bio-pileup-generation

SkillAI & models

Generate pileup data for variant calling using samtools mpileup and pysam. Use when preparing data for variant calling, analyzing per-position read data, or calculating allele frequencies.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the bio-pileup-generation skill

What this skill tells your AI

The instructions your AI receives, as published by pku-yuangroup/openai4s in skills/bioskills/bio-alignment-files-pileup-generation/SKILL.md and read by ahel’s review.

Version Compatibility

Reference examples tested with: 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.

Pileup Generation

Generate pileup data for variant calling and position-level analysis.

"Generate pileup from BAM" -> Produce per-position read summaries showing depth, bases, and qualities.

  • CLI: samtools mpileup -f ref.fa input.bam
  • Python: bam.pileup(chrom, start, end) (pysam)

"Count alleles at a position" -> Extract per-base read support at a specific genomic coordinate.

  • Python: iterate pileup_column.pileups and count bases (pysam)

What is Pileup?

Pileup shows all reads covering each position in the reference, used for:

  • Variant calling (with bcftools)
  • Coverage analysis
  • Allele frequency calculation
  • SNP/indel detection

samtools mpileup vs bcftools mpileup (Deprecation)

samtools mpileup -g/-u (BCF output for variant calling) was deprecated in samtools 1.9 and removed in 1.15 (the option no longer exists; the usage/manpage directs users to bcftools mpileup) -- the genotype-likelihood code now lives in bcftools mpileup, which keeps mpileup logic versioned alongside bcftools call and avoids version-skew bugs.

Use caseRecommended tool
Quick allele counts at known sitessamtools mpileup or pysam pileup
Germline variant calling (small genomes, simple cohorts)bcftools mpileup -> bcftools call
Germline WGS / WES productionDeepVariant or HaplotypeCaller (not mpileup)
Somatic SNV/indelMutect2 / VarDict / VarScan2 (direct from BAM)
Long-read small variantsclair3 / DeepVariant ONT (direct from BAM)
Long-read SVSniffles / cuteSV (direct from BAM)
Ultra-low-frequency (ctDNA / MRD)fgbio consensus -> bcftools call or hot-spot Mutect2
Per-position allele counts (custom)pysam pileup

samtools mpileup (without -g) is still the standard tool for human-readable per-position read summaries.

Basic Pileup

samtools mpileup -f reference.fa input.bam > pileup.txt

Pileup Specific Region

samtools mpileup -f reference.fa -r chr1:1000000-2000000 input.bam

Regions from BED

samtools mpileup -f reference.fa -l targets.bed input.bam

Multiple BAM Files

samtools mpileup -f reference.fa sample1.bam sample2.bam sample3.bam > pileup.txt

Output Format

Text pileup format (6 columns per sample):

chr1    1000    A    15    ...............    FFFFFFFFFFF
chr1    1001    T    12    ............      FFFFFFFFFFFF
ColumnDescription
1Chromosome
2Position (1-based)
3Reference base
4Read depth
5Read bases
6Base qualities

Read Bases Encoding

SymbolMeaning
.Match on forward strand
,Match on reverse strand
ACGTMismatch (uppercase = forward)
acgtMismatch (lowercase = reverse)
^QStart of read (Q = MAPQ as ASCII)
$End of read
+NNNInsertion of N bases
-NNNDeletion of N bases
*Deleted base
> / <Reference skip (intron)

Quality Filtering Options

Minimum Mapping Quality

samtools mpileup -f reference.fa -q 20 input.bam

Minimum Base Quality

samtools mpileup -f reference.fa -Q 20 input.bam

Combined Quality Filters

samtools mpileup -f reference.fa -q 20 -Q 20 input.bam

Maximum Depth (Critical Trap)

# samtools mpileup default -d 8000 silently truncates targeted / mt-DNA / amplicon / UMI-deduped data
# bcftools mpileup default -d 250 is far lower; both must be set explicitly when piping
samtools mpileup -f reference.fa -d 0 input.bam        # no cap
samtools mpileup -f reference.fa -d 1000000 input.bam  # explicit high cap

# WRONG -- samtools 8000 cap, then bcftools 250 cap re-applied
samtools mpileup -f ref.fa in.bam | bcftools call -mv

# RIGHT -- single tool, explicit -d
bcftools mpileup -d 1000000 -f ref.fa in.bam | bcftools call -mv

BAQ: Base Alignment Quality (Critical Default)

When -f ref.fa is passed, BAQ is enabled by default. BAQ Phred-scales the probability that a base is misaligned (HMM realignment over a small window) and reduces base quality near indels. Tradeoffs: ~30% slower; suppresses FP SNVs near indels; hurts indel detection sensitivity.

FlagBehavior
(default with -f)BAQ on (computed from CIGAR if MD missing)
-B / --no-BAQDisable BAQ -- raw qualities
-E / --redo-BAQForce recompute (after BQSR; if MD stale)

BAQ ON for: short-read germline SNV (BWA, Bowtie2, HISAT2), short-read somatic SNV.

BAQ OFF (-B) for: long-read variant calling (ONT, PacBio HiFi), SV calling, RNA-seq near splice junctions, viral / amplicon, ultra-deep ctDNA from consensus reads (consensus quality already inflated), aDNA (qualities pre-rescaled by mapDamage).

-A (count anomalous read pairs / orphans) is required for amplicon -- amplicon reads are by design not properly paired.

-aa (output all positions, including zero-coverage) is required for ARTIC SARS-CoV-2 consensus generation.

Library-Typed Flags Cheat Sheet

LibraryFlags
Short-read germline WGS (BWA)-q 20 -Q 20 -d 0 (BAQ on default)
Short-read tumor WGS-q 1 -Q 13 -d 0 -B (low MAPQ kept; BAQ off)
Amplicon viral (ARTIC)-aa -A -d 600000 -B -Q 20
Capture / exome-q 20 -Q 20 -d 250
Long-read ONT R10.4+-q 30 -Q 0 -B -d 0; for bcftools mpileup add --max-BQ 30 (its ont preset value)
PacBio HiFi-q 20 -Q 0 -B -d 0
RNA-seq variants-q 20 -Q 20 -B -d 0
Forensic / aDNA-q 0 -Q 0 -A -d 0 -B

Variant Calling Pipeline (Modern: bcftools mpileup)

Goal: Call variants from alignment data using the pileup-based approach.

Approach: Use bcftools mpileup (not samtools mpileup -g) so genotype-likelihood code is co-versioned with bcftools call. Apply quality and depth caps explicitly; annotate FORMAT fields needed for downstream filtering.

Modern Germline Calling

bcftools mpileup -f reference.fa -d 1000000 -q 20 -Q 20 \
    --annotate FORMAT/AD,FORMAT/DP,FORMAT/SP,INFO/AD \
    input.bam | \
  bcftools call -mv -Oz -o variants.vcf.gz
bcftools index -t variants.vcf.gz

Multi-Sample Joint Calling

bcftools mpileup -f reference.fa --threads 4 -d 250 -q 20 -Q 20 \
    -a FORMAT/AD,FORMAT/DP s1.bam s2.bam s3.bam | \
  bcftools call -mv --threads 4 -Oz -o joint.vcf.gz

For somatic / low-VAF, prefer Mutect2 / Strelka2 / DeepVariant -- materially better than mpileup-based callers.

Overlap Detection Defaults

When fragment length < 2 * read_length, R1 and R2 overlap. Both samtools mpileup and bcftools mpileup enable overlap detection by default (per samtools-mpileup(1)) and count overlapping bases once; pass -x to disable (long form is --disable-overlap-removal in samtools since 1.16, but --ignore-overlaps in bcftools). Disabling overlap correction can inflate somatic VAFs at sites covered by overlapping pairs (especially in cfDNA / FFPE).

pysam Python Alternative

Basic Pileup

import pysam

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for pileup_column in bam.pileup('chr1', 1000000, 1001000):
        print(f'{pileup_column.reference_name}:{pileup_column.pos} depth={pileup_column.n}')

Access Reads at Position

import pysam

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for pileup_column in bam.pileup('chr1', 1000000, 1000001, truncate=True):
        print(f'Position: {pileup_column.pos}')
        print(f'Depth: {pileup_column.n}')

        for pileup_read in pileup_column.pileups:
            if pileup_read.is_del:
                print('  Deletion')
            elif pileup_read.is_refskip:
                print('  Reference skip')
            else:
                qpos = pileup_read.query_position
                base = pileup_read.alignment.query_sequence[qpos]
                qual = pileup_read.alignment.query_qualities[qpos]
                print(f'  {base} (Q{qual})')

Count Alleles at Position

import pysam
from collections import Counter

def allele_counts(bam_path, chrom, pos):
    counts = Counter()

    with pysam.AlignmentFile(bam_path, 'rb') as bam:
        for pileup_column in bam.pileup(chrom, pos, pos + 1, truncate=True):
            if pileup_column.pos != pos:
                continue

            for pileup_read in pileup_column.pileups:
                if pileup_read.is_del:
                    counts['DEL'] += 1
                elif pileup_read.is_refskip:
                    continue
                else:
                    qpos = pileup_read.query_position
                    base = pileup_read.alignment.query_sequence[qpos]
                    counts[base.upper()] += 1

    return dict(counts)

counts = allele_counts('input.bam', 'chr1', 1000000)
print(counts)  # {'A': 45, 'G': 5}

Calculate Allele Frequency

import pysam
from collections import Counter

def allele_frequency(bam_path, chrom, pos, min_qual=20):
    counts = Counter()

    with pysam.AlignmentFile(bam_path, 'rb') as bam:
        for pileup_column in bam.pileup(chrom, pos, pos + 1, truncate=True,
                                         min_base_quality=min_qual):
            if pileup_column.pos != pos:
                continue

            for pileup_read in pileup_column.pileups:
                if pileup_read.is_del or pileup_read.is_refskip:
                    continue
                qpos = pileup_read.query_position
                base = pileup_read.alignment.query_sequence[qpos]
                counts[base.upper()] += 1

    total = sum(counts.values())
    if total == 0:
        return {}

    return {base: count / total for base, count in counts.items()}

freq = allele_frequency('input.bam', 'chr1', 1000000)
for base, f in sorted(freq.items(), key=lambda x: -x[1]):
    print(f'{base}: {f:.1%}')

Pileup with Quality Filtering

import pysam

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for pileup_column in bam.pileup('chr1', 1000000, 1001000,
                                     truncate=True,
                                     min_mapping_quality=20,
                                     min_base_quality=20):
        print(f'{pileup_column.pos}: {pileup_column.n}')

Generate Pileup Text

import pysam

def pileup_text(bam_path, ref_path, chrom, start, end):
    with pysam.AlignmentFile(bam_path, 'rb') as bam:
        with pysam.FastaFile(ref_path) as ref:
            for pileup_column in bam.pileup(chrom, start, end, truncate=True):
                pos = pileup_column.pos
                ref_base = ref.fetch(chrom, pos, pos + 1)
                depth = pileup_column.n

                bases = []
                for pileup_read in pileup_column.pileups:
                    if pileup_read.is_del:
                        bases.append('*')
                    elif pileup_read.is_refskip:
                        bases.append('>')
                    else:
                        qpos = pileup_read.query_position
                        base = pileup_read.alignment.query_sequence[qpos]
                        if base.upper() == ref_base.upper():
                            bases.append('.' if not pileup_read.alignment.is_reverse else ',')
                        else:
                            bases.append(base.upper() if not pileup_read.alignment.is_reverse else base.lower())

                print(f'{chrom}\t{pos+1}\t{ref_base}\t{depth}\t{"".join(bases)}')

pileup_text('input.bam', 'reference.fa', 'chr1', 1000000, 1000100)

Pileup Options Summary

OptionDescriptionCommon pitfall
-f FILEReference FASTATriggers BAQ ON by default
-r REGIONRestrict to region
-l FILEBED file of regions
-q INTMin mapping qualityAligner-dependent semantics
-Q INTMin base quality-Q 0 with default overlap detection has subtle behavior
-d INTMax depthDefault 8000 silently truncates; bcftools mpileup default is 250
-BDisable BAQOften correct for long reads, SV, viral, consensus
-ACount anomalous pairsRequired for amplicon
-aaOutput all positionsRequired for consensus generation
-x (--disable-overlap-removal; bcftools: --ignore-overlaps)Disable mate-overlap correctionRarely correct
--max-BQ INT (bcftools mpileup only)Cap baseQ/BAQ (default 60)Not a samtools mpileup option; useful for ONT/HiFi (Q values inflated)
-g (REMOVED in 1.15)Old BCF outputUse bcftools mpileup instead

Quick Reference

TaskCommand
Basic pileupsamtools mpileup -f ref.fa in.bam
Quality filtersamtools mpileup -f ref.fa -q 20 -Q 20 in.bam
Regionsamtools mpileup -f ref.fa -r chr1:1-1000 in.bam
To bcftoolsbcftools mpileup -f ref.fa -d 1000000 in.bam | bcftools call -mv

Common Errors

ErrorCauseSolution
No FASTA referenceMissing -f optionAdd -f reference.fa
Reference mismatchWrong referenceUse same reference as alignment
Out of memoryHigh coverage regionUse -d to cap depth

Related Skills

  • alignment-filtering - Filter BAM before pileup
  • reference-operations - Index reference for pileup; M5 cross-check
  • bam-statistics - mosdepth, depth tool selection
  • variant-calling/variant-calling - Full variant calling workflows
  • variant-calling/vcf-basics - VCF/BCF I/O
  • variant-calling/joint-calling - Multi-sample joint calling

Signals

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Last commit
Sep 2026
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skill
Gateway key
bio-pileup-generation
Source
github.com/pku-yuangroup/openai4s