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cff-version: 1.2.0
message: 'To cite package "preciseTAD" in publications use:'
type: software
license: MIT
title: 'preciseTAD: preciseTAD: A machine learning framework for precise TAD boundary
  prediction'
version: 1.15.0
doi: 10.32614/CRAN.package.preciseTAD
abstract: 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.
authors:
- family-names: Stilianoudakis
  given-names: Spiro
  email: stilianoudasc@vcu.edu
- family-names: Dozmorov
  given-names: Mikhail
  email: mikhail.dozmorov@gmail.com
repository: https://bioc.r-universe.dev
repository-code: https://github.com/dozmorovlab/preciseTAD
commit: 01a9559f92f9d8b2ceff3a33b14bf92e4ff8b0e5
url: https://github.com/dozmorovlab/preciseTAD
contact:
- family-names: Dozmorov
  given-names: Mikhail
  email: mikhail.dozmorov@gmail.com