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DaPars: Dynamitic analysis of Alternative PolyAdenylation from RNA-seq
============================================






Introduction
================================
The dynamic usage of the 3’untranslated region (3’UTR) resulting from alternative polyadenylation (APA) is emerging as a pervasive mechanism for regulating mRNA diversity, stability and translation.
Though RNA-seq provides the whole-transcriptome information and a lot of tools for analyzing gene/isoform expression are available, very few tool focus on the analysis of 3'UTR from standard RNA-seq. 
DaPars is the first de novo tool that directly infers the dynamic alternative polyadenylation (APA) usage by comparing standard RNA-seq.
Given the annotated gene model, DaPars can infer the de novo proximal APA sites as well as the long and short 3'UTR expression levels. Finally, the dynamic APA usages between two conditions will be identified.


Installation
===================================

Prerequisite: `python2.7 <http://www.python.org/getit/releases/2.7/>`_;  `numpy <http://numpy.scipy.org/>`_; `scipy <http://www.scipy.org/>`_; `R <http://www.r-project.org/>`_
 

Install DaPars::
 
 tar zxf DaPars-VERSION.tar.gz
  
 cd DaPars-VERSION


Input format
=============================

DaPars requires the following two file formats as input:

* `BED <http://genome.ucsc.edu/FAQ/FAQformat.html#format1>`_ file is tab separated, 12 column, plain text file to represent gene model. The gene model can be downloaded from `UCSC <http://genome.ucsc.edu/cgi-bin/hgTables?command=start>`_. 
* `Wiggle <http://genome.ucsc.edu/goldenPath/help/wiggle.html>`_ files store the reads alignment result, which can be generated from BAM file from RNA-seq alignment tool such as TopHat.

Usage Information
================================
Step 1: Generate region annotation: DaPars_Extract_Anno.py
-------------------------------
DaPars will use the extracted distal polyadenylation sites to infer the proximal polyadenylation sites based on
the alignment wiggle files of two samples. The output in this step will be used by the next step.

Options:
  -h, --help            show this help message and exit
  -b GENE_BED_FILE, --bed=GENE_BED_FILE
                        The gene model in BED format. The BED file can be downloaded from `UCSC <http://genome.ucsc.edu/cgi-bin/hgTables?command=start>`_ 
  -s Gene_Symbol_FILE, --gene_symbol_map=Gene_Symbol_FILE
			The mapping of transcripts to gene symbol.
  -o OUTPUT_FILE, --out-prefix=OUTPUT_FILE
                        The extracted annotation region will be stored into this file.


Step 2: main function to get final result: python DaPars_main.py configure_file
-------------------------------
Run this function to get the final result. The configure file is the only parameter for DaPars_main.py, which stores all the parameters. 

The format of the configure is:: 

 #The following file is the result of step 1.

 Annotated_3UTR=hg19_refseq_extracted_3UTR.bed

 #A comma-separated list of Wiggle files of samples from condition 1

 Group1_Tophat_aligned_Wig=Condition_A_chrX.wig

 #A comma-separated list of Wiggle files of samples from condition 2

 Group2_Tophat_aligned_Wig=Condition_B_chrX.wig

 Output_directory=DaPars_Test_data/

 Output_result_file=DaPars_Test_data

 #At least how many samples passing the coverage threshold in two conditions 
 Num_least_in_group1=1

 Num_least_in_group2=1

 Coverage_cutoff=30

 #Cutoff for FDR of P-values from Fisher exact test.

 FDR_cutoff=0.05
 
 
 PDUI_cutoff=0.5

 Fold_change_cutoff=0.59 


Output format
-------------------------------

Output format:

.. image:: _static/Result_example.jpg
   :height: 70 px
   :width: 1400 px



Release history
===================
DaPars v0.9.0

* DaPars is released.


Contact
================================
* Zheng Xia: zxia@bcm.edu
* Wei Li <wl1@bcm.edu>