Metadata-Version: 2.1
Name: HiNT-Package
Version: 2.2.1
Summary: HiNT -- HiC for copy number vairations and translocations detection 
Home-page: https://github.com/suwangbio/HiNT_py3
Author: Su Wang
Author-email: wangsu0623@gmail.com
License: UNKNOWN
Description: # HiNT 
        ## A computational method for detecting copy number variations and translocations from Hi-C data
        
        ## Summary
        **HiNT** (**Hi**-C for copy **N**umber variation and **T**ranslocation detection), a computational method to detect CNVs and Translocations from Hi-C data. HiNT has three main components: **HiNT-PRE**, **HiNT-CNV**, and **HiNT-TL**. HiNT-PRE preprocesses Hi-C data and computes the contact matrix, which stores contact frequencies between any two genomic loci; both HiNT-CNV and HiNT-TL starts with HI-C contact matrix, predicts copy number segments, and inter-chromosomal translocations, respectively 
        
        #### Overview of HiNT workflow: 
        <img src="https://github.com/suwangbio/HiNT/blob/master/images/HiNT_workflow.png" width="800">
        
        ## Installation
        
        ### Dependencies
        R and R packages
        
        1. [R >= 3.4](https://www.r-project.org/)
        2. [mgcv](https://cran.r-project.org/web/packages/mgcv/index.html), [strucchange](https://cran.r-project.org/web/packages/strucchange/index.html), [doParallel](https://www.rdocumentation.org/packages/parallel/versions/3.4.1), [Cairo](https://cran.r-project.org/web/packages/Cairo/index.html), [foreach](https://cran.r-project.org/web/packages/optparse/index.html)
        
        Python and Python packages
        
        1. [python >= 3.5](https://www.python.org/)
        2. [pyparix >= 0.3.0](https://github.com/4dn-dcic/pairix#pypairix), [cooler >= 0.7.4](https://github.com/mirnylab/cooler), [pairtools >= 0.2.2](https://github.com/mirnylab/pairtools), [numpy](https://www.scipy.org/install.html), [scipy](https://www.scipy.org/install.html), [pandas](https://pandas.pydata.org/), [sklearn](https://scikit-learn.org/stable/install.html), [multiprocessing](https://pypi.org/project/multiprocess/)
        
        Java and related tools (Optional: required when want to process Hi-C data with juicer tools)
        
        1. [Java (version >= 1.7)](https://www.java.com/en/download/)
        2. [Juicer tools (1.8.9 is recommended)](https://github.com/aidenlab/juicer/wiki/Download) 
        
        Perl
        
        1. [Perl (version >= 5)](https://www.perl.org/)
        
        
        Other dependencies
        
        1. [samtools](http://www.htslib.org/doc/) (1.3.1+)
        2. [BIC-seq2](http://www.math.pku.edu.cn/teachers/xirb/downloads/software/BICseq2/BICseq2/BICseq2-seg_v0.7.3.tar.gz) (0.7.3) ! This is optional: if you don't want to run HiNT-CNV, you don't need this package. [Download BICseq2, unzip it, and give the path of BICseq2-seg_v0.7.3 (/path/to/BICseq2-seg_v0.7.3)].
        3. [bwa](https://sourceforge.net/projects/bio-bwa/files/) (0.7.16+) ! This is optional: required only when your input is fastq
        4. [tabix](https://sourceforge.net/projects/samtools/files/tabix/) (0.2.6)
        
        ### Install HiNT
        
        * Method1: Install using conda (highly recommended)
        
        	``` $ conda install -c su hint=2.2.1```
        
        	or
        
        	``` $ conda install hint```
        	
        * Method2: Install from PyPI using pip.
        
        	``` $ pip install HiNT-Packages```
        
        * Method3: Install manually 
          1. Install HiNT dependencies
          2. Download HiNT ```git clone https://github.com/parklab/HiNT.git```
          3. Go to HiNT directory, install it by ```$ python setup.py install ```
          
          *** Type ```$ hint``` to test if HiNT successfully installed
        
        * Method 4: Run HiNT in a Docker container (highly recommended)
        
        	``` $ docker pull suwangbio/hint```
        
        	``` $ docker run suwangbio/hint hint```
        
        	See details of the usage on HiNT page at [docker hub](https://hub.docker.com/r/suwangbio/hint) 
        
        ### Download reference files used in HiNT [HERE](https://www.dropbox.com/sh/2ufsyu4wvrboxxp/AABk5-_Fwy7jdM_t0vIsgYf4a?dl=0)
        
        1. Download HiNT references [HERE](https://www.dropbox.com/sh/qas48d7409t2syz/AACk5G2ngZ0vylLXsLFZXif_a?dl=0). Only hg19, hg38 and mm10 are available currently. Unzip it ```$ unzip hg19.zip ```
        2. Download HiNT background matrices [HERE](https://www.dropbox.com/sh/fyxx9u5g5vn57ez/AAAx-DtByKaU6HvTYyEUvCzRa?dl=0). Only hg19, hg38 and mm10 are available currently. Unzip it ```$ unzip hg19.zip ```
        3. Download BWA index files [HERE](https://www.dropbox.com/sh/l004df4108s6d3c/AAB6qtS95mBK_MdDZYlo2V-pa?dl=0). Only hg19, hg38 and mm10 are available currently. Unzip it ```$ unzip hg19.zip ```
        
        ## Quick Start
        
        * Download the test datasets from [HERE](https://www.dropbox.com/sh/z1rceh8ddnsdtj7/AAC0VuDu48eh_RtzKHipztkLa?dl=0)
        
        ### HiNT-PRE
        HiNT pre: Preprocessing Hi-C data. HiNT pre does alignment, contact matrix creation and normalization in one command line.
        
        ```$ hint pre -d /path/to/hic_1.fastq.gz,/path/to/hic_2.fastq.gz -i /path/to/bwaIndex/hg19/hg19.fa --refdir /path/to/refData/hg19 --informat fastq --outformat cooler -g hg19 -n test -o /path/to/outputdir --pairtoolspath /path/to/pairtools --samtoolspath /path/to/samtools --coolerpath /path/to/cooler```
        
        ```$ hint pre -d /path/to/test.bam --refdir /path/to/refData/hg19 --informat bam --outformat juicer -g hg19 -n test -o /path/to/outputdir --pairtoolspath /path/to/pairtools --samtoolspath /path/to/samtools --juicerpath /path/to/juicer_tools.1.8.9_jcuda.0.8.jar```
        
        use ```$ which samtools ``` ```$ which pairtools ``` ```$ which cooler ``` to get the absolute path of these tools, and ```/path/to/juicer_tools.1.8.9_jcuda.0.8.jar``` should be the path where you store this file
        
        see details and more options
        
        ```$ hint pre -h ```
        
        ### HiNT-CNV
        HiNT cnv: prediction of copy number information, as well as segmentation from Hi-C.
        
        ```$ hint cnv -m contactMatrix.cool -f cooler --refdir /path/to/refDir/hg19 -r 50 -g hg19 -n test -o /path/to/outputDir --bicseq /path/to/BICseq2-seg_v0.7.3 -e MboI```
        
        ```$ hint cnv -m /path/to/4DNFIS6HAUPP.mcool::/resolutions/50000 -f cooler --refdir /path/to/refDir/hg38 -r 50 -g hg38 -n HepG2 --bicseq /path/to/BICseq2-seg_v0.7.3 -e DpnII```
        
        ```$ hint cnv -m /path/to/4DNFICSTCJQZ.hic -f juicer --refdir /path/to/refDir/hg38 -r 50 -g hg38 -n HepG2 --bicseq /path/to/BICseq2-seg_v0.7.3 -e DpnII```
        
        ```/path/to/BICseq2-seg_v0.7.3``` should be the path where you store this package
        
        see details and more options
        
        ```$ hint cnv -h ```
        
        ### HiNT-TL
        HiNT tl: interchromosomal translocations and breakpoints detection from
        Hi-C inter-chromosomal interaction matrices.
        
        ```$ hint tl -m /path/to/data_1Mb.cool,/path/to/data_100kb.cool --chimeric /path/to/test_chimeric.sorted.pairsam.gz --refdir /path/to/refDir/hg19 --backdir /path/to/backgroundMatrices/hg19 --ppath /path/to/pairix -f cooler -g hg19 -n test -o /path/to/outputDir```
        
        ```$ hint tl -m /path/to/4DNFIS6HAUPP.mcool::/resolutions/1000000,/path/to/4DNFIS6HAUPP.mcool::/resolutions/100000 -f cooler --refdir /path/to/refDir/hg38 --backdir /path/to/backgroundMatrices/hg38 -g hg38 -n 4DNFICSTCJQZ -c 0.05 --ppath /path/to/pairix -p 12```
        
        ```$ hint tl -m /path/to/4DNFICSTCJQZ.hic -f juicer --refdir /path/to/refData/hg38 --backdir /path/to/backgroundMatrices/hg38 -g hg38 -n 4DNFICSTCJQZ -c 0.05 --ppath /path/to/pairix -p 12 -o HiNTtransl_juicerOUTPUT```
        
        use ```$ which pairix ``` to get the absolute path of pairix
        
        see details and more options
        
        ```$ hint tl -h ```
        
        ## Output of HiNT
        ### HiNT-PRE output
        In the HiNT-PRE output directory, you will find
        
        1. ```jobname.bam``` aligned lossless file in bam format
        2. ```jobname_merged_valid.pairs.gz``` reads pairs in pair format
        3. ```jobname_chimeric.sorted.pairsam.gz``` ambiguous chimeric read pairs used for breakpoint detection in [pairsam](https://github.com/mirnylab/pairtools) format
        4. ```jobname_valid.sorted.deduped.pairsam.gz``` valid read pairs used for Hi-C contact matrix creation in [pairsam](https://github.com/mirnylab/pairtools) format
        5. ```jobname.mcool``` Hi-C contact matrix in [cool](https://github.com/mirnylab/cooler) format
        6. ```jobname.hic``` Hi-C contact matrix in [hic](https://github.com/aidenlab/juicer) format
        
        ### HiNT-CNV output
        In the HiNT-CNV output directory, you will find
        
        1. ```jobname_GAMPoisson.pdf``` the GAM regression result
        2. ```segmentation/jobname_bicsq_allchroms.txt``` CNV segments with log2 copy ratio and p-values in txt file
        3. ```segmentation/jobname_resolution_CNV_segments.png``` figure to visualize CNV segments
        4. ```segmentation/jobname_bicseq_allchroms.l2r.pdf``` figure to visualize log2 copy ration in each bin (bin size = resolution you set)
        5. ```segmentation/other_files``` intermediate files used to run BIC-seq
        6. ```jonname_dataForRegression/*``` data used for regression as well as residuals after removing Hi-C biases
        
        ### HiNT-TL output
        In the HiNT-TL output directory, you will find
        
        1. ```jobname_Translocation_IntegratedBP.txt``` the final integrated translocation breakpoint
        2. ```jobname_chrompairs_rankProduct.txt``` rank product predicted potential translocated chromosome pairs
        3. ```otherFolders``` intermediate files used to identify the translocation breakpoints
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Environment :: Console
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Topic :: Software Development
Description-Content-Type: text/markdown
