Title: Short Tandem Repeats Analysis of Hotspot Zones
Authors@R: c(person("Philipp", "Hermann", role = c("aut", "cre"), email = "philipp.hermann@jku.at", comment = c(ORCID = "https://orcid.org/0000-0003-4556-6297")),
person("Monika", "Heinzl", role = "aut",   email = "monika.heinzl@edumail.at"),
person("Angelika", "Heissl", role = "ctb"),
person("Irene", "Tiemann-Boege", role = "ctb", comment = c(ORCID = "https://orcid.org/0000-0002-3621-7020")),
person("Andreas", "Futschik", role = "ctb", comment = c(ORCID = "https://orcid.org/0000-0002-7980-0304")))
Author: Philipp Hermann [aut, cre] (<https://orcid.org/0000-0003-4556-6297>),
Description: Searches for short tandem repeats (STR) in a specified region of any genome. This analysis can be expanded such that several regions (chromosomes) are studied. These STRs can be grouped into hotspot as well as flanking regions of user specified width. Hotspots are defined by the double strand break maps from Pratto et al. (2014) <doi:10.1126/science.1256442>. Moreover, the user can also search for a specified motif in a DNAStringSet-object, or a fasta-file, or a specified region of any genome. For an application of STR detections please see Heissl et al. (2018) <doi:10.1101/431841>.
Imports: Biostrings (>= 2.38.4), BSgenome, BSgenome.Hsapiens.UCSC.hg19,

Title: Analysis of Short Tandem Repeat (STR) Massively Parallel
Maintainer: Søren B. Vilsen <svilsen@math.aau.dk>
Description: Loading, identifying, aggregating, manipulating, and analysing short tandem repeat regions of massively parallel sequencing data in forensic genetics. 'STRMPS' can work with the package 'STRaitRazoR' (an R interface to the 'STRaitRazor' commandline tool) for added speed. 'STRaitRazoR' only works on linux and can found at <https://github.com/svilsen/STRaitRazoR>. The analyses and framework implemented in this package relies on the papers of Vilsen et al. (2017) <doi:10.1016/j.fsigen.2017.01.017> and Vilsen et al. (2018) <doi:10.1016/j.fsigen.2018.04.003>. Lastly, note that the parallelisation in the package relies on mclapply() and, thus, speed-ups will only be seen on UNIX based systems.
Depends: R (>= 3.1.0), Biostrings, ShortRead
Imports: methods, utils, IRanges, tidyr, tibble, dplyr, stringr, purrr,