Metadata-Version: 2.1
Name: ChIP-R
Version: 1.1.11
Summary: ChIP-R is a method for assessing the reproducibility of replicated ChIP-seq type experiments. It incorporates the rank product method, a novel thresholding methods, and the use of peak fragmentation return the most reproducible peaks.
Home-page: https://github.com/rhysnewell/ChIP-R
Author: Rhys Newell, Mikael Boden, Alex Essebier
Author-email: r.newell@uq.edu.au
License: GPL-3.0
Description: ChIP-R ("chipper")
        ==================
        
        ChIP-R uses an adaptation of the rank product statistic to assess the reproducibility of ChIP-seq peaks by incorporating information from multiple ChIP-seq replicates and "fragmenting" peak locations to better combine the information present across the replicates.
        
        Install
        -------
        
        - [Python3.x](https://www.python.org/getit/) with the following packages:
        - Numpy
        - Scipy
        - pyBigWig
        
        To install ChIP-R:
            
            pip install ChIP-R
            
        OR if you want to install from source:
        
            git clone https://github.com/rhysnewell/ChIP-R.git
            cd ChIP-R
            python3 setup.py install
        
        
        
        Usage
        -----
        
        In the command line, type in **'chipr -h '** for detailed usage.
        
            $ chipr -h
            
            usage: chipr [-h] -i INPUT [INPUT ...] [-o OUTPUT] [-m MINENTRIES]
                     [--rankmethod RANKMETHOD] [--duphandling DUPHANDLING]
                     [--seed RANDOM_SEED] [-a ALPHA]
        
            Combine multiple ChIP-seq files and return a union of all peak locations and a
            set confident, reproducible peaks as determined by rank product analysis
        
            optional arguments:
              -h, --help            show this help message and exit
              -i INPUT [INPUT ...], --input INPUT [INPUT ...]
                                    ChIP-seq input files. These files must be in either
                                    narrowPeak, broadPeak, or regionPeak format. Multiple
                                    inputs are separeted by a single space
              -o OUTPUT, --output OUTPUT
                                    ChIP-seq output filename prefix
              -B, --bigbed          Specify if input files are in BigBed format
              -m MINENTRIES, --minentries MINENTRIES
                                    The minimum peaks between replicates required to form
                                    an intersection of the peaks Default: 1
              --rankmethod RANKMETHOD
                                    The ranking method used to rank peaks within
                                    replicates. Options: 'signalvalue', 'pvalue',
                                    'qvalue'. Default: pvalue
              --duphandling DUPHANDLING
                                    Specifies how to handle entries that are ranked
                                    equally within a replicate Can either take the
                                    'average' ranks or a 'random' rearrangement of the
                                    ordinal ranks Options: 'average', 'random' Default:
                                    'average'
              --seed RANDOM_SEED    Specify a seed to be used in conjunction with the
                                    'random' option for -duphandling Must be between 0 and
                                    1 Default: 0.5
              -a ALPHA, --alpha ALPHA
                                    Alpha specifies the user cut-off value for set of
                                    reproducible peaks The analysis will still produce
                                    results including peaks within the threshold
                                    calculatedusing the binomial method Default: 0.05
        
        
        
        Example
        ------
            $ chipr -i input_prefix1.bed input_prefix2.bed input_prefix3.bed input_prefix4.bed -m 2 -o output_prefix   
        
        Output
        ------
        
        Important result files:
        
        - **prefixname_ALL.bed**: All intersected peaks, ordered from most significant to least (10 columns)
        - **prefixname_T2.bed**: The tier 2 intersected peaks, the peaks that fall within the binomial threshold (10 columns)
        - **prefixname_T1.bed**: The tier 1 intersected peaks, the peaks that fall within the user defined threshold (10 columns)
        - **prefixname_log.txt**: A log containing the number of peaks appearing in each tier.
        
        
        prefixname.bed file has 10 columns. The output follows the standard peak format for bed files, with the addition of a 10th column that specifies the ranks of the peaks that produced this possible peak. See the toy example below.
        
        |chr |start|end  |name |score |strand  |signalValue |p-value |q-value|
        |----|-----|-----|----|------|-----|------|------|------|
        |chr1|9118 |10409|T3_peak_87823|	491|	.	|15.000000	| 0.113938|0.712353	|
        
        
        Citation
        --------
        
        
        
        
        Contact
        -------
        
        Authors: Rhys Newell, Michael Piper, Mikael Boden, Alexandra Essebier
        
        Contact:  rhys.newell(AT)uq.edu.au
        
Keywords: ChIP-R
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Requires-Python: >=3
Description-Content-Type: text/markdown
