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README.md
predict_varfile.py
predict_variants.py
setup.py

README.md

PhD-SNPg

INTRODUCTION

  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.

INSTALLATION

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

  Run:
    python setup.py install arch_typ

  For Linux 64bit architecture there are two compiled versions:
    - linux.x86_64
    - linux.x86_64.v287
    - macOSX.x86_64

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

  For light installation:
    python 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:
    python setup.py test

  For web installation:
    python setup.py test --web

MANUAL INSTALLATION

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

  2) Required python 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
      python setup.py install --install-lib=..
      
  3) Required UCSC tools and data:
    - bigWigToBedGraph and twoBitToFa 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
      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.phastCons7way.bw http://hgdownload.cse.ucsc.edu/goldenPath/hg38/phastCons7way/hg38.phastCons7way.bw
      hg38.phastCons100way.bw http://hgdownload.cse.ucsc.edu/goldenPath/hg38/phastCons100way/hg38.phastCons100way.bw
      in ucsc/hg38 directory
      Alterantively hg38 bundle is available at http://snps.biofold.org/PhD-SNPg/ucsc/hg38.tar.gz

HOW TO RUN

  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 single variants use the option -c:
    python predict_variants.py chr7,158715219,A,G -g hg19 -c

  - For input file the input can be either: 

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

OUTPUT

  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 7-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       45331676        G       A       Yes     Pathogenic      0.990   0.022   7.723   3.313
    1       237634938       G       T       Yes     Benign  0.005   0.011   -4.248  5.939
    2       26461838        G       A       Yes     Benign  0.011   0.022   -0.064  6.116
    2       166009835       A       G       Yes     Benign  0.176   0.072   0.591   4.188
    2       174753570       G       C       Yes     Pathogenic      0.777   0.087   -1.937  5.439
    5       44305045        G       A       Yes     Pathogenic      0.985   0.026   1.655   3.964