DESCRIPTION

Package: PeakSegPipeline
Type: Package
Title: Genome-Wide Peak Prediction Using Supervised Learning and Optimal Segmentation
Version: 2020.2.13
Author: Toby Dylan Hocking
Maintainer: Toby Dylan Hocking <toby.hocking@r-project.org>
Description: Implementations of supervised machine learning
 algorithms for peak detection in genomic data, as described in
 Hocking and Bourque (2020) <doi:10.1142/9789811215636_0033>.
 Functional Pruning Optimal Partitioning with up-down constraints,
 Hocking et al. (2018) <arXiv:1810.00117>
 is used for single-sample peak prediction
 (independently for each sample and genomic problem).
 A fast heuristic discrete segmentation algorithm,
 Hocking and Bourque (2015) <arXiv:1506.01286>
 is used for joint peak prediction
 (for each peak, jointly using all samples).
License: GPL-3
SystemRequirements: Command line programs
 bedGraphToBigWig, bigWigToBedGraph, bedToBigBed, bigWigInfo
 which can be downloaded from the UCSC Genome Bioinformatics
 web site; detailed instructions also available on
 https://github.com/tdhock/PeakSegPipeline/wiki/FAQ
Depends: R (>= 2.10)
Imports: data.table (>= 1.12.4), 
 PeakError,
 PeakSegJoint (>= 2020.2.13),
 penaltyLearning (>= 2017.07.11),
 nc,
 xtable,
 scales, Matrix, ggplot2,
 PeakSegDisk (>= 2019.12.9),
 future.apply
Suggests: testthat, httr, batchtools, future
URL: https://github.com/tdhock/PeakSegPipeline
BugReports: https://github.com/tdhock/PeakSegPipeline/issues
Remotes: tdhock/PeakSegDisk, tdhock/PeakSegJoint@491bf43571c6c2901bbc83a824e6603555b28d94