debar: A Post-Clustering Denoiser for COI-5P Barcode Data

The 'debar' sequence processing pipeline is designed for denoising high throughput sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package is designed to detect and correct insertion and deletion errors within sequencer outputs. This is accomplished through comparison of input sequences against a profile hidden Markov model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998, ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted for through the introduction of a placeholder character. Since the PHMM is a probabilistic representation of the COI barcode, corrections are not always perfect. For this reason 'debar' censors base pairs adjacent to reported indel sites, turning them into placeholder characters (default is 7 base pairs in either direction, this feature can be disabled). Testing has shown that this censorship results in the correct sequence length being restored, and erroneous base pairs being masked the vast majority of the time (>95%).

Version: 0.1.0
Depends: R (≥ 3.0.0)
Imports: ape, aphid, seqinr, parallel
Suggests: knitr, rmarkdown, testthat
Published: 2019-12-22
Author: Cameron M. Nugent
Maintainer: Cameron M. Nugent <nugentc at uoguelph.ca>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: debar results

Downloads:

Reference manual: debar.pdf
Vignettes: debar-algorithm-details
debar-vignette
Package source: debar_0.1.0.tar.gz
Windows binaries: r-devel: debar_0.1.0.zip, r-devel-gcc8: debar_0.1.0.zip, r-release: debar_0.1.0.zip, r-oldrel: debar_0.1.0.zip
OS X binaries: r-release: debar_0.1.0.tgz, r-oldrel: debar_0.1.0.tgz

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