Briefly, the goal of metaseq is to tie together lots of existing software into a framework for exploring genomic data. It focuses on flexibility and interactive exploration and plotting of disparate genomic data sets.

The main documentation for metaseq can be found at http://packages.python.org/metaseq/.

There are multiple ways of viewing this example, depending on how you are viewing this document:

• Latest release version on PyPI: Example 1
• Reading this on GitHub? See Example 1.
• IPython notebook: View on nbviewer
• Compiled Sphinx docs: :ref:`[relative link within this documentation] <example_session>`,

Top: Heatmap of ATF3 ChIP-seq signal over transcription start sites (TSS) on chr17 in human K562 cells. Middle: average ChIP enrichment over all TSSs +/- 1kb, with 95% CI band. Bottom: Integration with ATF3 knockdown RNA-seq results, showing differential enrichment over transcripts that went up, down, or were unchanged upon ATF3 knockdown.

There are multiple ways of viewing this example, depending on how you are viewing this document.

• Latest release version on PyPI: Example 2
• Reading this on GitHub? See Example 2.
• IPython notebook: View on nbviewer
• Compiled Sphinx docs: :ref:`[relative link within this documentation] <example_session_2>`,

Control vs knockdown expression (log2(FPKM + 1)) for an ATF3 knockdown experiment. Each point represents one transcript on chromosome 17. Marginal distributions are shown on top and side. 1:1 line shown as a dotted line. Up- and downregulated genes determined by a simple 2-fold cutoff.

In addition, metaseq offers:

• A format-agnostic API for accessing "genomic signal" that allows you to work with BAM, BED, VCF, GTF, GFF, bigBed, and bigWig using the same API.
• Parallel data access from the file formats mentioned above
• "Mini-browsers", zoomable and pannable Python-only figures that show genomic signal and gene models and are spawned by clicking on features of interest
• A wrapper around pandas.DataFrames to simplify the manipulation and plotting of tabular results data that contain gene information (like DESeq results tables)
• Integrates data keyed by genomic interval (think BAM or BED files) with data keyed by gene ID (e.g., Cufflinks or DESeq results tables)

Check out the full documentation for more.