metaseq 0.5

Integrative analysis of high-thoughput sequencing data

Metaseq
=======

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/.


Example 1: Average ChIP-seq signal over promoters
-------------------------------------------------
There are multiple ways of viewing this example, depending on how you are
viewing this document:

* Latest release version on PyPI: `Example 1 <https://pythonhosted.org/metaseq/example_session.html>`_
* Reading this on GitHub? See `Example 1 <doc/source/example_session.rst>`_.
* IPython notebook: View on `nbviewer <http://nbviewer.ipython.org/github/daler/metaseq/blob/master/doc/source/example_session.ipynb?create=1>`_
* Compiled Sphinx docs: :ref:`[relative link within this documentation] <example_session>`,


.. figure:: demo.png

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.

Example 2: Differential expression scatterplots
-----------------------------------------------
There are multiple ways of viewing this example, depending on how you are
viewing this document.

* Latest release version on PyPI: `Example 2 <https://pythonhosted.org/metaseq/example_session_2.html>`_
* Reading this on GitHub? See `Example 2 <doc/source/example_session_2.rst>`_.
* IPython notebook: View on `nbviewer <http://nbviewer.ipython.org/github/daler/metaseq/blob/master/doc/source/example_session_2.ipynb?create=1>`_
* Compiled Sphinx docs: :ref:`[relative link within this documentation] <example_session_2>`,


.. figure:: expression-demo.png

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.

Other features
--------------
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 <http://packages.python.org/metaseq/>`_ for
more.