# This file created by conda-build 3.24.0
# meta.yaml template originally from:
# /opt/recipe, last modified Mon Jul 17 09:29:11 2023
# ------------------------------------------------

package:
  name: bioconductor-precisetad
  version: 1.10.0
source:
  md5: 3d90370e884f035d71c774706abf96f2
  url:
    - https://bioarchive.galaxyproject.org/preciseTAD_1.10.0.tar.gz
    - https://bioconductor.org/packages/3.17/bioc/src/contrib/Archive/preciseTAD/preciseTAD_1.10.0.tar.gz
    - https://bioconductor.org/packages/3.17/bioc/src/contrib/preciseTAD_1.10.0.tar.gz
    - https://depot.galaxyproject.org/software/bioconductor-precisetad/bioconductor-precisetad_1.10.0_src_all.tar.gz
build:
  noarch: generic
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r43hdfd78af_0
requirements:
  host:
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  run:
    - bioconductor-genomicranges >=1.52.0,<1.53.0
    - bioconductor-iranges >=2.34.0,<2.35.0
    - bioconductor-rcgh >=1.30.0,<1.31.0
    - bioconductor-s4vectors >=0.38.0,<0.39.0
    - r-base >=4.3,<4.4.0a0
    - r-caret
    - r-cluster
    - r-dbscan
    - r-dosnow
    - r-e1071
    - r-foreach
    - r-gtools
    - r-modelmetrics
    - r-pbapply
    - r-proc
    - r-prroc
    - r-randomforest
test:
  commands:
    - $R -e "library('preciseTAD')"
about:
  description: preciseTAD provides functions to predict the location of boundaries
    of topologically associated domains (TADs) and chromatin loops at base-level resolution.
    As an input, it takes BED-formatted genomic coordinates of domain boundaries detected
    from low-resolution Hi-C data, and coordinates of high-resolution genomic annotations
    from ENCODE or other consortia. preciseTAD employs several feature engineering
    strategies and resampling techniques to address class imbalance, and trains an
    optimized random forest model for predicting low-resolution domain boundaries.
    Translated on a base-level, preciseTAD predicts the probability for each base
    to be a boundary. Density-based clustering and scalable partitioning techniques
    are used to detect precise boundary regions and summit points. Compared with low-resolution
    boundaries, preciseTAD boundaries are highly enriched for CTCF, RAD21, SMC3, and
    ZNF143 signal and more conserved across cell lines. The pre-trained model can
    accurately predict boundaries in another cell line using CTCF, RAD21, SMC3, and
    ZNF143 annotation data for this cell line.
  home: https://bioconductor.org/packages/3.17/bioc/html/preciseTAD.html
  license: MIT + file LICENSE
  license_file: LICENSE
  summary: 'preciseTAD: A machine learning framework for precise TAD boundary prediction'
extra:
  copy_test_source_files: true
  final: true
