Agent skill
bio-reference-operations
Generate consensus sequences and manage reference files using samtools. Use when creating consensus from alignments, indexing references, or creating sequence dictionaries.
Stars
163
Forks
31
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/reference-operations
SKILL.md
Reference Operations
Generate consensus sequences and manage reference files using samtools.
samtools faidx - Index Reference FASTA
Create index for random access to reference sequences.
Create Index
bash
samtools faidx reference.fa
# Creates reference.fa.fai
Fetch Region from Reference
bash
samtools faidx reference.fa chr1:1000-2000
Fetch Multiple Regions
bash
samtools faidx reference.fa chr1:1000-2000 chr2:3000-4000
Fetch Entire Chromosome
bash
samtools faidx reference.fa chr1
Output to File
bash
samtools faidx reference.fa chr1:1000-2000 > region.fa
Reverse Complement
bash
samtools faidx -i reference.fa chr1:1000-2000
FAI File Format
chr1 248956422 6 60 61
chr2 242193529 253404903 60 61
Columns: name, length, offset, line bases, line width
samtools dict - Create Sequence Dictionary
Create SAM header dictionary for reference (used by GATK, Picard).
Create Dictionary
bash
samtools dict reference.fa -o reference.dict
With Assembly Info
bash
samtools dict -a GRCh38 -s "Homo sapiens" reference.fa -o reference.dict
Dictionary Format
@HD VN:1.6 SO:unsorted
@SQ SN:chr1 LN:248956422 M5:6aef897c3d6ff0c78aff06ac189178dd UR:file:reference.fa
@SQ SN:chr2 LN:242193529 M5:f98db672eb0993dcfdabafe2a882905c UR:file:reference.fa
samtools consensus - Generate Consensus
Create consensus sequence from alignments.
Basic Consensus
bash
samtools consensus input.bam -o consensus.fa
From Specific Region
bash
samtools consensus -r chr1:1000-2000 input.bam -o region_consensus.fa
Output Formats
bash
# FASTA (default)
samtools consensus -f fasta input.bam -o consensus.fa
# FASTQ (includes quality)
samtools consensus -f fastq input.bam -o consensus.fq
Quality Options
bash
# Minimum depth to call base
samtools consensus -d 5 input.bam -o consensus.fa
# Call all positions (including low coverage)
samtools consensus -a input.bam -o consensus.fa
Ambiguity Handling
bash
# Use IUPAC codes for heterozygous positions
samtools consensus --show-ins no --show-del no input.bam -o consensus.fa
pysam Python Alternative
Fetch from Indexed FASTA
python
import pysam
with pysam.FastaFile('reference.fa') as ref:
seq = ref.fetch('chr1', 999, 2000) # 0-based
print(seq)
Get Reference Lengths
python
with pysam.FastaFile('reference.fa') as ref:
for name in ref.references:
length = ref.get_reference_length(name)
print(f'{name}: {length:,} bp')
Fetch All Chromosomes
python
with pysam.FastaFile('reference.fa') as ref:
for chrom in ref.references:
seq = ref.fetch(chrom)
print(f'>{chrom}')
print(seq[:100] + '...')
Generate Simple Consensus
python
import pysam
from collections import Counter
def consensus_at_position(bam, chrom, pos):
bases = Counter()
for pileup in bam.pileup(chrom, pos, pos + 1, truncate=True):
if pileup.pos == pos:
for read in pileup.pileups:
if not read.is_del and not read.is_refskip:
bases[read.alignment.query_sequence[read.query_position]] += 1
if bases:
return bases.most_common(1)[0][0]
return 'N'
with pysam.AlignmentFile('input.bam', 'rb') as bam:
consensus = consensus_at_position(bam, 'chr1', 1000000)
print(f'Consensus at chr1:1000000 = {consensus}')
Build Consensus Sequence
python
import pysam
from collections import Counter
def build_consensus(bam_path, chrom, start, end, min_depth=3):
consensus = []
with pysam.AlignmentFile(bam_path, 'rb') as bam:
for pileup in bam.pileup(chrom, start, end, truncate=True):
bases = Counter()
for read in pileup.pileups:
if not read.is_del and not read.is_refskip:
base = read.alignment.query_sequence[read.query_position]
bases[base] += 1
if sum(bases.values()) >= min_depth:
consensus.append(bases.most_common(1)[0][0])
else:
consensus.append('N')
return ''.join(consensus)
seq = build_consensus('input.bam', 'chr1', 1000, 2000, min_depth=5)
print(f'>{chrom}:{start}-{end}')
print(seq)
Create Dictionary Header
python
import pysam
def create_dict_header(fasta_path):
header = {'HD': {'VN': '1.6', 'SO': 'unsorted'}, 'SQ': []}
with pysam.FastaFile(fasta_path) as ref:
for name in ref.references:
length = ref.get_reference_length(name)
header['SQ'].append({'SN': name, 'LN': length})
return header
header = create_dict_header('reference.fa')
for sq in header['SQ'][:5]:
print(f'{sq["SN"]}: {sq["LN"]:,} bp')
Reference Preparation Workflow
Prepare Reference for Analysis
bash
# 1. Index FASTA for samtools/pysam
samtools faidx reference.fa
# 2. Create sequence dictionary for GATK/Picard
samtools dict reference.fa -o reference.dict
# 3. Index for BWA
bwa index reference.fa
# 4. Index for Bowtie2
bowtie2-build reference.fa reference
Check Reference Setup
bash
# Verify FAI exists
ls -la reference.fa.fai
# Verify dict exists
head reference.dict
# Test fetch
samtools faidx reference.fa chr1:1-100
Common Operations
Extract Chromosome
bash
samtools faidx reference.fa chr1 > chr1.fa
samtools faidx chr1.fa # Index the subset
Get Chromosome Sizes
bash
cut -f1,2 reference.fa.fai > chrom.sizes
Subset Reference
bash
samtools faidx reference.fa chr1 chr2 chr3 > subset.fa
samtools faidx subset.fa
Compare Consensus to Reference
bash
# Generate consensus
samtools consensus input.bam -o consensus.fa
# Align consensus back to reference
minimap2 -a reference.fa consensus.fa > comparison.sam
Quick Reference
| Task | Command |
|---|---|
| Index FASTA | samtools faidx ref.fa |
| Fetch region | samtools faidx ref.fa chr1:1-1000 |
| Create dict | samtools dict ref.fa -o ref.dict |
| Build consensus | samtools consensus in.bam -o out.fa |
| Chrom sizes | cut -f1,2 ref.fa.fai |
Related Skills
- sam-bam-basics - Reference required for CRAM
- alignment-indexing - faidx for reference access
- pileup-generation - Pileup for consensus building
- variant-calling - bcftools consensus from VCF
- sequence-io/read-sequences - Parse FASTA with Biopython
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