Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

Bio Genome Intervals Bed File Basics · FreedomIntelligence | DecimalAI

Bio Genome Intervals Bed File Basics

SkillScore96Maintained

bio-genome-intervals-bed-file-basics

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.

scienceby FreedomIntelligence

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

Research pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/bio-genome-intervals-bed-file-basics/SKILL.md

decimalai skills pull bio-genome-intervals-bed-file-basics --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/bio-genome-intervals-bed-file-basics/SKILL.md https://app.decimal.ai/s/bio-genome-intervals-bed-file-basics/SKILL.md

Raw file: /s/bio-genome-intervals-bed-file-basics/SKILL.md ↗ · machine-readable summary at /s/bio-genome-intervals-bed-file-basics.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

23 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 49d ago

Yes — +17% lift · at higher cost+166% tokens0% turns96% of whole cases pass with skill (22/23)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-09	✗→✓	▲ Improved	76%	0%
case-11	✗→✓	▲ Improved	82%	0%
case-22	✗→✓	▲ Improved	147%	0%
case-23	✗→✓	▲ Improved	78%	0%
case-07	✓→✓	＝ Same ✓	562%	0%

Showing 5 of 23 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/1/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

FreedomIntelligence / OpenClaw-Medical-Skills

skills/bio-genome-intervals-bed-file-basics/SKILL.md

View on GitHub ↗

Imported 8/1/2026Version v2

Skill contents

SKILL.md

<!--

COPYRIGHT NOTICE

This file is part of the "Universal Biomedical Skills" project.

Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

All Rights Reserved.

This code is proprietary and confidential.

Unauthorized copying of this file, via any medium is strictly prohibited.

Provenance: Authenticated by MD BABU MIA

-->

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

Illustrative Want to see what bio-genome-intervals-bed-file-basics would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Latest benchmark · publisher runVerified

8/2/2026, 16:25 UTC · 23 cases

Lift

+17%

96% vs 78%

Agent turns

1.0 vs 1.0

Tokens

+166%

3,401.83 vs 1,281

fail → pass · 4pass → pass · 18fail → fail · 1

Per-case results (23)

Case	Status	Duration (ms)			Turns			Tokens			Tool calls
Case	Status	Without	With	Δ	Without	With	Δ	Without	With	Δ	Without	With	Δ
case-07 Instead of generating windows across an entire genome, I want to divide specific candidate intervals in `enhancers.bed` into smaller 1,000 bp sub-windows. What option should be passed to create windows within these target regions instead of a genome size file?	pass→pass	2,693	3,285	+22%	1	1	0%	497	3,288	+562%	0	0	—
case-01 I need to represent a genomic feature that spans the first 100 base pairs of chromosome 1 in a BED file. A colleague suggested setting the start coordinate to 1 and end coordinate to 100, following 1-based inclusive genomic coordinates. What exact start and end coordinate integers should be placed in the BED file for this interval?	pass→pass	10,549	3,349	-68%	1	1	0%	966	3,331	+245%	0	0	—
case-06 I need to tile a genome using 10,000 bp windows that slide along chromosomes with a step size of 5,000 bp, using a chromosome size file `genome.txt`. What CLI command creates these overlapping sliding windows?	pass→pass	5,047	2,365	-53%	1	1	0%	1,000	3,134	+213%	0	0	—
case-02 When running the standard Unix sort command `sort -k1,1 -k2,2 peaks.bed` on a BED file, genomic regions on chromosome 1 starting at position 1000 appear before position 200 because second column sorting defaults to alphabetical string comparison. What flags should be specified to ensure start coordinates are sorted numerically?	pass→pass	4,679	3,579	-24%	1	1	0%	847	3,347	+295%	0	0	—
case-03 I want to extract nucleotide sequences from a reference genome FASTA file named `hg38.fa` using genomic coordinates defined in `targets.bed`. What command-line tool and flags extract these sequences to an output FASTA file?	pass→pass	6,724	3,815	-43%	1	1	0%	1,316	3,471	+164%	0	0	—
case-04 I am converting RNA-seq alignment records in `aligned.bam` to BED format using bedtools. By default, reads spanning splice junctions are represented as single continuous intervals covering introns. Which bedtools subcommand and flag split spliced alignments into proper block features?	pass→pass	8,900	3,105	-65%	1	1	0%	1,542	3,197	+107%	0	0	—
case-05 I am converting VCF variant calls to BED format using `bcftools query`. VCF positions use 1-based coordinates (`%POS`). If I output `%CHROM\t%POS\t%END`, my start coordinates will be off by one. Which format specifier in `bcftools query` outputs 0-based start coordinates automatically?	pass→pass	6,786	2,662	-61%	1	1	0%	1,385	3,154	+128%	0	0	—
case-08 I am writing a bash validation check using `awk` to flag invalid BED intervals where the start position is greater than or equal to the end position. What conditional comparison inside `awk` detects both inverted and zero-length invalid BED intervals?	pass→pass	5,560	4,568	-18%	1	1	0%	1,065	3,541	+232%	0	0	—
case-09 In Python, I have a list of interval tuples `[('chr1', 100, 200, 'peak1', 100, '+')]`. I want to load these into a genomic interval object without saving them to a disk file first. How do I instantiate this object using standard Python BED processing tools?	fail→pass	10,482	3,851	-63%	1	1	0%	1,988	3,493	+76%	0	0	—
case-10 I have a Python genomic interval object loaded as `bed`. I want to filter out all intervals shorter than 100 bp using Python object methods rather than extracting coordinates into custom list loops. What method and condition should be used?	pass→pass	7,203	3,067	-57%	1	1	0%	1,440	3,227	+124%	0	0	—
case-11 I have transcript structures formatted in a BED12 file named `transcripts.bed` and need to convert them into GFF format for gene annotation tools. Which command line command performs this conversion?	fail→pass	9,141	1,804	-80%	1	1	0%	1,627	2,969	+82%	0	0	—
case-12 I want to isolate intervals on `chr1` from `all_peaks.bed` using `grep`. If I run `grep 'chr1' all_peaks.bed`, lines matching `chr10`, `chr11`, and `chr1_gl000191_random` are also returned. What regex pattern ensures only exact tab-delimited `chr1` records are selected?	pass→pass	6,529	4,679	-28%	1	1	0%	1,266	3,551	+180%	0	0	—
case-13 When running complex interval processing scripts in Python, temporary files build up in system temporary directories. What function provided by the `pybedtools` library removes all temporary files created during the session?	pass→pass	3,325	2,008	-40%	1	1	0%	641	3,014	+370%	0	0	—
case-14 I have created a pandas DataFrame `df` containing genomic coordinates with columns `chrom`, `start`, `end`, `name`, `score`, `strand`. How do I convert this DataFrame directly into an active interval object in Python without writing it to disk first?	pass→pass	11,179	2,876	-74%	1	1	0%	2,112	3,240	+53%	0	0	—
case-15 I loaded a BED file into a Python interval object `bed` and want to convert it into a pandas DataFrame. I want the resulting DataFrame columns explicitly named `chrom`, `start`, `end`, `name`, `score`, `strand`. What method call performs this conversion with named columns?	pass→pass	5,336	1,976	-63%	1	1	0%	1,055	3,026	+187%	0	0	—
case-16 When generating 10kb windows across a genome with `bedtools makewindows -g genome.txt -w 10000`, each output line contains only chromosome, start, and end coordinates. What flag option should be added so each window receives a sequential index identifier in column 4?	pass→pass	5,320	3,600	-32%	1	1	0%	972	3,344	+244%	0	0	—
case-17 In a standard 6-column BED file (BED6), what genomic attributes are expected in the 5th and 6th columns respectively?	pass→pass	3,314	1,873	-43%	1	1	0%	595	2,957	+397%	0	0	—
case-18 I am writing an `awk` script to convert non-header records from a VCF file (where column 1 is CHROM and column 2 is 1-based POS) into a BED3 interval for single-nucleotide variants. How should the 0-based start and exclusive end coordinates be calculated from column 2?	pass→pass	8,386	4,211	-50%	1	1	0%	1,635	3,423	+109%	0	0	—
case-19 I want to divide each chromosome in a genome into exactly 100 equal-sized chunks regardless of chromosome length using `bedtools makewindows`. What parameter flag sets the fixed number of windows per chromosome?	pass→pass	3,435	2,067	-40%	1	1	0%	553	2,957	+435%	0	0	—
case-20 ENCODE narrowPeak files extend standard BED formats to represent ChIP-seq peak regions. Which standard BED format (BED3, BED6, or BED12) forms the initial 6 columns of a narrowPeak file, and what fields follow?	pass→pass	5,563	5,170	-7%	1	1	0%	1,104	3,683	+234%	0	0	—
case-21 I have two BED files, `chip_peaks.bed` and `promoters.bed`. I want to find all ChIP-seq peaks that overlap promoter regions and output the intersected genomic coordinates. How should this overlap operation be executed?	fail→fail	8,513	7,456	-12%	1	1	0%	1,602	4,083	+155%	0	0	—
case-22 I want to calculate the average sequencing depth of coverage across each genomic region in `exons.bed` using aligned reads from a BAM file `sample.bam`. How do I perform this calculation?	fail→pass	9,279	8,940	-4%	1	1	0%	1,818	4,486	+147%	0	0	—
case-23 I have a GTF annotation file `annotation.gtf` containing gene, transcript, and exon features. I need to parse transcript IDs, gene IDs, and exon rank attributes from column 9 in Python. How should GTF feature attributes be parsed?	fail→pass	12,374	8,032	-35%	1	1	0%	2,437	4,326	+78%	0	0	—

These cases were also screened by the skill’s intent / safety pass.See the Trust review ↗Reproduce this benchmark in Colab ↗

How this skill was measured

DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +17 percentage points is the difference between those two pass rates over the 23 comparable cases.

Model measured: gemini-3.6-flash
Graded by: gemini-3.6-flash
Grading method: judged
Verification: verified
Pass rate without the skill: 78.3%
Pass rate with the skill: 95.7%
Cases attempted: 23
Cases counted toward lift: 23
Cases that regressed: 0
Skill version measured: v2
Run completed: 8/2/2026, 16:25 UTC

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-09

Prompt

In Python, I have a list of interval tuples `[('chr1', 100, 200, 'peak1', 100, '+')]`. I want to load these into a genomic interval object without saving them to a disk file first. How do I instantiate this object using standard Python BED processing tools?

Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.

Show both answers

SkillScore blends 4 signals1 of 4 complete

BenchmarkPass rate WITH skill on a verified run96

Live evalNeeds ≥10 evaluated traces in 30 days

AI ratingNeeds ≥10 rated traces

AdoptionNeeds ≥10 serves across ≥2 orgs in 30 days

Efficiency · separate axis (at equal correctness)+0% turns+195% tokens

Shown for transparency; not credited unless a verified run shows no correctness regression.

Backed by 1 of 4 signals — each added signal makes the score more robust.

Verified on gemini-3.6-flash · re-tested 48 days ago

Model	Method	Date	Lift
gemini-3.6-flash	verified	7/26/2026	—

Other measured skills in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

Bio Genome Intervals Bed File Basics

SkillSafety

Source

COPYRIGHT NOTICE

This file is part of the "Universal Biomedical Skills" project.

Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

All Rights Reserved.

This code is proprietary and confidential.

Unauthorized copying of this file, via any medium is strictly prohibited.

Provenance: Authenticated by MD BABU MIA

BED File Basics

BED Format Columns

Coordinate System

Create BED Files

From Text (CLI)

From Python

Sort BED Files

CLI

Python

Validate BED Files

Check Format

Python Validation

Read BED Files

Python

Filter BED Files

By Chromosome

By Size

Python Filtering

Convert Formats

BED to Other Formats

From VCF to BED

From BAM to BED

Interval Arithmetic Basics

Make Windows - Create Genomic Intervals

CLI

Python

Common BED Extensions

Cleanup Temp Files

Related Skills

How this skill was measured

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-09

Related skills