{% set version = "1.2.0" %}
{% set name = "BRGenomics" %}
{% set bioc = "3.12" %}

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: ee1b271530cae5872b2151e1a7f97d87
build:
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/
  noarch: generic
# Suggests: BiocStyle, knitr, rmarkdown, testthat, apeglm, remotes, ggplot2, reshape2, Biostrings
requirements:
  host:
    - 'bioconductor-deseq2 >=1.30.0,<1.31.0'
    - 'bioconductor-genomeinfodb >=1.26.0,<1.27.0'
    - 'bioconductor-genomicalignments >=1.26.0,<1.27.0'
    - 'bioconductor-genomicranges >=1.42.0,<1.43.0'
    - 'bioconductor-iranges >=2.24.0,<2.25.0'
    - 'bioconductor-rsamtools >=2.6.0,<2.7.0'
    - 'bioconductor-rtracklayer >=1.50.0,<1.51.0'
    - 'bioconductor-s4vectors >=0.28.0,<0.29.0'
    - 'bioconductor-summarizedexperiment >=1.20.0,<1.21.0'
    - r-base
  run:
    - 'bioconductor-deseq2 >=1.30.0,<1.31.0'
    - 'bioconductor-genomeinfodb >=1.26.0,<1.27.0'
    - 'bioconductor-genomicalignments >=1.26.0,<1.27.0'
    - 'bioconductor-genomicranges >=1.42.0,<1.43.0'
    - 'bioconductor-iranges >=2.24.0,<2.25.0'
    - 'bioconductor-rsamtools >=2.6.0,<2.7.0'
    - 'bioconductor-rtracklayer >=1.50.0,<1.51.0'
    - 'bioconductor-s4vectors >=0.28.0,<0.29.0'
    - 'bioconductor-summarizedexperiment >=1.20.0,<1.21.0'
    - r-base
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: Artistic-2.0
  summary: 'Tools for the Efficient Analysis of High-Resolution Genomics Data'
  description: 'This package provides useful and efficient utilites for the analysis of high-resolution genomic data using standard Bioconductor methods and classes. BRGenomics is feature-rich and simplifies a number of post-alignment processing steps and data handling. Emphasis is on efficient analysis of multiple datasets, with support for normalization and blacklisting. Included are functions for: spike-in normalizing data; generating basepair-resolution readcounts and coverage data (e.g. for heatmaps); importing and processing bam files (e.g. for conversion to bigWig files); generating metaplots/metaprofiles (bootstrapped mean profiles) with confidence intervals; conveniently calling DESeq2 without using sample-blind estimates of genewise dispersion; among other features.'

