Agent skill

Bio Sam Bam Basics

by GPTomics in GPTomics/bioSkills

View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam.

MITAuto-check passedResearch & Science

Install Bio Sam Bam Basics

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-sam-bam-basics -a claude-code

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

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-sam-bam-basics --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/sam-bam-basics .claude/skills/bio-sam-bam-basics && 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-sam-bam-basics
GitHub stars
1.2k
Used in
2 other repos
Token cost
~3.7k tokens
SKILL.md length
1,421 words
Files
4
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam.

  • Works in 12 steps: QNAME - Read name → FLAG - Bitwise flag → RNAME - Reference name → …
  • Inspecting alignments
  • SKILL.md covers Version Compatibility, Format Overview, SAM Format Structure and samtools view, plus 9 more sections
  • Runs Shell and Python scripts from its folder; calls pip

What it does

Bio Sam Bam Basics is an agent skill from GPTomics/bioSkills. View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.

Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `examples/convert_formats.sh`, `examples/view_bam.py` and `usage-guide.md`).

It sits in Research & Science. It works with pysam. 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

  • Inspecting alignments
  • Converting between formats
  • Understanding alignment file structure

Example prompts

  • “/bio-sam-bam-basics”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

12 steps, taken from the first numbered list in SKILL.md.

  1. QNAME - Read name
  2. FLAG - Bitwise flag
  3. RNAME - Reference name
  4. POS - 1-based position
  5. MAPQ - Mapping quality
  6. CIGAR - Alignment description
  7. RNEXT - Mate reference
  8. PNEXT - Mate position
  9. TLEN - Template length
  10. SEQ - Read sequence
  11. QUAL - Base qualities
  12. Optional tags (NM:i:0, MD:Z:50, etc.)

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 (Shell and 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 Sam Bam Basics loads about 3.7k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 1,421 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k

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). 1,421 words, ~3,686 tokens.

Download SKILL.mdSave it as .claude/skills/bio-sam-bam-basics/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
bio-sam-bam-basics
description
View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.
tool_type
cli
primary_tool
samtools

Version Compatibility

Reference examples tested with: 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.

SAM/BAM/CRAM Basics

"Read a BAM file" -> Open a binary alignment file and iterate over aligned reads with their mapping coordinates, flags, and quality scores.

  • Python: pysam.AlignmentFile() (pysam)
  • CLI: samtools view (samtools)
  • R: scanBam() (Rsamtools)

View and convert alignment files using samtools and pysam.

Format Overview

FormatDescriptionUse Case
SAMText format, human-readableDebugging, small files
BAMBinary compressed SAMStandard storage format
CRAMReference-based compressionLong-term archival, smaller than BAM

SAM Format Structure

@HD VN:1.6 SO:coordinate
@SQ SN:chr1 LN:248956422
@RG ID:sample1 SM:sample1
@PG ID:bwa PN:bwa VN:0.7.17
read1  0   chr1  100  60  50M  *  0  0  ACGT...  FFFF...  NM:i:0

Header lines start with @:

  • @HD - Header metadata (version, sort order)
  • @SQ - Reference sequence dictionary
  • @RG - Read group information
  • @PG - Program used to create file

Alignment fields (tab-separated):

  1. QNAME - Read name
  2. FLAG - Bitwise flag
  3. RNAME - Reference name
  4. POS - 1-based position
  5. MAPQ - Mapping quality
  6. CIGAR - Alignment description
  7. RNEXT - Mate reference
  8. PNEXT - Mate position
  9. TLEN - Template length
  10. SEQ - Read sequence
  11. QUAL - Base qualities
  12. Optional tags (NM:i:0, MD:Z:50, etc.)

samtools view

View BAM as SAM
bash
samtools view input.bam | head
View with Header
bash
samtools view -h input.bam | head -100
View Header Only
bash
samtools view -H input.bam
View Specific Region
bash
samtools view input.bam chr1:1000-2000
Count Alignments
bash
samtools view -c input.bam

Format Conversion

Goal: Convert between SAM (text), BAM (binary), and CRAM (reference-compressed) alignment formats.

Approach: Use samtools view with format flags (-b for BAM, -C for CRAM, -h for SAM with header). CRAM requires a reference FASTA with -T.

BAM to SAM
bash
samtools view -h -o output.sam input.bam
SAM to BAM
bash
samtools view -b -o output.bam input.sam
BAM to CRAM
bash
samtools view -C -T reference.fa -o output.cram input.bam
CRAM to BAM
bash
samtools view -b -T reference.fa -o output.bam input.cram
Pipe Conversion
bash
samtools view -b input.sam > output.bam

Common Flags

FlagDecimalMeaning
0x11Paired
0x22Proper pair
0x44Unmapped
0x88Mate unmapped
0x1016Reverse strand
0x2032Mate reverse strand
0x4064First in pair
0x80128Second in pair
0x100256Secondary alignment
0x200512Failed QC
0x4001024PCR duplicate
0x8002048Supplementary
Decode Flags (Bidirectional)
bash
# Number to mnemonics
samtools flags 147
# 0x93 147 PAIRED,PROPER_PAIR,REVERSE,READ2

# Mnemonics to number
samtools flags PAIRED,PROPER_PAIR,REVERSE,READ2   # 147
Secondary vs Supplementary (Different Semantics)

Two different concepts that are routinely conflated:

BitNameMeaningFilter implication
0x100 (256)SecondaryAn alternative candidate alignment for the same read; not the primary location-F 256 is correct for SNV/indel calling on short reads
0x800 (2048)SupplementaryA piece of a chimeric/split alignment (the read is split across loci)Carries SA:Z tag; required by SV callers (Manta, Sniffles, cuteSV, GRIDSS, Delly)

-F 2304 removes both. Strip supplementary only when downstream is small-variant calling; keep supplementary for SV calling, fusion detection, or any analysis that follows split-reads.

MAPQ Is Not Portable Across Aligners

samtools view -q 30 does different things depending on what produced the BAM. MAPQ is an aligner-specific scale, not a universal probability:

AlignerMAPQ scale"Unique" sentinelCommon gotcha
BWA-MEM / BWA-MEM20-6060-q 30 is sensible "high confidence"
minimap2 (DNA / pbmm2)0-6060Spec-compliant
HISAT20-6060Spec-compliant
Bowtie20-4242 (rare)-q 60 drops everything; -q 23 is a common "uniquely mapped" convention (not a probabilistic 99% threshold)
STAR0, 1, 3, 255255 = uniquely mapped (sentinel, not a quality)-q 255 for "unique only"; -q 30 accidentally keeps unique only too
DRAGEN0 to --mapq-max (default 60)varies-q 30 still meaningful; distribution shape differs
Cell Ranger / STARsoloinherits STAR255Same trap as STAR

Verify the actual scale of any unfamiliar BAM:

bash
samtools view input.bam | awk '{print $5}' | sort -un | head
samtools view -H input.bam | grep '^@PG' | head -1   # which aligner produced this BAM

0-Based vs 1-Based Coordinates (Footgun)

ContextCoordinate system
SAM text POS1-based, inclusive
samtools view chr1:100-2001-based, closed interval
samtools faidx chr1:100-2001-based, closed interval
BAM binary internal0-based, half-open
pysam read.reference_start0-based
bam.fetch('chr1', 100, 200)0-based, half-open
BED files0-based, half-open
VCF1-based
GFF/GTF1-based, inclusive

samtools view bam chr1:100-200 and bam.fetch('chr1', 100, 200) return different read sets at boundaries.

CIGAR Operations

OpDescription
MAlignment match (can be mismatch)
IInsertion to reference
DDeletion from reference
NSkipped region (introns in RNA-seq; do NOT count as covered bases)
SSoft clipping (sequence in SEQ but not aligned)
HHard clipping (sequence not in SEQ)
=Sequence match (explicit)
XSequence mismatch (explicit)
PPadding (rare; multiple-sequence-alignment context)

Example: 50M2I30M = 50 bases match, 2 base insertion, 30 bases match

CIGAR M is overloaded -- it is the union of = and X. Some aligners emit =/X directly (e.g. minimap2 with --eqx); bcftools / Picard often need M and rebuild MD/NM with samtools calmd. N operations break naive coverage calculations: a 1000 bp RNA-seq read with one 50 kb intron does not cover 50 kb. Distinguish soft-clip (S, bases retained) from hard-clip (H, bases discarded -- irreversible).

Show full SKILL.md (663 more words)Show less

Context-Specific Tags

Beyond the standard fields, downstream tools depend on optional tags whose presence depends on aligner and assay. Inspect with samtools view input.bam | head -1 | tr '\t' '\n' or pysam read.get_tag('XX').

TagSet byMeaningRequired by
NM:ibwa, samtools calmdEdit distance to referencemapDamage, many filters
MD:Zbwa, samtools calmdMismatch positions (text)bcftools mpileup BAQ, IGV mismatch coloring
MC:Zsamtools fixmate -mMate CIGARsamtools markdup
ms:isamtools fixmate -mMate score (lowercase per SAMtags)samtools markdup
RG:Zaligner from -RRead group IDGATK BQSR, MarkDuplicates LB lookup
SA:ZAll split-read alignersComma-list of supplementary coordsSniffles, Manta, cuteSV, GRIDSS, Delly
NH:iSTAR, HISAT2Number of reported hitsfeatureCounts multimapper handling, Salmon
HI:iSTARHit index among NH (1-based by default; --outSAMattrIHstart 0 for 0-based)RSEM
XS:ASTAR (--outSAMstrandField intronMotif), HISAT2Strand inferred from splice motifStringTie, Cufflinks
ts:Aminimap2 -ax spliceTranscript strand from splice motifStringTie
CB:ZCell Ranger, STARsoloCorrected cell barcodescRNA quantification
UB:ZCell Ranger, STARsoloCorrected UMIUMI-aware dedup
RX:Zfgbio AnnotateBamWithUmisRaw UMI (bulk)fgbio GroupReadsByUmi
MI:Zfgbio GroupReadsByUmiMolecular identifier (UMI group)CallMolecularConsensusReads, duplex calling
cs:Zminimap2 --csCompact CIGAR-with-basespaftools, SV tools

Missing tags fail in two modes: silently wrong (featureCounts ignoring multimappers without NH; markdup marking nothing without MC/MS) or loudly (consensus tools rejecting input without MD).

Provenance: @PG Chain

The @PG lines record every tool that touched the BAM, linked through PP (previous program) tags. This is the audit trail.

bash
samtools view -H input.bam | grep '^@PG'

A clean germline pipeline:

@PG ID:bwa-mem PN:bwa VN:0.7.17
@PG ID:samtools.1 PN:samtools VN:1.20 PP:bwa-mem CL:samtools sort
@PG ID:samtools.2 PN:samtools VN:1.20 PP:samtools.1 CL:samtools fixmate
@PG ID:samtools.3 PN:samtools VN:1.20 PP:samtools.2 CL:samtools markdup

A broken/missing chain (no PP, unknown tools, gaps) means the BAM cannot be reliably reproduced. Production pipelines often reject inputs without a complete chain.

CRAM Reference Resolution (Critical)

CRAM stores reads relative to a reference; without it, the file is unreadable. htslib resolves the reference in this order:

  1. Command-line -T ref.fa / --reference
  2. REF_CACHE env var (local MD5-named cache; searched before REF_PATH)
  3. REF_PATH env var (colon-separated; each element matched by the @SQ M5: MD5). A remote server such as EBI ENA is consulted only if its URL is present here -- it was the built-in default through htslib 1.21, but that default was removed in 1.22 to reduce EBI load, so modern htslib does no network lookup unless that URL is added explicitly.
  4. Local file named in the @SQ UR: header tag (local / file:// paths only; http/ftp URIs in UR: are ignored)

On HPC nodes without internet, populate a local cache once:

bash
mkdir -p $HOME/cram_cache
seq_cache_populate.pl -root $HOME/cram_cache reference.fa
export REF_CACHE=$HOME/cram_cache/%2s/%2s/%s
export REF_PATH=$REF_CACHE   # local only; no network/ENA lookup

samtools quickcheck -v file.cram   # header + EOF only
samtools view -c file.cram          # forces full decode; proves reference reachable

CRAM can be made irreversibly lossy, but the archive profile is NOT how: --output-fmt-option archive is a lossless maximum-compression preset (fqzcomp quality codec, name tokenization, larger slices) that does not alter bases or qualities. Irreversible loss comes instead from explicit quality binning (e.g. Illumina 8-bin), which must be applied deliberately and is harmful for low-coverage / somatic / forensic / archival data. Convert against the exact reference the BAM was aligned to (matched by @SQ M5:); a different reference silently corrupts bases on read-back.

pysam Python Alternative

Goal: Read and manipulate alignment data programmatically in Python.

Approach: Use pysam.AlignmentFile to open BAM/CRAM files, iterate over reads, and access properties like coordinates, flags, CIGAR, and tags.

Open and Iterate
python
import pysam

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for read in bam:
        print(f'{read.query_name}\t{read.reference_name}:{read.reference_start}')
Access Header
python
with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for sq in bam.header['SQ']:
        print(f'{sq["SN"]}: {sq["LN"]} bp')
Read Alignment Properties
python
with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for read in bam:
        print(f'Name: {read.query_name}')
        print(f'Flag: {read.flag}')
        print(f'Chrom: {read.reference_name}')
        print(f'Pos: {read.reference_start}')  # 0-based
        print(f'MAPQ: {read.mapping_quality}')
        print(f'CIGAR: {read.cigarstring}')
        print(f'Seq: {read.query_sequence}')
        print(f'Qual: {read.query_qualities}')
        break
Check Flag Properties
python
with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for read in bam:
        if read.is_paired and read.is_proper_pair:
            if read.is_reverse:
                strand = '-'
            else:
                strand = '+'
            print(f'{read.query_name} on {strand} strand')
Fetch Region
python
with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for read in bam.fetch('chr1', 1000, 2000):
        print(read.query_name)
Convert BAM to SAM
python
with pysam.AlignmentFile('input.bam', 'rb') as infile:
    with pysam.AlignmentFile('output.sam', 'w', header=infile.header) as outfile:
        for read in infile:
            outfile.write(read)
Convert to CRAM
python
with pysam.AlignmentFile('input.bam', 'rb') as infile:
    with pysam.AlignmentFile('output.cram', 'wc', reference_filename='reference.fa', header=infile.header) as outfile:
        for read in infile:
            outfile.write(read)

Quick Reference

Tasksamtoolspysam
View BAMsamtools view file.bamAlignmentFile('file.bam', 'rb')
View headersamtools view -H file.bambam.header
Count readssamtools view -c file.bamsum(1 for _ in bam)
Get regionsamtools view file.bam chr1:1-1000bam.fetch('chr1', 0, 1000)
BAM to SAMsamtools view -h -o out.sam in.bamOpen with 'w' mode
SAM to BAMsamtools view -b -o out.bam in.samOpen with 'wb' mode
BAM to CRAMsamtools view -C -T ref.fa -o out.cram in.bamOpen with 'wc' mode
  • alignment-indexing - Create indices for random access (required for fetch/region queries)
  • alignment-sorting - Sort alignments by coordinate or name
  • alignment-filtering - Filter alignments by flags, quality, regions
  • alignment-validation - Sequence dictionary cross-validation (M5 checksums)
  • bam-statistics - Generate statistics from alignment files
  • reference-operations - REF_PATH/REF_CACHE setup for CRAM
  • sequence-io/read-sequences - Parse FASTA/FASTQ input files

© 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 3 other files in alignment-files/sam-bam-basics of GPTomics/bioSkills.

  • SKILL.md
  • examples/convert_formats.sh
  • examples/view_bam.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 7, 2026.

Compare with similar skills

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Works with

Questions about Bio Sam Bam Basics

What does Bio Sam Bam Basics do?

View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Bio Sam Bam Basics is an agent skill from GPTomics/bioSkills. View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam.

When should I use Bio Sam Bam Basics?

Bio Sam Bam Basics fits situations like: inspecting alignments; converting between formats; understanding alignment file structure.

How do I install Bio Sam Bam Basics in Claude Code?

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

How do I install Bio Sam Bam Basics in Codex?

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

Can I use Bio Sam Bam Basics 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-sam-bam-basics -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-sam-bam-basics, .gemini/skills/bio-sam-bam-basics, .github/skills/bio-sam-bam-basics and .opencode/skills/bio-sam-bam-basics in your project.

What does Bio Sam Bam Basics need to run?

Going by SKILL.md and its folder, Bio Sam Bam Basics needs a shell and Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3; A Bash shell.

Does Bio Sam Bam Basics 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 Sam Bam Basics 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 Sam Bam Basics use?

Bio Sam Bam Basics 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 Sam Bam Basics use?

About 3.7k tokens (SKILL.md is roughly 15k 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 Sam Bam Basics?

Skills that share tags, products or a category with Bio Sam Bam Basics: Pysam (davila7/claude-code-templates, 33k stars), Pysam (K-Dense-AI/scientific-agent-skills, 48k stars), Omics Tools (DrugClaw/DrugClaw, 126 stars) and Tooluniverse Epigenomics (wu-yc/LabClaw, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Sam Bam Basics?

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.