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Pre-trained random forest models obtained using preciseTAD

Bioconductor version: Release (3.19)

An experimentdata package to supplement the preciseTAD package containing pre-trained models and the variable importances of each genomic annotation used to build the model parsed into list objects and available in ExperimentHub. In total, preciseTADhub provides access to n=84 random forest classification models optimized to predict TAD/chromatin loop boundary regions and stored as .RDS files. The value, n, comes from the fact that we considered l=2 cell lines {GM12878, K562}, g=2 ground truth boundaries {Arrowhead, Peakachu}, and c=21 autosomal chromosomes {CHR1, CHR2, ..., CHR22} (omitting CHR9). Furthermore, each object is itself a two-item list containing: (1) the model object, and (2) the variable importances for CTCF, RAD21, SMC3, and ZNF143 used to predict boundary regions. Each model is trained via a "holdout" strategy, in which data from chromosomes {CHR1, CHR2, ..., CHRi-1, CHRi+1, ..., CHR22} were used to build the model and the ith chromosome was reserved for testing. See for more detail on the model building strategy.

Author: Spiro Stilianoudakis [aut], Mikhail Dozmorov [aut, cre]

Maintainer: Mikhail Dozmorov <mikhail.dozmorov at>

Citation (from within R, enter citation("preciseTADhub")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

preciseTADhub HTML R Script
Reference Manual PDF


biocViews ExperimentData, ExperimentHub, Genome, PackageTypeData
Version 1.12.0
License MIT + file LICENSE
Depends R (>= 4.1)
Imports ExperimentHub
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Suggests knitr, rmarkdown, markdown, BiocStyle, preciseTAD
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Source Package preciseTADhub_1.12.0.tar.gz
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