Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
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.
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Reference examples tested with: bcftools 1.19+, bedtools 2.31+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signatures<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Work with BED files" → Read, create, and manipulate genomic interval files in BED format (0-based, half-open coordinates).
pybedtools.BedTool('file.bed') (pybedtools)bedtools commandsBED (Browser Extensible Data) format stores genomic intervals. Uses 0-based, half-open coordinates.
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
BED uses 0-based, half-open coordinates:
# 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
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 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
import pybedtools
bed = pybedtools.BedTool('input.bed')
sorted_bed = bed.sort()
sorted_bed.saveas('sorted.bed')
# 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"
Goal: Programmatically validate a BED file for common format errors before using it in downstream tools.
Approach: Load the file with pybedtools, iterate through all intervals checking for negative coordinates and invalid ranges (start >= end), and return a pass/fail status with error description.
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}')
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'])
# 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
# 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
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')
# 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
# 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
# Convert alignments to BED
bedtools bamtobed -i input.bam > alignments.bed
# BED12 for spliced alignments
bedtools bamtobed -i input.bam -split > spliced.bed
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', '+']
# 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
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')
| 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 |
import pybedtools
# At end of script
pybedtools.cleanup()
# Or use context manager (auto-cleanup)
pybedtools.set_tempdir('/tmp/pybedtools')
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
日本語の概要は準備中です。原文の説明を表示しています。
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
日本語の概要は準備中です。原文の説明を表示しています。
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
日本語の概要は準備中です。原文の説明を表示しています。