Emidio Capriotti, 2016.
  Scripts are licensed under the Creative Commons by NC-SA license.

  PhD-SNPg is a program for the annotation of single nucleotide variants that uses data from the UCSC repository.

  Minimum requirements:
  wget, curl, zcat, scikit-learn.

  Run:
    python2 setup.py install arch_typ

  For Linux 64bit architecture there are two compiled versions:
    - linux.x86_64
    - linux.x86_64.v369
    - linux.x86_64.v385
    - macOSX.arm64
    - macOSX.x86_64

  Installation time depends on the network speed.
  About 30G UCSC files need to be downloaded.

  For light installation:
    python2 setup.py install arch_typ --web

  With the web_install option the PhD-SNPg
  runs without downloading the UCSC data.
  The functionality of the program depends 
  on the network speed.

  Test:
    python2 setup.py test

  For web installation:
    python2 setup.py install --web

  1) Download PhD-SNPg script from github
    - git clone https://github.com/biofold/PhD-SNPg.git

  2) Required python2 library: scikit-learn-0.17
      It is available in tools directory or at
      https://pypi.python.org/simple/scikit-learn/

- Untar the scikit-learn-0.17.tar.gz 
      move in the directory and run
      python2 setup.py install --install-lib=..
      
  3) Required UCSC tools and data:
    - bigWigToBedGraph, twoBitToFa and liftOver from
      http://hgdownload.cse.ucsc.edu/admin/exe
      in ucsc/exe directory

    - For hg19 based predictions:
      hg19.2bit: http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.2bit
      hg19.phyloP46way.primate.bw: http://snps.biofold.org/PhD-SNPg/ucsc/hg19/hg19.phyloP46way.primate.bw	
      hg19.100way.phyloP100way.bw: http://hgdownload.cse.ucsc.edu/goldenPath/hg19/phyloP100way/hg19.100way.phyloP100way.bw
      hg19ToHg38.over.chain.gz: https://hgdownload.cse.ucsc.edu/goldenpath/hg19/liftOver/hg19ToHg38.over.chain.gz
      in ucsc/hg19 directory
      Alterantively hg19 bundle is available at http://snps.biofold.org/PhD-SNPg/ucsc/hg19.tar.gz		

    - For hg38 based predictions:
      hg38.2bit: http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.2bit
      hg38.phyloP7way.bw:  http://hgdownload.cse.ucsc.edu/goldenPath/hg38/phyloP7way/hg38.phyloP7way.bw
      hg38.phyloP100way.bw:  http://hgdownload.cse.ucsc.edu/goldenPath/hg38/phyloP100way/hg38.phyloP100way.bw
      hg38.phyloP470way.bw:  http://hgdownload.cse.ucsc.edu/goldenPath/hg38/phyloP470way/hg38.phyloP470way.bw
      in ucsc/hg38 directory
      Alterantively hg38 bundle is available at http://snps.biofold.org/PhD-SNPg/ucsc/hg38.tar.gz

  PhD-SNPg can take in input a single variation or a file containing multiple single nucleotide variants.
  For web installation append --web at the of all commands.

  - For input file the input can be either: 

    plain tab separated file with 4 columns: chr, position, ref, alt
    python2 predict_variants.py test/test_variants_hg38.tsv -g hg38
   
    vcf file with in the firt 5 columns: chr, position, rsid, ref, alt  
    python2 predict_variants.py test/test_variants_hg19.vcf --vcf -g hg19

  PhD-SNPg returns in output:

    PREDICTION: Pathogenic or Benign
    SCORE: a probabilistic score between 0 and 1. If the score is >0.5 the variants is predicted to be Pathogenic.
    FDR: The false discovery rate associated to higher/lower SCORE.
    PhyloP100: PhyloP100 in the mutated position.
    AvgPhyloP100: Average value of PhyloP100 in a 5-nucleotide window around the mutated position.

    The scores added as extra columns to the input file. An example of output is reported below.

    #CHROM  POS     REF     ALT     CODING  PREDICTION      SCORE   FDR   PhyloP100       AvgPhyloP100
    1       197125161       C       T        Yes    Pathogenic      0.954   0.024   7.361   4.082
    2       31526225        G       A        Yes    Pathogenic      0.792   0.061   1.911   2.799
    3       46998228        T       G        No     Pathogenic      0.990   0.007   7.762   5.511
    4       663761          C       G        No     Benign  0.269   0.068   -0.211  -0.410
    5       74750639        C       T        Yes    Benign  0.006   0.004   -0.074  5.880
    11      121125583       A       G        Yes    Benign  0.001   0.003   -9.071  2.506