert to pandas DataFrame df = bed.to_dataframe() # Access specific columns df = bed.to_dataframe(names=['chrom', 'start', 'end', 'name', 'score', 'strand']) ``` ## Filter BED Files ### By Chromosome ```bash # Single chromosome grep "^chr1\t" input.bed > chr1.bed # Multiple chromosomes grep -E "^(chr1|chr2)\t" input.bed > chr1_2.bed # Exclude chromosome grep -v "^chrM\t" input.bed > no_chrM.bed ``` ### By Size ```bash # Intervals >= 100bp awk '($3 - $2) >= 100' input.bed > large.bed # Intervals between 100-500bp awk '($3 - $2) >= 100 && ($3 - $2) <= 500' input.bed > medium.bed ``` ### Python Filtering ```python import pybedtools bed = pybedtools.BedTool('input.bed') # Filter by chromosome chr1 = bed.filter(lambda x: x.chrom == 'chr1') # Filter by size large = bed.filter(lambda x: len(x) >= 100) # Filter by strand plus_strand = bed.filter(lambda x: x.strand == '+') # Filter by score high_score = bed.filter(lambda x: float(x.score) >= 500) # Chain filters result = bed.filter(lambda x: x.chrom == 'chr1' and len(x) >= 100) result.saveas('filtered.bed') ``` ## Convert Formats ### BED to Other Formats ```bash # BED to GFF bedtools bed12togff -i input.bed > output.gff # BED to FASTA (extract sequences) bedtools getfasta -fi reference.fa -bed input.bed -fo output.fa # BED to FASTA with names bedtools getfasta -fi reference.fa -bed input.bed -name -fo output.fa ``` ### From VCF to BED ```bash # Extract variant positions bcftools query -f '%CHROM\t%POS0\t%END\n' input.vcf > variants.bed # Or using awk (simpler for SNPs) grep -v "^#" input.vcf | awk '{print $1"\t"$2-1"\t"$2}' > snps.bed ``` ### From BAM to BED ```bash # Convert alignments to BED bedtools bamtobed -i input.bam > alignments.bed # BED12 for spliced alignments bedtools bamtobed -i input.bam -split > spliced.bed ``` ## Interval Arithmetic Basics ```python import pybedtools interval = pybedtools.create_interval_from_list(['chr1', '100', '200', 'peak1', '0', '+']) # Access fields print(interval.chrom) # chr1 print(interval.start) # 100 (int) print(interval.end) # 200 (int) print(interval.name) # peak1 print(interval.score) # 0 print(interval.strand) # + # Get length print(len(interval)) # 100 # Get fields list print(interval.fields) # ['chr1', '100', '200', 'peak1', '0', '+'] ``` ## Make Windows - Create Genomic Intervals ### CLI ```bash # Fixed-size windows across genome bedtools makewindows -g genome.txt -w 10000 > windows_10kb.bed # Windows with step size (sliding windows) bedtools makewindows -g genome.txt -w 10000 -s 5000 > sliding_10kb.bed # Fixed number of windows per chromosome bedtools makewindows -g genome.txt -n 100 > 100_windows_per_chr.bed # Windows within BED regions bedtools makewindows -b regions.bed -w 1000 > windows_in_regions.bed # Add window ID bedtools makewindows -g genome.txt -w 10000 -i winnum > numbered_windows.bed # Source chromosome in name bedtools makewindows -g genome.txt -w 10000 -i srcwinnum > windows_with_source.bed ``` ### Python ```python import pybedtools # From genome file windows = pybedtools.BedTool().window_maker(g='genome.txt', w=10000) # Sliding windows windows = pybedtools.BedTool().window_maker(g='genome.txt', w=10000, s=5000) # From BED regions bed = pybedtools.BedTool('regions.bed') windows = pybedtools.BedTool().window_maker(b=bed.fn, w=1000) windows.saveas('windows.bed') ``` ## Common BED Extensions | Format | Description | Columns | |--------|-------------|---------| | narrowPeak | ENCODE narrow peaks | BED6 + signalValue, pValue, qValue, peak | | broadPeak | ENCODE broad peaks | BED6 + signalValue, pValue, qValue | | bedGraph | Signal track | chr, start, end, value | | bedpe | Paired intervals | chr1, s1, e1, chr2, s2, e2, name, score, strand1, strand2 | ## Cleanup Temp Files ```python import pybedtools # At end of script pybedtools.cleanup() # Or use context manager (auto-cleanup) pybedtools.set_tempdir('/tmp/pybedtools') ``` ## Related Skills - interval-arithmetic - intersect, subtract, merge operations - gtf-gff-handling - annotation file parsing - coverage-analysis - depth calculations - alignment-files/sam-bam-basics - BAM to BED conversion --- Source attribution: GPTomics Original project: https://github.com/GPTomics/bioSkills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Mega AI Hub resource metadata Creator / publisher: GPTomics Original project: https://github.com/GPTomics/bioSkills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Data & Analytics, Education & Research Roles: Business Intelligence Analyst, Data Scientist, Researcher Specialties: API, Backend Tools: Bedtools, Pybedtools Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 2b79b8004d553dfc219e3c3f66de5e4d5f502f666ab3a808676cfb70ee65d352 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.