Bio Reference Operations

Version Compatibility

Reference examples tested with: GATK 4.5+, bcftools 1.19+, pysam 0.22+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Reference Operations

Generate consensus sequences and manage reference files using samtools.

"Prepare a reference genome" → Index the FASTA and create a sequence dictionary for downstream tools.

• CLI: samtools faidx ref.fa + samtools dict ref.fa -o ref.dict
• Python: pysam.FastaFile('ref.fa') (auto-uses .fai index)

"Build a consensus from BAM" → Derive the most-supported base at each position from aligned reads.

• CLI: samtools consensus input.bam -o consensus.fa
• Python: iterate pileup columns and take majority base (pysam)

samtools faidx - Index Reference FASTA

Create index for random access to reference sequences.

Create Index

samtools faidx reference.fa
# Creates reference.fa.fai

Fetch Region from Reference

samtools faidx reference.fa chr1:1000-2000

Fetch Multiple Regions

samtools faidx reference.fa chr1:1000-2000 chr2:3000-4000

Fetch Entire Chromosome

samtools faidx reference.fa chr1

Output to File

samtools faidx reference.fa chr1:1000-2000 > region.fa

Reverse Complement

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

samtools dict reference.fa -o reference.dict

With Assembly Info

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

The M5: (MD5) tag is the only definitive reference-identity check -- two references named "GRCh38" with different decoy/alt content have different M5s. CRAM enforces M5 match on read-back. See alignment-validation for BAM-vs-reference M5 cross-check.

GRCh38 Is Not One Reference

Mixing no-alt and ALT-aware BAMs in one cohort produces inconsistent multi-mapping behavior at HLA, KIR, and segmental-duplication regions. Standardize before joint calling.

Contig Naming: The Silent Killer

A BAM with @SQ SN:chr1 cannot be analyzed against a 1-named reference (and vice versa). Detect:

samtools view -H sample.bam | grep '^@SQ' | head -3
samtools dict ref.fa | head -3

Convert: bcftools annotate --rename-chrs for VCF; for BAM there is no clean conversion -- re-align.

samtools consensus - Generate Consensus

Create consensus sequence from alignments.

Basic Consensus

samtools consensus input.bam -o consensus.fa

From Specific Region

samtools consensus -r chr1:1000-2000 input.bam -o region_consensus.fa

Output Formats

# 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

# 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

IUPAC Ambiguity for Heterozygotes

# Emit IUPAC codes (R, Y, S, W, K, M, B, D, H, V, N) for heterozygous columns
# --ambig is REQUIRED -- without it, output is restricted to A,C,G,T,N,*
samtools consensus --ambig --het-fract 0.2 --call-fract 0.5 input.bam -o consensus.fa

--het-fract controls the fraction of the second-most-common base relative to the most common required to call a heterozygote (default ~0.15). Without --ambig, columns where the second base passes --het-fract resolve to N rather than the IUPAC code. --show-ins / --show-del control insertion / deletion display, not ambiguity.

Platform-Aware Consensus

# Default: Bayesian Illumina profile
samtools consensus -f fasta input.bam -o consensus.fa

# Platform-specific profiles (samtools 1.21+; verify via samtools consensus --help for installed version)
samtools consensus --config hifi    input.bam -o consensus.fa   # PacBio HiFi
samtools consensus --config ont     input.bam -o consensus.fa   # ONT R10.4+
samtools consensus --config ultima  input.bam -o consensus.fa   # Ultima Genomics
samtools consensus --config illumina input.bam -o consensus.fa  # default

# Report ref base where consensus unavailable (low coverage)
samtools consensus -T ref.fa input.bam -o consensus.fa

samtools consensus vs bcftools consensus

Different operations -- conflating them produces nonsense:

For viral consensus from BAM:

# Modern: samtools consensus
samtools consensus --config illumina -d 10 --het-fract 0.5 \
    --show-ins yes --show-del yes input.bam -o consensus.fa

# Apply called variants to reference (different question)
bcftools consensus -f reference.fa variants.vcf.gz -o sample_consensus.fa
bcftools consensus -f reference.fa -H 1 phased.vcf.gz -o haplotype1.fa   # phased haplotype 1

For bacterial / phage assembly polishing, prefer Pilon (short-read) or medaka (ONT); samtools consensus is not iterative.

pysam Python Alternative

Fetch from Indexed FASTA

import pysam

with pysam.FastaFile('reference.fa') as ref:
    seq = ref.fetch('chr1', 999, 2000)  # 0-based
    print(seq)

Get Reference Lengths

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

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

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 (Pedagogical Only)

The Python majority-vote consensus below is illustrative, NOT production. samtools consensus is Bayesian, quality-aware, and platform-aware; majority vote ignores base qualities and produces wrong calls on low-coverage / low-quality regions. Use for teaching pileup iteration mechanics; use samtools consensus for any real consensus.

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)

Create Dictionary Header

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

Goal: Set up a reference genome with all indices needed by common analysis tools.

Approach: Create FASTA index (.fai), sequence dictionary (.dict), and aligner-specific indices in sequence.

Prepare Reference for Analysis

# 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. Pre-populate CRAM REF_CACHE (for offline HPC nodes)
seq_cache_populate.pl -root $REF_CACHE_DIR reference.fa

For aligner-specific indices (BWA, Bowtie2, STAR, minimap2, Salmon), see read-alignment.

Check Reference Setup

# 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

samtools faidx reference.fa chr1 > chr1.fa
samtools faidx chr1.fa  # Index the subset

Get Chromosome Sizes

cut -f1,2 reference.fa.fai > chrom.sizes

Subset Reference

samtools faidx reference.fa chr1 chr2 chr3 > subset.fa
samtools faidx subset.fa

Compare Consensus to Reference

# Generate consensus
samtools consensus input.bam -o consensus.fa

# Align consensus back to reference
minimap2 -a reference.fa consensus.fa > comparison.sam

Quick Reference

Related Skills

• sam-bam-basics - CRAM reference resolution (REF_PATH, REF_CACHE)
• alignment-indexing - faidx for reference access
• alignment-validation - BAM-vs-reference M5 cross-validation
• pileup-generation - Pileup for consensus building
• variant-calling/vcf-basics - VCF I/O for bcftools consensus
• variant-calling/consensus-sequences - Consensus from VCF (different operation)
• read-alignment/bwa-alignment - BWA index preparation
• sequence-io/read-sequences - Parse FASTA with Biopython