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sanger

A lightweight toolkit for Sanger sequencing data: chromatogram visualization, alignment & mutation calling, quality control, base-calling, trimming, assembly and export — usable as a CLI, a Python library, or an MCP server for LLM agents.

Quick start

pip install sanger
sanger mut -q read.ab1 -s ref.fa -o out --plot

from sanger import Chromatogram, parse_fasta

cg = Chromatogram.from_abi("./data/B5-M13R_B07.ab1")
print(cg.length, cg.mean_quality, cg.gc_percent)   # 1141 50.2 52.0
ref = parse_fasta("./data/ref.fa")
print(cg.qc())                                    # QC metrics (CRL, SNR)
print(cg.to_vcf(ref))                             # variant calling -> VCF
cg.plot(region=(55, 90))                          # render a region

Examples

The gallery below is produced from the bundled real ABI sample (data/B5-M13R_B07.ab1 vs data/ref.fa) by python -m scripts.make_readme_examples.

Mutation calling — the SNP T61A is highlighted and annotated.

Mutation calling

Quality control — per-base Phred quality, CRL and Mott trimming.

Quality profile

Side-by-side panels — chromatogram + GC% + quality on a shared axis.

Side-by-side

Feature overlay — primers, amplicon and SNPs on the trace.

Feature overlay

Re-called bases — mixed/heterozygous sites are marked (M/W/K).

Re-called bases

Assembly — pileup depth and consensus against a reference.

Assembly

Installation

The default install pulls only numpy, click and rich-click — no C compiler and no plotting library required. Parsing, QC, alignment (a bundled Cython Smith-Waterman with a NumPy fallback), analysis and export all work out of the box.

pip install sanger

Optional extras:

pip install "sanger[plot]"     # matplotlib -> chromatogram figures
pip install "sanger[viewer]"   # DNA Features Viewer integration
pip install "sanger[agent]"    # MCP server for LLM agents
pip install "sanger[all]"      # everything

The bundled Cython Smith-Waterman accelerator (sanger._swalign, self-contained, no ssw dependency) is compiled automatically when a C compiler is present at build time; otherwise the NumPy fallback is used.

From source:

git clone git@github.com:y9c/sanger.git
cd sanger
make init       # install dependencies
make test       # run the test-suite

Command-line interface

Built on rich-click, with themed command groups (sanger --help):

sanger mut            mutation calling & reporting
sanger qc             per-read quality-control metrics
sanger track          split / join / slice chromatogram trace files
sanger edit           trim, strip primers, reverse-complement
sanger basecall       re-call bases from raw four-channel traces
sanger assemble       reference-guided pileup & consensus
sanger analyze        sequence biology (translate, motifs, restriction)
sanger export         FASTA / VCF / JSON / batch summary
sanger plot           chromatogram rendering (+ features / DNA viewer)

Examples:

sanger mut -q read.ab1 -s ref.fa -o out --plot        # mutation report + figure
sanger qc r1.ab1 r2.ab1                               # QC table
sanger track split read.ab1 -c 20,40 -f tsv           # split traces
sanger edit trim read.ab1 -c 0.05 -o out              # Mott quality trim
sanger edit strip-primers read.ab1 -f AAAA -r CCCA    # primer removal
sanger basecall call read.ab1 -r 0.45                 # re-call bases
sanger basecall hetero read.ab1                       # mixed/heterozygous sites
sanger assemble consensus a.ab1 b.ab1 -r ref.fa       # reference-guided consensus
sanger analyze rest read.ab1                          # restriction sites
sanger analyze translate read.ab1 -f 1                # protein translation
sanger export vcf -q read.ab1 -s ref.fa               # VCF of variants
sanger export batch *.ab1 -o out -f csv               # batch QC table
sanger plot dnaviewer read.ab1 --start 50 --end 100   # with DNA Features Viewer

Python API

The high-level Chromatogram object is the easiest way to work with the toolkit; the low-level modules remain available for custom work.

from sanger import Chromatogram, parse_fasta

cg = Chromatogram.from_abi("./data/B5-M13R_B07.ab1")
ref = parse_fasta("./data/ref.fa")
Common operations

Mutation calling & quality filtering

from sanger.quality import QualityFilter

snps = cg.call_mutations(ref)
confident = QualityFilter(min_base_qual=20, min_local_qual=20).filter(snps)
print([f"{s.ref_base}{s.ref_pos}{s.cf_base}" for s in confident])

QC metrics (incl. continuous read length)

m = cg.qc()
print(m["mean_qual"], m["trim_start"], m["trim_end"], m["crl"], m["snr"])

Re-call bases from raw traces + mixed/heterozygous sites

res = cg.basecall()
print(res.sequence)
for pos, major, minor, frac in res.heterozygotes(min_ratio=0.2):
    print(pos, major, minor, frac)

Trim / reverse-complement / orientation

trimmed = cg.trim().trim_leading_ns()     # Mott trim then drop leading Ns
rc      = cg.reverse_complement()
ori     = cg.detect_orientation(ref)      # +1 forward, -1 reverse-complement

Sequence-level analysis

print(cg.analyze("translate", frame=1))            # protein translation
print(cg.analyze("restriction"))                    # {'EcoRI': [27], ...}
print(cg.analyze("motif", motif="AATT"))            # motif positions

Export

print(cg.to_fasta())
print(cg.to_vcf(ref))
cg.export("out")                                    # write FASTA to disk

Feature overlay (for external tools)

from sanger import ChromatogramFeature
from sanger.features import plot_features

feat = ChromatogramFeature(start=90, end=130, strand=+1, label="primer F")
fig, ax = cg.plot(region=(80, 140))
plot_features(cg.to_record, ax, features=[feat])

Side-by-side with another tool's output (shared x-axis)

from sanger.composite import side_by_side

def my_panel(ax, trace_x, peaks, seq, record, start=None):
    ax.bar(range(len(seq)), [1.0] * len(seq), color="0.66")
    ax.set_ylabel("my tool signal")

fig, (ax_chrom, ax_panel) = side_by_side(cg.to_record, my_panel, region=(10, 40))

Consensus / assembly from many reads

from sanger import parse_abi
from sanger.assembly import pileup, consensus

reads = [parse_abi(f) for f in ["a.ab1", "b.ab1", "c.ab1"]]
table = pileup(reads, ref, quality_threshold=20)
print(consensus(table))

Plot together with DNA Features Viewer (needs sanger[viewer])

from sanger import ChromatogramFeature
from sanger.dnalink import plot_combined

feats = [ChromatogramFeature(start=90, end=130, strand=+1, label="primer F")]
fig, (ax_feat, ax_chrom) = plot_combined(cg.to_record, features=feats, region=(55, 90))

Chromatogram object

A terse, idiomatic workflow in one object:

from sanger import Chromatogram

cg = Chromatogram.from_abi("./data/B5-M13R_B07.ab1")
cg.length, cg.mean_quality, cg.gc_percent, cg.channels   # 1141 50.2 52.0 GATC
cg.qc()                            # QC metrics (CRL, SNR)
cg.basecall()                      # re-call bases from raw traces
cg.call_mutations(ref)             # variant calling
cg.trim().trim_leading_ns()        # quality trimming
cg.reverse_complement()            # reverse-strand view
cg.analyze("restriction")          # restriction-site scan
cg.plot(region=(55, 90))           # render a region
cg.to_fasta(), cg.to_vcf(ref)      # export
cg.export("out")                   # write to disk

Agent / MCP

sanger ships a Model Context Protocol server so LLM agents and MCP clients can call the toolkit as tools:

pip install "sanger[agent]"     # adds mcp>=2
sanger-mcp                        # run the MCP server over stdio
python -m sanger.mcp_server       # identical
Tool Purpose
read_chromatogram(path) parse an ABI → summary (length, GC%, quality, CRL)
qc_metrics(path) full per-read QC metrics (incl. CRL, signal, SNR)
call_mutations(query ab1, subject fa) variants vs a reference (SNPs/indels)
re_call_bases(path) re-call bases from raw traces + heterozygotes
analyze_sequence(path, kind) translate / motifs / restriction / GC
trim_read(path, mode) quality-trim a read
export_sequence(path, outdir) write FASTA or VCF
plot_chromatogram(path, out, start, end) render a chromatogram PNG

Register it in an MCP client's config, e.g.:

{ "mcpServers": { "sanger": { "command": "sanger-mcp" } } }

ChangeLog

  • Replace the external ssw aligner with a bundled Cython Smith-Waterman (+ NumPy fallback) — no external alignment dependency.
  • Reverse-complement the chromatogram file (inspired by Snapgene).
  • Add per-base quality filtering, continuous read length (CRL), signal/SNR QC.
  • Add base-calling from raw four-channel traces + heterozygote (mixed-base) calling.
  • Add feature overlay API and DNA Features Viewer integration.
  • Add split / join / slice trace operations with provenance (offset).
  • Add reference-guided pileup & consensus (assembly module).
  • Add FASTA / VCF / JSON / batch export.
  • Fix false-positive mutations from lowercase reference bases (case-insensitive).
  • Build the CLI with rich-click (themed groups, short options, --version).

TODO

  • call mutation by alignment and plot Chromatogram graphic
  • add a doc
  • change x-axis by peak location
  • fix bug that chromatogram switches position after trim
  • wrap as a CLI app
  • return quality score in output (quality module + report)
  • fix selected base not being centred (center_region)
  • fix plot_chromatograph rendering bug (full-region bounds, channels)
  • add projection/assembly (assembly module)
  • preserve trimmed-origin positions when slicing/joining (offset provenance)

Project links

Data verified by PyPI on Aug 27, 2026
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  • Released:
Newer release available (0.1.5)

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Avatar for yech from gravatar.com yech

Credits

Author: Ye Chang

GitHub Statistics

Data verified by PyPI on Aug 27, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

plot viewer agent all

Tags

DNA mutation chromatogram biology

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