Library user guide#

Use this page if you intend to call Sequana from Python or a Jupyter notebook — to read NGS file formats, compute metrics, or assemble report sections from a script.

For end-users running pre-built pipelines, see Pipeline user guide instead.

Test data#

Sequana ships a small data folder accessible from Python via sequana_data():

from sequana import sequana_data
filename = sequana_data('JB409847.bed')

A complete list of bundled files is in sequana.datatools.

Coverage from a BED file#

Read a BED file produced by bedtools genomecov:

from sequana import SequanaCoverage
gc = SequanaCoverage(filename)

Select a chromosome, compute the running median and z-score:

chrom = gc[0]
chrom.running_median(n=5001, circular=True)
chrom.compute_zscore()

Plot the coverage with its 3-sigma confidence band:

chrom.plot_coverage()

(Source code, png, hires.png, pdf)

_images/userguide-1.png

A matching notebook is at notebooks/coverage.ipynb.

FastQ inspection#

The FastQC class exposes per-read metrics:

from sequana import FastQC, sequana_data

fastqc = FastQC(sequana_data("test.fastq"))
print(fastqc.fastq)
for x in 'ACGT':
    fastqc.get_actg_content()[x].hist(
        alpha=0.5, label=x, histtype='step', lw=3, bins=10)

from sequana import FastQC, sequana_data
fastqc = FastQC(sequana_data("test.fastq"))
for x in 'ACGT':
    fastqc.get_actg_content()[x].hist(
        alpha=0.5, label=x, histtype='step', lw=3, bins=10)
from pylab import legend
legend()

(Source code, png, hires.png, pdf)

_images/userguide-2.png

Building HTML report sections from Python#

Sequana's pipeline reports are themselves assembled from reusable building blocks in sequana.modules_report. You can call them on your own data. Example for a BAM file:

from sequana import BAM, sequana_data
from sequana.modules_report.bamqc import BAMQCModule
BAMQCModule(sequana_data("test.bam", "testing"), "bam.html")

The generated bam.html is a self-contained page (see bam.html for a rendered example).

To build a brand-new report module, see Module reports in the developer guide.

Where to look next#

  • API reference — the full module index.

  • Gallery — Sphinx-Gallery of short, runnable scripts.

  • Notebooks — Jupyter notebooks demonstrating BAM, coverage, FastQ, feature counts and ribodesigner workflows.

  • CLI reference — the sequana CLI sub-commands (some of these wrap the Python API).