leading_ins

<p>Check for a large number of leading inserted bases,
if this is the case, TRUE is returned and the PHMM
should be run a second time on the truncated data.</p>

internal

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%).

Cameron Nugent

debar

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

leading_ins function

Check for a large number of leading inserted bases,
if this is the case, TRUE is returned and the PHMM
should be run a second time on the truncated data. — leading_ins

Check for a large number of leading inserted bases,
if this is the case, TRUE is returned and the PHMM
should be run a second time on the truncated data.

leading_ins: Check for a large number of leading inserted bases, if this is the case, TRUE is returned and the PHMM should be run a second time on the truncated data.

Description

Usage

Arguments