{% set version = "1.0.0" %}
{% set name = "pram" %}
{% set bioc = "3.9" %}

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 }}/bioconductor-{{ name }}_{{ version }}_src_all.tar.gz'
  md5: bb7c3664804776014818ec6c3c14187b
build:
  number: 1
  rpaths:
    - lib/R/lib/
    - lib/
  noarch: generic
# Suggests: testthat, BiocStyle, knitr, rmarkdown
requirements:
  host:
    - 'bioconductor-biocgenerics >=0.30.0,<0.31.0'
    - 'bioconductor-biocparallel >=1.18.0,<1.19.0'
    - 'bioconductor-genomeinfodb >=1.20.0,<1.21.0'
    - 'bioconductor-genomicalignments >=1.20.0,<1.21.0'
    - 'bioconductor-genomicranges >=1.36.0,<1.37.0'
    - 'bioconductor-iranges >=2.18.0,<2.19.0'
    - 'bioconductor-rsamtools >=2.0.0,<2.1.0'
    - 'bioconductor-rtracklayer >=1.44.0,<1.45.0'
    - 'bioconductor-s4vectors >=0.22.0,<0.23.0'
    - r-base
    - 'r-data.table >=1.11.8'
  run:
    - 'bioconductor-biocgenerics >=0.30.0,<0.31.0'
    - 'bioconductor-biocparallel >=1.18.0,<1.19.0'
    - 'bioconductor-genomeinfodb >=1.20.0,<1.21.0'
    - 'bioconductor-genomicalignments >=1.20.0,<1.21.0'
    - 'bioconductor-genomicranges >=1.36.0,<1.37.0'
    - 'bioconductor-iranges >=2.18.0,<2.19.0'
    - 'bioconductor-rsamtools >=2.0.0,<2.1.0'
    - 'bioconductor-rtracklayer >=1.44.0,<1.45.0'
    - 'bioconductor-s4vectors >=0.22.0,<0.23.0'
    - r-base
    - 'r-data.table >=1.11.8'
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: 'GPL (>= 3)'
  summary: 'Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology.  Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis.  To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets.  This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models.'

