Agent skill

Bio Pileup Generation

by GPTomics in GPTomics/bioSkills

Generate pileup data for variant calling using samtools mpileup and pysam.

MITAuto-check passedResearch & Science

Install Bio Pileup Generation

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-pileup-generation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-pileup-generation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/alignment-files/pileup-generation .claude/skills/bio-pileup-generation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-pileup-generation
GitHub stars
1.2k
Used in
2 other repos
Token cost
~3.6k tokens
SKILL.md length
1,027 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Generate pileup data for variant calling using samtools mpileup and pysam.

  • Preparing data for variant calling
  • SKILL.md covers Version Compatibility, What is Pileup?, samtools mpileup vs bcftools… and Output Format, plus 6 more sections
  • Runs Python scripts from its folder; calls pip
  • Analyzing per-position read data

What it does

Bio Pileup Generation is an agent skill from GPTomics/bioSkills. 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.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/allele_counts.py` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. It works with pysam and Python. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Preparing data for variant calling
  • Analyzing per-position read data
  • Calculating allele frequencies

Example prompts

  • “/bio-pileup-generation”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Pileup Generation loads about 3.6k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 1,027 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,027 words, ~3,620 tokens.

Download SKILL.mdSave it as .claude/skills/bio-pileup-generation/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-pileup-generation
description
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.
tool_type
cli
primary_tool
samtools

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
bash
samtools mpileup -f reference.fa input.bam > pileup.txt
Pileup Specific Region
bash
samtools mpileup -f reference.fa -r chr1:1000000-2000000 input.bam
Regions from BED
bash
samtools mpileup -f reference.fa -l targets.bed input.bam
Multiple BAM Files
bash
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
bash
samtools mpileup -f reference.fa -q 20 input.bam
Minimum Base Quality
bash
samtools mpileup -f reference.fa -Q 20 input.bam
Combined Quality Filters
bash
samtools mpileup -f reference.fa -q 20 -Q 20 input.bam
Maximum Depth (Critical Trap)
bash
# 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
Show full SKILL.md (383 more words)Show less

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
bash
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
bash
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
python
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
python
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
python
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
python
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
python
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
python
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
  • 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

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in alignment-files/pileup-generation of GPTomics/bioSkills.

  • SKILL.md
  • examples/allele_counts.py
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 9, 2026.

Compare with similar skills

Bio Pileup Generation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Bio Pileup Generation compared with similar skills
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Pysam Genomic Filesjaechang-hits/SciAgent-Skills3741 repos~5.2kAutomated safety check: PassMIT
Bio Splicing QcFreedomIntelligence/OpenClaw-Medical-Skills3.1k—~1.6kAutomated safety check: PassNone

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Works with

Questions about Bio Pileup Generation

What does Bio Pileup Generation do?

Generate pileup data for variant calling using samtools mpileup and pysam. Bio Pileup Generation is an agent skill from GPTomics/bioSkills. Generate pileup data for variant calling using samtools mpileup and pysam.

When should I use Bio Pileup Generation?

Bio Pileup Generation fits situations like: preparing data for variant calling; analyzing per-position read data; calculating allele frequencies.

How do I install Bio Pileup Generation in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-pileup-generation -a claude-code`. Or copy the skill folder (alignment-files/pileup-generation in GPTomics/bioSkills) into .claude/skills/bio-pileup-generation in your project. Claude Code loads it when a task matches its description.

How do I install Bio Pileup Generation in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-pileup-generation -a codex`. Or copy the skill folder (alignment-files/pileup-generation in GPTomics/bioSkills) into .agents/skills/bio-pileup-generation in your project. Codex loads it when a task matches its description.

Can I use Bio Pileup Generation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-pileup-generation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-pileup-generation, .gemini/skills/bio-pileup-generation, .github/skills/bio-pileup-generation and .opencode/skills/bio-pileup-generation in your project.

What does Bio Pileup Generation need to run?

Going by SKILL.md and its folder, Bio Pileup Generation needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Bio Pileup Generation access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bio Pileup Generation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Pileup Generation use?

Bio Pileup Generation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Pileup Generation use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Pileup Generation?

Skills that share tags, products or a category with Bio Pileup Generation: Pysam (K-Dense-AI/scientific-agent-skills, 48k stars), Tooluniverse Epigenomics (wu-yc/LabClaw, 1.1k stars), Samtools Bam Processing (jaechang-hits/SciAgent-Skills, 374 stars) and Pysam Genomic Files (jaechang-hits/SciAgent-Skills, 374 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Pileup Generation?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.