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debar is an R package designed for denoising sequence
data for the animal DNA barcode marker: cytochrome c oxidase I (COI-5P,
or the five prime portion of COI). The package is designed to detect and
correct insertion and deletion errors within barcode sequences. This is
accomplished through comparison of input sequences against a profile
hidden Markov (PHMM) model using the Viterbi
algorithm and adjustment of the sequence based on the reported
Viterbi path (debar depends on functions from the R package
aphid for the
PHMM strucutre and for running the Viterbi algorithm).
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 7bp 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%). Multiple denoised and censored
reads from a sample can then be combined to obtain denoised barcode.
The debar denoising method is designed to increase the
accuracy of reported barcode sequences. It is therefore best applied
when the accuracy of a barcode is paramount, namely in the generation of
novel barcode sequences. In the case of metabarcoding analysis
debar can allow for denoised halpotypes to be obtained for
operational taxonomic units (OTUs).
The package was initially designed for processing single molecule real-time (SMRT) sequencing outputs produced by the Pacific Biosciences SEQUEL platform. However, the method is barcode-based (as opposed to sequencer-based) and should therefore be robust to COI sequence data of any origin. If you intend to apply debar in the denoising of metabarcode data, it is recommended that you do so after quality filtering and dereplication of reads. Please consult the package vignette for recommended parameters and examples of integration into barcoding and metabarcoding workflows.
The development version of debar can be installed
directly from GitHub. You’ll need to have the R package
devtools installed and loaded. Also note if the
build_vignettes option is set to true, you will need to have the R
package knitr installed.
#install.packages("devtools")
#install.packages("knitr") #required if build_vignettes = TRUE
#library(devtools)
devtools::install_github("CNuge/debar", build_vignettes = TRUE)
library(debar)The package’s vignette contains detailed explanations of the
functions and parameters of the debar denoising pipeline.
The use is encouraged to read this document in order to get oriented and
effectively deploy debar in the denoising of their own
data. Following package installation, the vignette can be accessed from
within R through the following command:
vignette('debar-vignette')A second vignette, with a detailed explination of the denoising process is also included:
vignette('debar-algorithm-details')debar can be used to perform denoising of sequences from
within R. The denoise function can be used to process a
single read and (optionally) its associated quality information as
well.
#read a file of example sequences
#fastq
fastq_example_file = system.file('extdata/coi_sequel_data_subset.fastq', package = 'debar')
data = read_fastq(fastq_example_file)
#denoise a given read
denoised_seq = denoise(data$sequence[[1]],
name = data$header_data[[1]],
quality = data$quality[[1]],
to_file = FALSE)
?denoise # for an exhaustive list of parameter options.
names(denoised_seq) # for list of available object components
This will produce a DNAseq object, from which detailed information related to a given read can be accessed using the dollar sign notation.
A list of sequences for a given haplotype can be denoised using the
denoise_list function. This is an especially useful feature
for tasks such as denoising sequences in an OTU prior to determining the
consensus sequence, or in obtaining common haplotypes for an OTU. The
denoise_list function is parallelized and can be run
acrosss multiple cores (specify the number available with the
cores argument)
# ex_nt_list is an example list of four barcode sequences that contain errors.
ex_out = denoise_list(ex_nt_list, cores = 2)Optionally, when multiple sequences are available from a given sample
(or OTU) they can be denoised as a group. Passing the
keep_flanks=FALSE option to the function will produce
denoised outputs with a common reading frame (leading placeholder Ns are
added as needed). The consensus_sequence function can then
be used to obtain a consensus from the denoised sequences.
ex_out = denoise_list(ex_nt_list, keep_flanks=FALSE)
ex_out #each output individually has some missing information
barcode_seq = consensus_sequence(ex_out)
barcode_seq #aligned through the denoising process, a consensus without missing information can be obtainedDenoising of COI-5P barcode data with debar can be
conducted in a file-to-file fashion using the denoise_file
function.
All a user needs to do is specify the input and output files and as
well as any deviations from the default parameters they wish to apply
(see ?denoise or the manual for exhaustive parameter list).
The sequences in the input file will be denoised and written to the
output file in the specified format. The denoise_file
function accepts barcode data in either fastq or
fasta formats (gzipped (.gz) files are also
permitted). Small example inputs are included with the package.
A complete file can be denoised in a single line of R code, simply
specify the input and output files. When processing fastq files,
debar preserves the phred scores.
Note: running the following example will generate an output file in your current working directory!
#gzipped fastq
gzfastq_example_file = system.file('extdata/coi_sequel_data_subset.fastq.gz', package = 'debar')
denoise_file(gzfastq_example_file, outfile = "example_output.fastq")If you are planning on utilizing debar for large input
files, please consult the package vignette section ‘Parameter
combinations - speed and accuracy trade-offs’ for suggestions on how to
optimize performance when scaling to tens or hundreds of thousands of
sequences.
Initial design and default parameters are based on using
debar to process the circular consensus sequences of single
molecule real-time (SMRT) sequencing outputs produced by the
Pacific Biosciences SEQUEL platform. Despite this, the package is
designed to interface with fastq or fasta files of any origin (although
the developers have yet to quantify performance on other data sources).
In the future we hope to quantify performance and provide informed
hyper-parameter choices for outputs from other sequencing platforms. If
you are interested in beta testing debar on barcode or
metabarcode data from other platforms, please contact Cam, we would be happy to
work with you to optimize debar’s functionality for other
sequencing platforms.
Funding for the development of this software was provided by grants in Bioinformatics and Computational Biology from the Government of Canada through Genome Canada and Ontario Genomics and from the Ontario Research Fund. Funders played no role in the study design or preparation of this software.