Bio Alignment Amplicon Clipping logo

Bio Alignment Amplicon Clipping

Organization
PKU-YuanGroup
bio-alignment-amplicon-clipping

Trim PCR primers from aligned reads in amplicon-panel BAMs using samtools ampliconclip. Use when processing SARS-CoV-2 ARTIC, hereditary cancer panels, ctDNA hot-spot panels, or any amplicon assay where primer-derived bases would falsely confirm reference at primer footprints.

Overview

PublisherPKU-YuanGroup
RepositoryOpenAI4S
Skill namebio-alignment-amplicon-clipping
Stars
622
Forks
72
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by PKU-YuanGroup on GitHub. Read the source before you install it.

Installation

Install the Bio Alignment Amplicon Clipping AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/PKU-YuanGroup/OpenAI4S/tree/main/skills/bioskills/bio-alignment-files-alignment-amplicon-clipping
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4S.git /tmp/OpenAI4S
mkdir -p .claude/skills
cp -r /tmp/OpenAI4S/skills/bioskills/bio-alignment-files-alignment-amplicon-clipping .claude/skills/bio-alignment-amplicon-clipping
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bio Alignment Amplicon Clipping in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bio Alignment Amplicon Clipping on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bio Alignment Amplicon Clipping is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Version Compatibility

Reference examples tested with: samtools 1.19+, pysam 0.22+

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.

Alignment Amplicon Clipping

"Trim primer-derived bases from amplicon BAM" -> Soft- or hard-clip the 5' primer footprint after alignment using a primer BED, then repair fixmate/MD/NM tags.

  • CLI: samtools ampliconclip -b primers.bed input.bam -o clipped.bam (since samtools 1.11)
  • Alternative: iVar trim, BAMClipper, fgbio ClipBam

Why Primer Trimming After Alignment

Amplicon panels (SARS-CoV-2 ARTIC, hereditary cancer panels, ctDNA hot-spot panels, fusion panels, 16S rRNA) use designed PCR primers for enrichment. Primer-derived bases at read 5' ends do NOT reflect biological sequence -- they reflect the primer template. Without trimming:

  • False reference confirmation at primer footprint positions.
  • Variant allele frequency suppressed at variants under primers (the primer sequence cannot capture the variant base).
  • Strand bias artifacts (primers are typically one-strand).

Standard amplicon BAMs should NEVER be processed by samtools markdup -- by design every read at a primer location is a "duplicate" by coordinate. See duplicate-handling for the assay-aware decision.

Tool Selection

ToolWhenNotes
samtools ampliconclipDefault for amplicon panels (since 1.11)Soft- or hard-clip from BED; modifies CIGAR; invalidates MD/NM
iVar trimIllumina SARS-CoV-2 / PrimalSeq route (Andersen lab)Coordinates by primer name/position; soft-clips only + quality sliding-window
BAMClipperCapture / hybrid panels with primer overlap5'-end clipping with overlap handling
fgbio ClipBamWhen read-pair coordination mattersSoft/hard-clip with mate-aware end adjustment
cutadapt (pre-alignment)Legacy / when alignment is downstreamTrims at FASTQ stage; less precise for amplicon

Soft-Clip vs Hard-Clip

Goal: Decide whether trimmed bases are kept in the BAM (reversible) or discarded (irreversible).

Approach: Soft-clip is the safe default; hard-clip only when archiving and disk is constrained.

ModeFlagWhat it doesReversible?
Soft-clip(default) / --soft-clipBases kept in SEQ; CIGAR uses S; bases not alignedYes (CIGAR can be re-extended)
Hard-clip--hard-clipBases removed from SEQ; CIGAR uses HNo (bases lost)

Soft-clip is the recommended default. Hard-clip is irreversible -- once applied, the trimmed bases cannot be recovered for re-analysis with different primer coordinates.

Basic ampliconclip Workflow

Goal: Trim primers from a coordinate-sorted, indexed amplicon BAM and produce a downstream-ready BAM.

Approach: Run ampliconclip with primer BED, choosing either --strand (5' strand-aware) or --both-ends (read-through amplicons; note --both-ends overrides --strand), then re-fixmate (CIGAR changed) and re-calmd (MD/NM tags invalidated by clip).

bash
# 1. Soft-clip primers (default; reversible). --strand clips only the designed strand.
samtools ampliconclip --strand --soft-clip \
    -b primers.bed input.bam -o clipped.bam

# 2. Re-pair tags (CIGARs changed -- mate info needs refresh)
samtools sort -n clipped.bam | \
    samtools fixmate -m - - | \
    samtools sort -o sorted.bam -

# 3. Repair MD/NM tags (invalidated by clip; required by mpileup BAQ and IGV)
samtools calmd -b sorted.bam reference.fa > clipped_final.bam
samtools index clipped_final.bam

Strand-Aware Clipping

--strand clips primer bases only on the strand the primer is designed for. Without --strand, both strands are clipped at the primer site, removing valid biological sequence on the off-strand.

Both-End Clipping

--both-ends allows clipping at both 5' and 3' positions of the read (some primers can appear at either end after alignment). Necessary for amplicon designs where reads can read through the entire amplicon. When --both-ends is set, --strand is ignored -- primer sites at both ends are clipped regardless of the BED strand column:

bash
samtools ampliconclip --both-ends --soft-clip -b primers.bed input.bam -o clipped.bam

Primer BED Format

# tab-separated, 0-based half-open like all BED
chr1   100   125   primer_1_F    +
chr1   500   525   primer_1_R    -
chr1   600   625   primer_2_F    +
chr1   1000  1025  primer_2_R    -

Tools that consume the BED: column 1-3 (region), column 6 (strand) is required for --strand. ARTIC primer schemes ship pre-built BEDs (e.g., primer.bed from artic-network/primer-schemes).

SARS-CoV-2 ARTIC Comparison

ToolApproachWhen
samtools ampliconclipSoft-clip from BED, post-alignmentGeneral amplicon panels; modern ARTIC workflows
iVar trimSoft-clip with primer-position parsing + quality trimnf-core/viralrecon; Illumina PrimalSeq route (Andersen lab)

Note: the ARTIC network's own nanopore field-bioinformatics pipeline (artic minion) trims primers with its align_trim tool, not iVar; iVar (Grubaugh et al. 2019, Genome Biol 20:8) is the Illumina/PrimalSeq route. Modern viral consensus pipelines tend to use ampliconclip then samtools consensus --config hiseq --ambig (Illumina preset) for IUPAC heterozygote handling. See reference-operations for consensus generation.

After Clipping: Required Re-Processing

Clipping invalidates several tags and CIGAR-derived fields:

FieldImpactRepair
CIGARNew S or H operations addedAutomatic from ampliconclip
MD:ZMismatch positions now wrongsamtools calmd -b in.bam ref.fa
NM:iEdit distance recomputedsamtools calmd
TLENTemplate length changes when both mates clippedsamtools fixmate -m
ms, MC:ZMate score (lowercase per SAMtags) / mate CIGARsamtools fixmate -m

A clipped BAM that bypasses fixmate + calmd causes silent failures in bcftools mpileup BAQ (which depends on MD), IGV mismatch coloring, and any tool using NM for filtering.

Why Not Markdup

Amplicon reads at primer locations are by design coordinate-degenerate -- every read mapped to the same amplicon shares the same start/end coordinates because they all come from the same primer pair. samtools markdup would mark essentially every read as a duplicate and erase the dataset. For amplicon panels:

  • WITHOUT UMIs: skip dedup entirely; rely on coverage uniformity from amplicon design.
  • WITH UMIs (deep panels): use fgbio GroupReadsByUmi -> CallMolecularConsensusReads instead of markdup. See duplicate-handling.

Common Errors

ErrorCauseSolution
MD tag mismatch after clippingcalmd not runRun samtools calmd -b clipped.bam ref.fa
Variant calls with strand bias at every amplicon endForgot --strandRe-run with strand-aware clipping
Markdup output shows ~100% duplicatesAmplicon BAM was processed with markdupRestart from raw alignment; use ampliconclip; skip markdup
Unexpected reference confirmation at primer-overlapping variantsampliconclip not runRun before variant calling

Quick Reference

TaskCommand
Soft-clip primers (strand-aware)samtools ampliconclip --strand -b primers.bed in.bam -o clipped.bam
Soft-clip primers (read-through amplicons)samtools ampliconclip --both-ends -b primers.bed in.bam -o clipped.bam
Hard-clip (irreversible)samtools ampliconclip --strand --hard-clip -b primers.bed in.bam -o clipped.bam
Repair MD/NM after clipsamtools calmd -b clipped.bam ref.fa > final.bam
Repair mate infosamtools sort -n - | samtools fixmate -m - - | samtools sort -o out.bam -

Related Skills

  • duplicate-handling - Why amplicon BAMs should not be markdup'd; UMI-aware alternatives
  • alignment-filtering - Post-clip filtering for amplicon variant calling
  • alignment-sorting - Re-sort after fixmate
  • pileup-generation - mpileup flags for amplicon (-aa -A -d 600000 -B)
  • reference-operations - Consensus generation from amplicon BAMs (samtools consensus)
  • read-qc/quality-reports - Pre-alignment adapter/quality trimming

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Bio Alignment Amplicon Clipping AI skill do?

Trim PCR primers from aligned reads in amplicon-panel BAMs using samtools ampliconclip. Use when processing SARS-CoV-2 ARTIC, hereditary cancer panels, ctDNA hot-spot panels, or any amplicon assay where primer-derived bases would falsely confirm reference at primer footprints.

Why use Bio Alignment Amplicon Clipping on TypingMind?

Because you install it once and use it with any model. Bio Alignment Amplicon Clipping is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bio Alignment Amplicon Clipping in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PKU-YuanGroup/OpenAI4S/tree/main/skills/bioskills/bio-alignment-files-alignment-amplicon-clipping. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Bio Alignment Amplicon Clipping?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bio Alignment Amplicon Clipping?

As many as you like. As long as a model supports skills, you can use Bio Alignment Amplicon Clipping with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bio Alignment Amplicon Clipping AI skill free?

Yes. It is published on GitHub by PKU-YuanGroup under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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