Peak-calling and Prioritization pipeline for replicated ChIP-Seq data
Project description
## This is just an initial attempt for the README file, more stuff will come in later.
=======================
README for PePr v1.0.1
=======================
<05-07-2014 Yanxiao Zhang>
HOMEPAGE
=======
https://code.google.com/p/pepr-chip-seq/
---------------------------------------
INTRODUCTION
============
PePr is a ChIP-Seq Peak-calling and Prioritization pipeline
that uses a sliding window approach and models read counts across
replicates and between groups with a negative binomial distribution.
PePr empirically estimates the optimal shift/fragment size and
sliding window width, and estimates dispersion from the local genomic
area. Regions with less variability across replicates are ranked more
favorably than regions with greater variability. Optional
post-processing steps are also made available to filter out peaks
not exhibiting the expected shift size and/or to narrow the width of peaks.
INSTALL
=======
if you want to use pip to install PePr(recommended):
if pip package is intalled in your system:
simply run `pip install PePr`
else:
install pip and then run `pip intall PePr`
else if you don't want to use pip (going the harder way):
manually download and install the dependencies if not already
installed(numpy, scipy and pysam); download PePr and unzip
(tar zxvf PePr-version.tar.gz); go into the PePr directory and
type `python setup.py install`.
You can test the intallation by running the example code in the
PePr/data directory to see if it gives the same results as provided.
USAGE
=====
Please visit our homepage for instructions.
=======================
README for PePr v1.0.1
=======================
<05-07-2014 Yanxiao Zhang>
HOMEPAGE
=======
https://code.google.com/p/pepr-chip-seq/
---------------------------------------
INTRODUCTION
============
PePr is a ChIP-Seq Peak-calling and Prioritization pipeline
that uses a sliding window approach and models read counts across
replicates and between groups with a negative binomial distribution.
PePr empirically estimates the optimal shift/fragment size and
sliding window width, and estimates dispersion from the local genomic
area. Regions with less variability across replicates are ranked more
favorably than regions with greater variability. Optional
post-processing steps are also made available to filter out peaks
not exhibiting the expected shift size and/or to narrow the width of peaks.
INSTALL
=======
if you want to use pip to install PePr(recommended):
if pip package is intalled in your system:
simply run `pip install PePr`
else:
install pip and then run `pip intall PePr`
else if you don't want to use pip (going the harder way):
manually download and install the dependencies if not already
installed(numpy, scipy and pysam); download PePr and unzip
(tar zxvf PePr-version.tar.gz); go into the PePr directory and
type `python setup.py install`.
You can test the intallation by running the example code in the
PePr/data directory to see if it gives the same results as provided.
USAGE
=====
Please visit our homepage for instructions.
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