{% set version = "1.2.0" %}
{% set name = "epialleleR" %}
{% set bioc = "3.14" %}

package:
  name: 'bioconductor-{{ name|lower }}'
  version: '{{ version }}'
source:
  url:
    - 'https://bioconductor.org/packages/{{ bioc }}/bioc/src/contrib/{{ name }}_{{ version }}.tar.gz'
    - 'https://bioarchive.galaxyproject.org/{{ name }}_{{ version }}.tar.gz'
    - 'https://depot.galaxyproject.org/software/bioconductor-{{ name|lower }}/bioconductor-{{ name|lower }}_{{ version }}_src_all.tar.gz'
  md5: 08f6206c96c6ab6a189da9f192f99b44
build:
  number: 1
  rpaths:
    - lib/R/lib/
    - lib/
# Suggests: RUnit, knitr, rmarkdown
# SystemRequirements: C++17, GNU make
requirements:
  host:
    - 'bioconductor-biocgenerics >=0.40.0,<0.41.0'
    - 'bioconductor-genomeinfodb >=1.30.0,<1.31.0'
    - 'bioconductor-genomicranges >=1.46.0,<1.47.0'
    - 'bioconductor-rhtslib >=1.26.0,<1.27.0'
    - 'bioconductor-summarizedexperiment >=1.24.0,<1.25.0'
    - 'bioconductor-variantannotation >=1.40.0,<1.41.0'
    - 'bioconductor-zlibbioc >=1.40.0,<1.41.0'
    - r-base
    - r-bh
    - r-data.table
    - r-rcpp
    - r-stringi
    - libblas
    - liblapack
  run:
    - 'bioconductor-biocgenerics >=0.40.0,<0.41.0'
    - 'bioconductor-genomeinfodb >=1.30.0,<1.31.0'
    - 'bioconductor-genomicranges >=1.46.0,<1.47.0'
    - 'bioconductor-rhtslib >=1.26.0,<1.27.0'
    - 'bioconductor-summarizedexperiment >=1.24.0,<1.25.0'
    - 'bioconductor-variantannotation >=1.40.0,<1.41.0'
    - 'bioconductor-zlibbioc >=1.40.0,<1.41.0'
    - r-base
    - r-bh
    - r-data.table
    - r-rcpp
    - r-stringi
  build:
    - {{ compiler('c') }}
    - {{ compiler('cxx') }}
    - make
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: Artistic-2.0
  summary: 'Fast, Epiallele-Aware Methylation Reporter'
  description: 'Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls hypermethylated epiallele frequencies at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Other functionality includes computing the empirical cumulative distribution function for per-read beta values, and testing the significance of the association between epiallele methylation status and base frequencies at particular genomic positions (SNPs).'

