Agent skill

Bwa Mem2 Dna Aligner

by jaechang-hits in jaechang-hits/SciAgent-Skills

Fast short-read DNA aligner for WGS/WES/ChIP-seq. An agent skill from jaechang-hits/SciAgent-Skills.

MITAuto-check passedResearch & Science

Install Bwa Mem2 Dna Aligner

skills CLI
$ npx skills add jaechang-hits/SciAgent-Skills --skill bwa-mem2-dna-aligner -a claude-code

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

GitHub CLI
$ gh skill install jaechang-hits/SciAgent-Skills bwa-mem2-dna-aligner --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/jaechang-hits/SciAgent-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/genomics-bioinformatics/alignment/bwa-mem2-dna-aligner .claude/skills/bwa-mem2-dna-aligner && 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
bwa-mem2-dna-aligner
GitHub stars
374
Used in
1 other repo
Token cost
~3.5k tokens
SKILL.md length
806 words
Files
1
Skills in repo
169
Repo updated
First seen
Licence
MIT

At a glance

Fast short-read DNA aligner for WGS/WES/ChIP-seq. An agent skill from jaechang-hits/SciAgent-Skills.

  • Works in 6 steps: Download Reference Genome → Build BWA-MEM2 Index → Align Paired-End Reads → …
  • Tasks that involve Bioinformatics
  • SKILL.md covers Overview, When to Use, Prerequisites and Pre-flight Interview, plus 7 more sections
  • Calls wget, conda and python3; reaches github.com and ftp.ncbi.nlm.nih.gov

What it does

Bwa Mem2 Dna Aligner is an agent skill from jaechang-hits/SciAgent-Skills. Fast short-read DNA aligner for WGS/WES/ChIP-seq. 2× faster BWA-MEM successor; outputs SAM/BAM with read group headers for GATK. Primary plus supplementary records for chimeric reads. Use STAR for RNA-seq splice-aware alignment; Bowtie2 is a comparable alternative.

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: 197 bioinformatics & life science skills for Claude Code and AI agents — BixBench 92.0% accuracy. RNA-seq, single-cell, drug discovery, proteomics, and more. Powers OmicsHorizon. The licence is MIT.

When your agent uses it

  • Tasks that involve Bioinformatics

Example prompts

  • “/bwa-mem2-dna-aligner”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Download Reference Genome
  2. Build BWA-MEM2 Index
  3. Align Paired-End Reads
  4. Mark PCR Duplicates
  5. Assess Alignment Quality
  6. Complete WGS/WES Pipeline → Variant Calling

What it can do on your machine

Read from SKILL.md and the folder at commit 82c862c. 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

    Shell commands in SKILL.md call:

    • wget
    • conda
    • python3

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • ftp.ncbi.nlm.nih.gov
    • ftp.ebi.ac.uk

    Also links to:

    • doi.org
    • gatk.broadinstitute.org

    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

Bwa Mem2 Dna Aligner loads about 3.5k tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 806 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k

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 jaechang-hits/SciAgent-Skills at commit 82c862c, republished under its MIT licence (© jaechang-hits). 806 words, ~3,487 tokens.

Download SKILL.mdSave it as .claude/skills/bwa-mem2-dna-aligner/SKILL.md (or your agent's skills folder).
name
bwa-mem2-dna-aligner
description
Fast short-read DNA aligner for WGS/WES/ChIP-seq. 2× faster BWA-MEM successor; outputs SAM/BAM with read group headers for GATK. Primary plus supplementary records for chimeric reads. Use STAR for RNA-seq splice-aware alignment; Bowtie2 is a comparable alternative.
license
MIT

BWA-MEM2 — DNA Short-Read Aligner

Overview

BWA-MEM2 aligns short DNA reads (Illumina, 50–250 bp) to a reference genome using the BWT-FM index. It is the standard aligner for whole-genome sequencing (WGS), whole-exome sequencing (WES), ChIP-seq, and ATAC-seq DNA alignment. BWA-MEM2 is 2× faster than the original BWA-MEM while producing identical results. It outputs SAM format with proper read group (@RG) headers required by GATK HaplotypeCaller and Picard tools. For paired-end reads, it marks proper pairs and resolves chimeric/split reads into supplementary alignments.

When to Use

  • Aligning WGS or WES Illumina reads to a reference genome for variant calling (SNP, indel, SV)
  • ChIP-seq or ATAC-seq DNA alignment to produce BAM files for peak calling with MACS3
  • Producing GATK-compatible BAM files with @RG read group tags
  • Aligning reads ≥ 50 bp; for shorter reads (< 50 bp), BWA-backtrack may be more appropriate
  • Re-aligning legacy FASTQ files to an updated reference genome assembly
  • Use STAR instead for RNA-seq reads that span splice junctions
  • Use Bowtie2 as an alternative for local alignment or when index size must be minimized

Prerequisites

  • Software: bwa-mem2 (conda or pre-compiled binary), samtools
  • Reference: genome FASTA (e.g., GRCh38, hg19, mm10)
  • RAM: ~28 GB for human genome index; 6–8 GB for mouse

Check before installing: The tool may already be available in the current environment (e.g., inside a pixi / conda env). Run command -v bwa-mem2 first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via pixi run bwa-mem2 rather than bare bwa-mem2.

bash
# Install with conda (recommended)
conda install -c bioconda bwa-mem2 samtools

# Or download pre-compiled binary
wget https://github.com/bwa-mem2/bwa-mem2/releases/download/v2.2.1/bwa-mem2-2.2.1_x64-linux.tar.bz2
tar -jxf bwa-mem2-2.2.1_x64-linux.tar.bz2
export PATH="$PWD/bwa-mem2-2.2.1_x64-linux:$PATH"

# Verify
bwa-mem2 version
# 2.2.1

Pre-flight Interview

Settle these with the user before writing any analysis code.

yaml
decisions:
  - id: D1
    param: reference_bundle
    kind: required
    source: user
    ask: "Which species and genome assembly should reads be aligned against?"
    default: null

  - id: D2
    param: read_group
    kind: required
    source: user
    ask: "What sample name and library identity should be stamped into each alignment?"
    default: null

  - id: D3
    param: downstream_compatibility
    kind: derived
    source: upstream
    ask: "Will GATK or Picard consume this BAM, requiring supplementary alignments flagged and soft-clipped?"
    default: "-M -Y when a GATK/Picard step follows"

  - id: D4
    param: seedLength
    kind: optional
    source: user
    ask: "Do these reads need a shorter seed than the default to map sensitively?"
    default: 19

  - id: D5
    param: minAlignmentScore
    kind: optional
    source: user
    ask: "How weak an alignment is still worth reporting?"
    default: 30

  - id: D6
    param: interleavedInput
    kind: derived
    source: data
    ask: "Are both mates held in a single interleaved FASTQ?"
    default: false
    skip_if: "reads supplied as separate R1/R2 files"

  - id: D7
    param: threads
    kind: never_ask
    source: data
    reason: "Affects runtime only, not the alignments"
    default: "min(8, available_cores)"

D2 is not cosmetic. The sample name in the read group is what joint genotyping groups by; an omitted or duplicated one silently merges or splits samples downstream, long after the alignment itself looks fine.

Quick Start

bash
# 1. Build genome index (~30 min, run once)
bwa-mem2 index GRCh38.fa

# 2. Align paired-end reads and sort
bwa-mem2 mem -t 16 -R "@RG\tID:sample1\tSM:sample1\tPL:ILLUMINA" \
    GRCh38.fa sample1_R1.fastq.gz sample1_R2.fastq.gz \
    | samtools sort -@ 8 -o sample1.sorted.bam

# 3. Index the BAM
samtools index sample1.sorted.bam
echo "Aligned reads: $(samtools view -c -F 4 sample1.sorted.bam)"

Workflow

Step 1: Download Reference Genome

Obtain the reference genome FASTA file matching the target assembly.

bash
# Download GRCh38 primary assembly (human)
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz
gunzip GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz
mv GCA_000001405.15_GRCh38_no_alt_analysis_set.fna GRCh38.fa

# Or use ENSEMBL/GENCODE
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/GRCh38.primary_assembly.genome.fa.gz
gunzip GRCh38.primary_assembly.genome.fa.gz

echo "Reference size: $(du -sh GRCh38.fa)"
Step 2: Build BWA-MEM2 Index

Index the reference genome — required once per genome, takes ~25-35 min for human.

bash
# Build index (~28 GB RAM required for human genome)
bwa-mem2 index GRCh38.fa

# This creates: GRCh38.fa.0123, GRCh38.fa.amb, GRCh38.fa.ann,
#               GRCh38.fa.bwt.2bit.64, GRCh38.fa.pac
echo "Index files: $(ls GRCh38.fa.* | wc -l) created"
ls -lh GRCh38.fa.*
Step 3: Align Paired-End Reads

Align FASTQ reads with a read group header required for GATK compatibility.

bash
# Align with read group (required for GATK)
# @RG fields: ID (run ID), SM (sample name), PL (platform), LB (library), PU (flowcell)
bwa-mem2 mem \
    -t 16 \
    -R "@RG\tID:sample1_run1\tSM:sample1\tPL:ILLUMINA\tLB:lib1\tPU:flowcell1" \
    GRCh38.fa \
    sample1_R1.fastq.gz \
    sample1_R2.fastq.gz \
    | samtools sort -@ 8 -m 2G -o sample1.sorted.bam

samtools index sample1.sorted.bam
echo "Alignment complete."
echo "Total reads:   $(samtools view -c sample1.sorted.bam)"
echo "Mapped reads:  $(samtools view -c -F 4 sample1.sorted.bam)"
Step 4: Mark PCR Duplicates

Remove or mark optical and PCR duplicates before variant calling.

bash
# Option A: samtools markdup (fast)
samtools fixmate -m sample1.sorted.bam sample1.fixmate.bam
samtools sort -@ 8 -o sample1.fixmate.sorted.bam sample1.fixmate.bam
samtools markdup -@ 8 sample1.fixmate.sorted.bam sample1.markdup.bam
samtools index sample1.markdup.bam

echo "Duplication rate:"
samtools flagstat sample1.markdup.bam | grep "duplicate"

# Option B: Picard MarkDuplicates (GATK best practices)
picard MarkDuplicates \
    INPUT=sample1.sorted.bam \
    OUTPUT=sample1.markdup.bam \
    METRICS_FILE=sample1.dupmetrics.txt \
    REMOVE_DUPLICATES=false \
    CREATE_INDEX=true
cat sample1.dupmetrics.txt | grep -A2 "ESTIMATED"
Step 5: Assess Alignment Quality

Generate alignment statistics and check key quality metrics.

bash
# Full alignment statistics
samtools flagstat sample1.markdup.bam > sample1.flagstat.txt
cat sample1.flagstat.txt

# Coverage statistics
samtools coverage sample1.markdup.bam | head -30

# Parse key metrics with Python
python3 - << 'EOF'
from pathlib import Path

flagstat = Path("sample1.flagstat.txt").read_text()
for line in flagstat.splitlines():
    if any(kw in line for kw in ["total", "mapped", "properly paired", "duplicate"]):
        print(line)
EOF
Step 6: Complete WGS/WES Pipeline → Variant Calling

Pipe BWA-MEM2 output directly into GATK HaplotypeCaller.

bash
#!/bin/bash
# Complete WGS alignment → variant calling pipeline
GENOME="GRCh38.fa"
SAMPLE="sample1"
R1="data/${SAMPLE}_R1.fastq.gz"
R2="data/${SAMPLE}_R2.fastq.gz"
THREADS=16
OUTDIR="results/${SAMPLE}"
mkdir -p "$OUTDIR"

# Step 1: Align + sort
bwa-mem2 mem -t $THREADS \
    -R "@RG\tID:${SAMPLE}\tSM:${SAMPLE}\tPL:ILLUMINA\tLB:lib1\tPU:run1" \
    $GENOME $R1 $R2 \
    | samtools sort -@ 8 -o $OUTDIR/${SAMPLE}.sorted.bam
samtools index $OUTDIR/${SAMPLE}.sorted.bam

# Step 2: Mark duplicates
samtools fixmate -m $OUTDIR/${SAMPLE}.sorted.bam - \
    | samtools sort -@ 8 \
    | samtools markdup -@ 8 - $OUTDIR/${SAMPLE}.markdup.bam
samtools index $OUTDIR/${SAMPLE}.markdup.bam

# Step 3: GATK variant calling
gatk HaplotypeCaller \
    -R $GENOME \
    -I $OUTDIR/${SAMPLE}.markdup.bam \
    -O $OUTDIR/${SAMPLE}.g.vcf.gz \
    -ERC GVCF \
    --native-pair-hmm-threads 4

echo "Pipeline complete: $OUTDIR/${SAMPLE}.g.vcf.gz"

Key Parameters

ParameterDefaultRange/OptionsEffect
-t11–64CPU threads; use 8–16 for production runs
-R—@RG\tID:...\tSM:...Read group string; required for GATK compatibility
-k1910–28Minimum seed length; lower = more sensitive for shorter reads
-w10050–500Band width for Smith-Waterman alignment
-MoffflagMark split/supplementary reads as secondary (BWA-MEM style); needed for Picard compatibility
-aoffflagOutput all alignments for single-end reads (for seeding; increases file size)
-poffflagTreat input as interleaved paired-end FASTQ
-c500100–10000Skip alignment for MEM count > threshold (reduces multi-mapper noise)
-T3020–60Minimum alignment score threshold; lower = report more low-quality alignments
-YoffflagUse soft clipping for supplementary alignments (recommended for GATK)
Show full SKILL.md (296 more words)Show less

Common Recipes

Recipe 1: Batch Align Multiple Samples
bash
#!/bin/bash
# Align all samples in parallel using GNU parallel or sequential loop
GENOME="GRCh38.fa"
SAMPLES=(ctrl_1 ctrl_2 treat_1 treat_2)
THREADS=12

for sample in "${SAMPLES[@]}"; do
    echo "=== Aligning $sample ==="
    bwa-mem2 mem -t $THREADS \
        -R "@RG\tID:${sample}\tSM:${sample}\tPL:ILLUMINA\tLB:lib1\tPU:run1" \
        $GENOME \
        data/${sample}_R1.fastq.gz \
        data/${sample}_R2.fastq.gz \
        | samtools sort -@ 4 -m 2G -o results/${sample}.sorted.bam
    samtools index results/${sample}.sorted.bam
    
    MAPPED=$(samtools view -c -F 4 results/${sample}.sorted.bam)
    TOTAL=$(samtools view -c results/${sample}.sorted.bam)
    echo "$sample: $MAPPED / $TOTAL reads mapped"
done
Recipe 2: Parse Alignment Metrics with Python
python
import subprocess
import pandas as pd
from pathlib import Path

samples = ["ctrl_1", "ctrl_2", "treat_1", "treat_2"]
metrics = []

for sample in samples:
    bam = f"results/{sample}.sorted.bam"
    result = subprocess.run(
        ["samtools", "flagstat", bam], capture_output=True, text=True
    )
    stats = {}
    for line in result.stdout.splitlines():
        if "total" in line:
            stats["total"] = int(line.split()[0])
        elif "mapped" in line and "%" in line:
            stats["mapped"] = int(line.split()[0])
            stats["pct_mapped"] = float(line.split("(")[1].split("%")[0])
        elif "properly paired" in line:
            stats["properly_paired"] = int(line.split()[0])
    stats["sample"] = sample
    metrics.append(stats)

df = pd.DataFrame(metrics).set_index("sample")
print(df[["total", "mapped", "pct_mapped", "properly_paired"]])
df.to_csv("alignment_metrics.tsv", sep="\t")

Expected Outputs

OutputFormatDescription
*.sorted.bamBAMCoordinate-sorted aligned reads; index with samtools index
*.sorted.bam.baiBAIBAM index; required for random access by GATK and IGV
*.flagstat.txtTextAlignment summary: total/mapped/paired/duplicate counts and percentages
*.markdup.bamBAMDuplicate-marked BAM; use as input to GATK HaplotypeCaller
*.dupmetrics.txtTextPicard duplication metrics with estimated library size

Troubleshooting

ProblemCauseSolution
Low mapping rate (< 85%)Genome mismatch, contamination, or low quality readsVerify genome assembly matches sample; run FastQC; trim adapters with Trim Galore
@RG header missing error in GATK-R flag not specified during alignmentRe-align with -R "@RG\tID:...\tSM:...\tPL:ILLUMINA"
Out of memory during indexingInsufficient RAM for genome indexBWA-MEM2 requires ~28 GB for human; use classic bwa with less RAM if needed
Unbalanced paired-end countsInterleaved FASTQ or file mismatchAdd -p for interleaved; verify R1/R2 read counts with zcat r1.fq.gz | wc -l
[E::bwa_idx_load_from_disk] errorIndex files missing or wrong prefixRe-run bwa-mem2 index genome.fa; ensure all .0123, .bwt.2bit.64 files exist
Slow alignment speedLow thread count or slow I/OUse -t 16 or more; store data on SSD; pipe directly to samtools sort
Supplementary alignments causing issuesSplit reads in downstream toolsAdd -M flag to mark split reads as secondary (Picard compatibility)
GATK base quality score recalibration failsMissing known variant VCFDownload dbSNP VCF for your genome assembly from NCBI or GATK resource bundle

References

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

Files

Just SKILL.md in skills/genomics-bioinformatics/alignment/bwa-mem2-dna-aligner of jaechang-hits/SciAgent-Skills.

Open the folder on GitHubat commit 82c862c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in jaechang-hits/SciAgent-Skills, which our catalogue first saw on October 7, 2026.

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Questions about Bwa Mem2 Dna Aligner

What does Bwa Mem2 Dna Aligner do?

Fast short-read DNA aligner for WGS/WES/ChIP-seq. An agent skill from jaechang-hits/SciAgent-Skills. Bwa Mem2 Dna Aligner is an agent skill from jaechang-hits/SciAgent-Skills. Fast short-read DNA aligner for WGS/WES/ChIP-seq.

When should I use Bwa Mem2 Dna Aligner?

Bwa Mem2 Dna Aligner fits situations like: tasks that involve Bioinformatics.

How do I install Bwa Mem2 Dna Aligner in Claude Code?

Run `npx skills add jaechang-hits/SciAgent-Skills --skill bwa-mem2-dna-aligner -a claude-code`. Or copy the skill folder (skills/genomics-bioinformatics/alignment/bwa-mem2-dna-aligner in jaechang-hits/SciAgent-Skills) into .claude/skills/bwa-mem2-dna-aligner in your project. Claude Code loads it when a task matches its description.

How do I install Bwa Mem2 Dna Aligner in Codex?

Run `npx skills add jaechang-hits/SciAgent-Skills --skill bwa-mem2-dna-aligner -a codex`. Or copy the skill folder (skills/genomics-bioinformatics/alignment/bwa-mem2-dna-aligner in jaechang-hits/SciAgent-Skills) into .agents/skills/bwa-mem2-dna-aligner in your project. Codex loads it when a task matches its description.

Can I use Bwa Mem2 Dna Aligner 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 jaechang-hits/SciAgent-Skills --skill bwa-mem2-dna-aligner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bwa-mem2-dna-aligner, .gemini/skills/bwa-mem2-dna-aligner, .github/skills/bwa-mem2-dna-aligner and .opencode/skills/bwa-mem2-dna-aligner in your project.

What does Bwa Mem2 Dna Aligner need to run?

Going by SKILL.md and its folder, Bwa Mem2 Dna Aligner needs the command-line tools its instructions call (wget, conda and python3). Our summary lists: Python 3.

Does Bwa Mem2 Dna Aligner access the network?

SKILL.md names 5 domains. In commands or code: github.com, ftp.ncbi.nlm.nih.gov and ftp.ebi.ac.uk; the agent is likely to contact these when it follows the instructions. As links in the text: doi.org and gatk.broadinstitute.org. This is read from the text; nothing was executed.

Is Bwa Mem2 Dna Aligner 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 Bwa Mem2 Dna Aligner use?

Bwa Mem2 Dna Aligner is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bwa Mem2 Dna Aligner use?

About 3.5k 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 Bwa Mem2 Dna Aligner?

Skills that share tags, products or a category with Bwa Mem2 Dna Aligner: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bwa Mem2 Dna Aligner?

jaechang-hits (a GitHub user) maintains it in jaechang-hits/SciAgent-Skills, which has 374 GitHub stars. The repository holds 169 skills in this directory. The repository was last updated on September 29, 2026.

Source: jaechang-hits/SciAgent-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.