Integrative analysis of high-thoughput sequencing data
Project description
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/.
.. image:: https://travis-ci.org/daler/metaseq.svg?branch=master
:target: https://travis-ci.org/daler/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.
=======
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/.
.. image:: https://travis-ci.org/daler/metaseq.svg?branch=master
:target: https://travis-ci.org/daler/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.
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