Package 'debar' reference manual

Title:	A Post-Clustering Denoiser for COI-5P Barcode Data
Description:	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%).
Authors:	Cameron M. Nugent
Maintainer:	Cameron M. Nugent <[email protected]>
License:	GPL-3
Version:	1.0.1
Built:	2025-02-06 04:11:41 UTC
Source:	https://github.com/cnuge/debar

Translate a sequence and assess it for the presence of stop codons.

Description

A side product of the framing and adjustment functions is that the reading frame of the sequence is established and translation can be conducted with high confidence. If the adjustment did in fact correct indel errors, the translated sequence should feature no stop codons. If stop codons are present this is grounds for rejection as indel errors (or some other issue) persists in the sequence.

Usage

aa_check(x, ...)

## S3 method for class 'DNAseq'
aa_check(x, ..., trans_table = 0, frame_offset = 0)
aa_check(x, ...)

## S3 method for class 'DNAseq'
aa_check(x, ..., trans_table = 0, frame_offset = 0)

Arguments

`x`	A ccs_reads class object.
`...`	Additional arguments to be passed between methods.
`trans_table`	The translation table to use for translating from nucleotides to amino acids. Default is "auto", meaning that the translation table will be inferred from the ccs_reads object's order.
`frame_offset`	The offset to the reading frame to be applied for translation. By default the offset is zero, so the first character in the framed sequence is considered the first nucelotide of the first codon. Passing frame_offset = 1 would offset the sequence by one and therefore make the second character in the framed sequence the the first nucelotide of the first codon.

Details

This test has limitations, as any indels late in the DNA sequence may not lead to stop codons existing. Additionally by default censored translation is used by this function when producing the amino acid sequence, so as to eliminate taxonomic bias against organisms with esoteric translation tables. The likelihood of catching errors is increased if the genetic code corresponding to sequences is known.

Value

a class object of code"ccs_reads"

Examples

#previously called
ex_data = DNAseq(example_nt_string, name = 'ex1')
ex_data =  frame(ex_data)
ex_data = adjust(ex_data)
#run the aa check on the adjusted sequences
ex_data = aa_check(ex_data)
ex_data$aaSeq #view the amino acid sequence
ex_data$stop_codons # Boolean indicating if stop codons in the amino acid sequence

#previously called
ex_data = DNAseq(example_nt_string, name = 'ex1')
ex_data =  frame(ex_data)
ex_data = adjust(ex_data)
#run the aa check on the adjusted sequences
ex_data = aa_check(ex_data)
ex_data$aaSeq #view the amino acid sequence
ex_data$stop_codons # Boolean indicating if stop codons in the amino acid sequence

Adjust the sequences based on the nt path outputs.

Description

Based on the PHMM path generated by the frame function, the sequence is the adjusted. Adjustments are limited to the 657bp region represeneted by the PHMM (and the part of the input sequence matching this region). Censorship can be applied around the corrections. This limits the number of indel errors missed by the PHMM correction algorithm, but comes at a cost of lost DNA sequence. The default of 7 is a conservative paramater meant to lead to the coverage of greater than 95

Usage

adjust(x, ...)

## S3 method for class 'DNAseq'
adjust(x, ..., censor_length = 7, added_phred = "*")
adjust(x, ...)

## S3 method for class 'DNAseq'
adjust(x, ..., censor_length = 7, added_phred = "*")

Arguments

`x`	A DNAseq class object.
`...`	Additional arguments to be passed between methods.
`censor_length`	The number of base pairs in either direction of a PHMM correction to convert to placeholder characters. Default is 7.
`added_phred`	The phred character to use for characters inserted into the original sequence. Default is "*".

Details

If the DNAseq object contains PHRED scores, the PHRED string will be adjusted along with the DNA sequence (corresponding) value removed when a bp removed. The 'added_phred' value indicated the phred chracter to be added to the string when a placeholder nucleotide is added to the string to account for a deletion. Default is "*" which indicates a score of 9 (a relitavely low quality base).

Value

a class object of code"ccs_reads"

Examples

#previously called
ex_data = DNAseq(example_nt_string_errors, name = 'error_adj_example')
ex_data =  frame(ex_data)
#adjust the sequence with default censor length is 7
ex_data = adjust(ex_data)
ex_data$adjusted_sequence #output is a vector, use outseq to build the string
#with a custom censorship size
ex_data = adjust(ex_data, censor_length = 5)
ex_data$adjusted_sequence #less flanking base pairs turned to placeholders
ex_data$adjustment_count #get a count of the number of adjustments applied
#previously called
ex_data = DNAseq(example_nt_string_errors, name = 'error_adj_example')
ex_data =  frame(ex_data)
#adjust the sequence with default censor length is 7
ex_data = adjust(ex_data)
ex_data$adjusted_sequence #output is a vector, use outseq to build the string
#with a custom censorship size
ex_data = adjust(ex_data, censor_length = 5)
ex_data$adjusted_sequence #less flanking base pairs turned to placeholders
ex_data$adjustment_count #get a count of the number of adjustments applied

Censored Translation of a DNA string.

Description

Translate a DNA sequence using the censored translation table, this translates codons for which the amino acids is unambigious across mitochondrial genetic codes across the animal kingdom and does not translate those for which the amino acid varies, but rather outputs a ? in the string.

Usage

censored_translation(dna_str, reading_frame = 1)
censored_translation(dna_str, reading_frame = 1)

Arguments

`dna_str`	The DNA string to be translated.
`reading_frame`	reading frame = 1 means the first bp in the string is the start of the first codon, can pass 1, 2 or 3. For 2 and 3 the first 1 and 2 bp will be dropped from translation respectively.

Take the list of denoised sequences and obtain the consensus sequence.

Description

Take the list of denoised sequences and obtain the consensus sequence.

Usage

consensus_sequence(x)
consensus_sequence(x)

Arguments

`x`	The list of adjusted DNA sequences, post censorship and AA correction

Value

A string containing the consensus nucleotide sequence.

Examples

#denoise list of sequences with the k
ex_out = denoise_list(ex_nt_list, keep_flanks=FALSE)

barcode_seq = consensus_sequence(ex_out)
#denoise list of sequences with the k
ex_out = denoise_list(ex_nt_list, keep_flanks=FALSE)

barcode_seq = consensus_sequence(ex_out)

debar

Description

debar is an R package designed for the identification and removal of insertion and deletion errors from COI-5P DNA barcode data.

Details

debar is built around the DNAseq object, which takes a COI-5P DNA barcode sequence and optionally its associated name and PHRED quality information as input. The package utilizes a nucleotide profile hidden Markov model (PHMM) for the identification of the COI-5P region of an input sequence and the identification and correction of indel errors from within the COI-5P region of the sequence. Indel corrections are by default applied in a conservative fashion, with subsequent censorship of 7 base pairs in either direction of an indel correction to mask most instances where the exact bp corresponding to the indel was not found exactly. Numerous filtering and double check steps are applied, and the package includes functions for input/output for either fasta or fastq formats.

The denoise pipeline is heavily paramaterized so that a user can tailor the denoising execution for their own data structure and goal.

Functions

denoise_file Run the denoise pipeline for each sequence in a specified input file.
denoise Run the denoise pipeline for a specified sequence
read_fasta Read data from a fasta file to a data frame.
read_fastq Read data from a fastq file to a data frame.
write_fasta Write a denoised sequence to the specified fasta file.
write_fastq Write a denoised sequence and associated quality information to the specified fastq file.
DNAseq Builds a DNAseq class object
frame Match a sequence against the COI-5P PHMM using the Viterbi algorithm to establish the reading frame, optional rejection of sequence based on the quality of the match to the PHMM.
adjust Use the PHMM path output corresponding to the sequence to adjust the DNA sequence and remove indels. Optional censorship of sequence around the corrections.
aa_check Translate the adjusted sequence to amino acids and check it for stop codons.
outseq Construct the output data for the given sequence. Optionally can include or exculde sequence data from outside of the COI-5P region (part of sequence that was not denoised).

Data

example_nt_string An example COI-5P sequence with no errors.
example_nt_string_errors An example COI-5P sequence with two indel errors.

Author(s)

Cameron M. Nugent

Run the denoiser pipeline for a sequence read.

Description

This function runs the complete denoising pipeline for a given input sequence and its corresponding name and phred scores. The default behaviour is set to interface with fastq files (standard output for most sequencers).

Usage

denoise(x, ...)

## Default S3 method:
denoise(
  x,
  ...,
  name = character(),
  phred = NULL,
  dir_check = TRUE,
  double_pass = TRUE,
  min_match = 100,
  max_inserts = 400,
  censor_length = 7,
  added_phred = "*",
  adjust_limit = 5,
  ambig_char = "N",
  nt_PHMM = debar::nt_coi_PHMM,
  to_file = FALSE,
  keep_flanks = TRUE,
  keep_phred = TRUE,
  outformat = "fastq",
  terminate_rejects = TRUE,
  outfile = NULL,
  phred_placeholder = "#",
  aa_check = TRUE,
  triple_translate = FALSE,
  trans_table = 0,
  frame_offset = 0,
  append = TRUE
)
denoise(x, ...)

## Default S3 method:
denoise(
  x,
  ...,
  name = character(),
  phred = NULL,
  dir_check = TRUE,
  double_pass = TRUE,
  min_match = 100,
  max_inserts = 400,
  censor_length = 7,
  added_phred = "*",
  adjust_limit = 5,
  ambig_char = "N",
  nt_PHMM = debar::nt_coi_PHMM,
  to_file = FALSE,
  keep_flanks = TRUE,
  keep_phred = TRUE,
  outformat = "fastq",
  terminate_rejects = TRUE,
  outfile = NULL,
  phred_placeholder = "#",
  aa_check = TRUE,
  triple_translate = FALSE,
  trans_table = 0,
  frame_offset = 0,
  append = TRUE
)

Arguments

`x`	A DNA sequence string.
`...`	Additional arguments to be passed between methods.
`name`	An optional character string. Identifier for the sequence.
`phred`	An optional character string. The phred score string corresponding to the nucleotide string. If passed then the input phred scores will be modified along with the nucleotides and carried through to the sequence output. Default = NULL.
`dir_check`	A boolean indicating if both the forward and reverse compliments of a sequence should be checked against the PHMM. Default is TRUE.
`double_pass`	A boolean indicating if a second pass through the Viterbi algorithm should be conducted for sequences that had leading nucleotides not matching the PHMM. This improves the accurate establishment of reading frame and will reduce false rejections by the amino acid check, but this comes at a cost of additional processing time. Default is TRUE.
`min_match`	The minimum number of sequential matches to the PHMM for a sequence to be denoised. Otherwise flag the sequence as a reject.
`max_inserts`	The maximum number of sequention insert states occuring in a sequence (including the flanking regions). If this number is exceeded than the entire read will be discarded if terminate_rejects = TRUE. Default is 400.
`censor_length`	The number of base pairs in either direction of a PHMM correction to convert to placeholder characters. Default is 7.
`added_phred`	The phred character to use for characters inserted into the original sequence.
`adjust_limit`	The maximum number of corrections that can be applied to a sequence read. If this number is exceeded then the entire read is rejected. Default is 3.
`ambig_char`	The character to use for ambigious positions in the sequence that is output to the file. Default is N.
`nt_PHMM`	The profile hidden Markov model against which the sequence should be compared. Default is the full COI-5P nucleotide PHMM (nt_coi_PHMM). A PHMM for a sub-section of the COI-5P region can be generated using the subsetPHMM function of the R package coil, or a novel PHMM can be trained using the R package aphid.
`to_file`	Boolean indicating whether the sequence should be written to a file. Default is TRUE.
`keep_flanks`	Should the regions of the input sequence outside of the barcode region be readded to the denoised sequence prior to outputting to the file. Options are TRUE, FALSE and 'right'. The 'right' option will keep the trailing flank but remove the leading flank. Default is TRUE. False will lead to only the denoised sequence for the 657bp barcode region being output to the file.
`keep_phred`	Should the original PHRED scores be kept in the output? Default is TRUE.
`outformat`	The format of the output file. Options are fasta or fastq (default) format.
`terminate_rejects`	Boolean indicating if analysis of sequences that fail to meet phred quality score or path match thresholds should be terminated early (prior to sequence adjustment and writing to file). Default it true.
`outfile`	The name of the file to output the data to. Default filenames are respectively: denoised.fasta or denoised.fastq.
`phred_placeholder`	The character to input for the phred score line. Default is '#'. Used with write_fastq and keep_phred == FALSE only.
`aa_check`	Boolean indicating whether the amino acid sequence should be generated and assessed for stop codons. Default = TRUE.
`triple_translate`	Boolean indicating whether the amino acid check should include all three reading frames. Default = FALSE.
`trans_table`	The translation table to use for translating from nucleotides to amino acids. Default is 0, meaning that censored translation is performed (amigious codons ignored). Used only when aa_check = TRUE.
`frame_offset`	The offset to the reading frame to be applied for translation. By default the offset is zero, so the first character in the framed sequence is considered the first nucelotide of the first codon. Passing frame_offset = 1 would offset the sequence by one and therefore make the second character in the framed sequence the the first nucelotide of the first codon. Used only when aa_check = TRUE.
`append`	Should the denoised sequence be appended to the output file?(TRUE) Or should the sequence overwrite the output file?(FALSE) Default is TRUE.

Details

Since the pipeline is designed for recieving or outputting either fasta or fastq data, this function is hevaily paramaterized. Note that not all paramaters will affect all use cases (i.e. if your outformat is to a fasta file, then the phred_placeholder paramater is ignored as this option only pertains to fastq outputs). The user is encouraged to read the vignette for a detailed walkthrough of the denoiser pipeline that will help identify the paramaters that relate to their given needs.

Value

a class object of code"DNAseq"

Examples

# Denoise example sequence with default paramaters.
ex_data = denoise(example_nt_string_errors, 
                  name = 'example_sequence_1', 
                  keep_phred = FALSE, 
                  to_file = FALSE)

#fastq data from a file
#previously run
fastq_example_file = system.file('extdata/coi_sequel_data_subset.fastq', 
                                 package = 'debar')
data = read_fastq(fastq_example_file)
#denoise the first sequence in the file
#use a custom censor length and no amino acid check
dn_dat_1 = denoise(x = data$sequence[[1]], 
                    name = data$header[[1]], 
                    phred = data$quality[[1]], 
                    censor_length = 11, 
                    aa_check = FALSE, 
                    to_file = FALSE)
# Denoise example sequence with default paramaters.
ex_data = denoise(example_nt_string_errors, 
                  name = 'example_sequence_1', 
                  keep_phred = FALSE, 
                  to_file = FALSE)

#fastq data from a file
#previously run
fastq_example_file = system.file('extdata/coi_sequel_data_subset.fastq', 
                                 package = 'debar')
data = read_fastq(fastq_example_file)
#denoise the first sequence in the file
#use a custom censor length and no amino acid check
dn_dat_1 = denoise(x = data$sequence[[1]], 
                    name = data$header[[1]], 
                    phred = data$quality[[1]], 
                    censor_length = 11, 
                    aa_check = FALSE, 
                    to_file = FALSE)

Denoise sequence data from a given file.

Description

This function allows for direct input to output exectution of the denoising pipeline. All paramaters for the fasta/fastq input and output functions as well as the denoise pipeline can be passed to this function. Please consult the documentation for those functions for a list of available paramaters. The function will run the denoise pipeline with the specified paramaters for all sequences in the input file, and write the denoised sequences and corresponding header/quality information to the output file. Additionally the function allows for rejected reads to be kept and sequestered to an additional output file (as opposed to being discarded) and also allows for a log file to be produced that tracks several statistics including the execition time, number of denoised reads and number of rejected reads.

Usage

denoise_file(x, ...)

## Default S3 method:
denoise_file(
  x,
  ...,
  outfile = "output.fastq",
  informat = "fastq",
  outformat = "fastq",
  to_file = TRUE,
  log_file = FALSE,
  keep_rejects = FALSE,
  multicore = FALSE
)
denoise_file(x, ...)

## Default S3 method:
denoise_file(
  x,
  ...,
  outfile = "output.fastq",
  informat = "fastq",
  outformat = "fastq",
  to_file = TRUE,
  log_file = FALSE,
  keep_rejects = FALSE,
  multicore = FALSE
)

Arguments

`x`	The name of the file to denoise sequences from.
`...`	Additional arguments to be passed to the denoise and input/output functions.
`outfile`	The name of the file to output sequences to.
`informat`	The format of the file to be denoised. Options are fastq or fasta. Default is fastq.
`outformat`	The format of the output file. Options are fasta or fastq (default) format.
`to_file`	Boolean indicating whether the sequence should be written to a file. Default is TRUE.
`log_file`	Boolean indicating if a log file should be produced. Default is FALSE.
`keep_rejects`	Boolean indicating if the bad reads should be written to a separate file (with the name "rejects_" + outfile). Defaut is FALSE.
`multicore`	An integer specifying the number of cores over which to multithread the denoising process. Default is FALSE, meaning the process is not multithreaded.

Details

Using this function is optimized by the appropriation of the multicore option, which allows a user to specify a number of cores that the denoising process should be multithreaded across. The more cores available, the faster the denoising of the input data. It should be noted that the multithreading relies on the entire fastq file being read into memory, because of this your machine's available ram will need to exceed the size of the unzipped fastq file being denoised. If your file size exceeds the available memory you may want to consider spliting the input into several smaller files and denoising them each with this function (this is a fast solution as the multicore option can be used to speed up denoising). Alternatively, you can depoly the 'denoise' function in an iterative fashion, reading in/denoising and writing only a single fq entry at a time. This would require a much smaller memory footprint, but would be much slower due to the lack of multithreading.

List-to-list denoising of COI barcode sequences.

Description

This function provides a shortcut for running the denoise function on a list of sequences. The to_return option can be used to control whether this function returns a list of sequence strings (default), or a list of DNA seq objects.

Usage

denoise_list(x, to_return = "seq", cores = 1, ...)
denoise_list(x, to_return = "seq", cores = 1, ...)

Arguments

`x`	A list like object of barcode sequences.
`to_return`	Indicate whether a the function should return a list of sequence ('seq') or the full DNAseq object ('DNAseq). Default is ('seq')
`cores`	The number of cores across which to thread the denosiing. Default is 1.
`...`	Additional arguments to pass to the denoise algorithm.

Examples

#denoise a list of sequences
out = denoise_list(ex_nt_list, dir_check = FALSE, double_pass = FALSE)
#denoise and add placehers to outputs 

#return a list of DNAseq objects 
ex_DNAseq_out = denoise_list(ex_nt_list, to_return = 'DNAseq',
 dir_check = FALSE, double_pass = FALSE)

#denoise a list of sequences
out = denoise_list(ex_nt_list, dir_check = FALSE, double_pass = FALSE)
#denoise and add placehers to outputs 

#return a list of DNAseq objects 
ex_DNAseq_out = denoise_list(ex_nt_list, to_return = 'DNAseq',
 dir_check = FALSE, double_pass = FALSE)

Take an input sequence and align both the forward and reverse compliments to the PHMM.

Description

The fuction returns the sequence, DNAbin and Path and score of the optimal orientation. Optimal orientation is determined by the direction with the longer string of consecutive ones in the path

Usage

dir_check(x, nt_PHMM = debar::nt_coi_PHMM)
dir_check(x, nt_PHMM = debar::nt_coi_PHMM)

Arguments

`x`	A DNAseq class object.
`nt_PHMM`	The profile hidden Markov model against which the sequence should be compared. Default is the full COI-5P nucleotide PHMM (nt_coi_PHMM).

Build a DNAseq object from a DNA sequence string.

Description

This can optionally include the DNA sequence's corresponding PHRED quality values and a sequencer identifier as well.

Usage

DNAseq(x = character(), name = character(), phred = NULL)
DNAseq(x = character(), name = character(), phred = NULL)

Arguments

`x`	A nucleotide string. Valid characters within the nucleotide string are: a,t,g,c,-,n. The nucleotide string can be input as upper case, but will be automatically converted to lower case.
`name`	An optional character string. Identifier for the sequence.
`phred`	An optional character string. The phred score string corresponding to the nucleotide string. If passed then the input phred scores will be modified along with the nucleotides and carried through to the sequence output. Default = NULL.

Value

An object of class "DNAseq"

Examples

dat = DNAseq(example_nt_string)
#named DNAseq sequence
dat = DNAseq(example_nt_string, name = "example_seq1")
#components in output DNAseq object:
dat$raw
dat$name
dat = DNAseq(example_nt_string)
#named DNAseq sequence
dat = DNAseq(example_nt_string, name = "example_seq1")
#components in output DNAseq object:
dat$raw
dat$name

An example of a list of four coi5p sequences, each containing indel errors.

Description

An example of a list of four coi5p sequences, each containing indel errors.

Usage

ex_nt_list
ex_nt_list

Format

An object of class list of length 4.

Example coi5p DNA sequence string.

Description

This string of barcode data is used in the package documentation's examples and within the vignette demonstrating how to use the package.

Usage

example_nt_string
example_nt_string

Format

An object of class character of length 1.

Example coi5p DNA sequence string with insertion and deletion errors.

Description

This string of barcode data is used in the package documentation's examples and within the vignette demonstrating how to use the package.

Usage

example_nt_string_errors
example_nt_string_errors

Format

An object of class character of length 1.

Take a DNAseq object and isolate the COI-5P region.

Description

Raw DNA sequence is taken and compared against the nucleotide profile hidden Markov model (PHMM), generating the statistical information later used to apply corrections to the sequence read. The speed of this function and how it operates are dependent on the paramaters chosen. When dir_check == TRUE the function will compare the forward and reverse compliment against the PHMM. Since the PHMM comparison is a computationally intensive process, this option should be set to FALSE if you're data are all forward reads.

Usage

frame(x, ...)

## S3 method for class 'DNAseq'
frame(
  x,
  ...,
  dir_check = TRUE,
  double_pass = TRUE,
  min_match = 100,
  max_inserts = 400,
  terminate_rejects = TRUE,
  nt_PHMM = debar::nt_coi_PHMM
)
frame(x, ...)

## S3 method for class 'DNAseq'
frame(
  x,
  ...,
  dir_check = TRUE,
  double_pass = TRUE,
  min_match = 100,
  max_inserts = 400,
  terminate_rejects = TRUE,
  nt_PHMM = debar::nt_coi_PHMM
)

Arguments

`x`	A DNAseq class object.
`...`	additional arguments to be passed between methods.
`dir_check`	A boolean indicating if both the forward and reverse compliments of a sequence should be checked against the PHMM. Default is TRUE.
`double_pass`	A boolean indicating if a second pass through the Viterbi algorithm should be conducted for sequences that had leading nucleotides not matching the PHMM. This improves the accurate establishment of reading frame and will reduce false rejections by the amino acid check, but this comes at a cost of additional processing time. Default is TRUE.
`min_match`	The minimum number of sequential matches to the PHMM for a sequence to be denoised. Otherwise flag the sequence as a reject.
`max_inserts`	The maximum number of sequention insert states occuring in a sequence (including the flanking regions). If this number is exceeded than the entire read will be labelled for rejection. Default is 400.
`terminate_rejects`	Should a check be made to enusre minimum homology of the input sequence to the PHMM. Makes sure the match conditions are met prior to continuing with the framing of the sequence. If conditions not met then the function is stopped and the sequence labelled for rejection.
`nt_PHMM`	The profile hidden Markov model against which the sequence should be compared. Default is the full COI-5P nucleotide PHMM (nt_coi_PHMM). A PHMM for a sub-section of the COI-5P region can be generated using the subsetPHMM function of the R package coil, or a novel PHMM can be trained using the R package aphid.

Details

The min match and max inserts provide the minimal amount of matching to the PHMM requried for a sequence to not be flagged for rejection. If you are dealing with sequence fragments much shorter than the entire COI-5P region you should lower these values.

Value

a class object of code"DNAseq"

Examples

#previously called
hi_phred = paste0(rep("~~~", 217), collapse="")
dat1 = DNAseq(example_nt_string_errors, name = "err_seq1", phred = hi_phred)
dat1= frame(dat1)
dat1$frame_dat # a labelled list with framing information is produced
dat1$reject == FALSE # the match criteria were met, not labelled for rejection
dat1$data$path #one can call and view the path diagram produced by the PHMM comparison.
#previously called
hi_phred = paste0(rep("~~~", 217), collapse="")
dat1 = DNAseq(example_nt_string_errors, name = "err_seq1", phred = hi_phred)
dat1= frame(dat1)
dat1$frame_dat # a labelled list with framing information is produced
dat1$reject == FALSE # the match criteria were met, not labelled for rejection
dat1$data$path #one can call and view the path diagram produced by the PHMM comparison.

Get the final denoised output sequence for a read.

Description

Get the final denoised output sequence for a read.

Usage

outseq(x, ...)

## S3 method for class 'DNAseq'
outseq(x, ..., keep_flanks = TRUE, ambig_char = "N", adjust_limit = 5)
outseq(x, ...)

## S3 method for class 'DNAseq'
outseq(x, ..., keep_flanks = TRUE, ambig_char = "N", adjust_limit = 5)

Arguments

`x`	A DNAseq class object.
`...`	Additional arguments to be passed between methods.
`keep_flanks`	Should the regions of the input sequence outside of the barcode region be readded to the denoised sequence prior to outputting to the file. Options are TRUE, FALSE and 'right'. The 'right' option will keep the trailing flank but remove the leading flank. Default is TRUE. False will lead to only the denoised sequence for the 657bp barcode region being output to the file.
`ambig_char`	The character to use for ambigious positions in the sequence.
`adjust_limit`	the maximum number of corrections that can be applied to a sequence read. If this number is exceeded then the entire read is masked with ambigious characters. Default is 5.

Value

a class object of code"ccs_reads"

Examples

#previously run
excess_string = paste0("CCCCCC", example_nt_string_errors, 
                       "CCCCCCCC", collapse="")
ex_data = DNAseq(excess_string, name = 'ex1')
ex_data =  frame(ex_data)
ex_data = adjust(ex_data)
#build output sequence with trimmed edges
ex_data = outseq(ex_data, keep_flanks = TRUE)
ex_data$outseq #view the output sequence, edges were reattached
#you will avoid data loss on edge of sequence, but errors in edge, or
#off target sequence will be present in the output
#
#build output sequence with only the COI-5P region
ex_data = outseq(ex_data, keep_flanks = FALSE)
ex_data$outseq #view the output sequence
#Ns added to the front to buffer trimmed region
#Note some sequence is lost due to the strange 
#path match that occurs at the front of the sequence.
ex_data$data$path
#previously run
excess_string = paste0("CCCCCC", example_nt_string_errors, 
                       "CCCCCCCC", collapse="")
ex_data = DNAseq(excess_string, name = 'ex1')
ex_data =  frame(ex_data)
ex_data = adjust(ex_data)
#build output sequence with trimmed edges
ex_data = outseq(ex_data, keep_flanks = TRUE)
ex_data$outseq #view the output sequence, edges were reattached
#you will avoid data loss on edge of sequence, but errors in edge, or
#off target sequence will be present in the output
#
#build output sequence with only the COI-5P region
ex_data = outseq(ex_data, keep_flanks = FALSE)
ex_data$outseq #view the output sequence
#Ns added to the front to buffer trimmed region
#Note some sequence is lost due to the strange 
#path match that occurs at the front of the sequence.
ex_data$data$path

Read in raw data from a fasta file.

Description

Read in raw data from a fasta file.

Usage

read_fasta(x)
read_fasta(x)

Arguments

`x`	The name of the fasta file to read data from.

Examples

fasta_example_file = system.file('extdata/coi_sequel_data_subset.fastq', package = 'debar')
data = read_fasta(fasta_example_file)
fasta_example_file = system.file('extdata/coi_sequel_data_subset.fastq', package = 'debar')
data = read_fasta(fasta_example_file)

Read in raw data from a fastq file.

Description

Read in raw data from a fastq file.

Usage

read_fastq(x, keep_quality = TRUE)
read_fastq(x, keep_quality = TRUE)

Arguments

`x`	The name of the fastq file to read data from.
`keep_quality`	Boolean indicating if the Phred quality scores should be retained in the output dataframe. Default is TRUE.

Examples

#read in an unzipped fastq file
fastq_example_file = system.file('extdata/coi_sequel_data_subset.fastq', 
                                  package = 'debar')
data = read_fastq(fastq_example_file)
#read in a gzipped fastq file and do not keep the phred scores
gz_fastq_example_file = system.file('extdata/coi_sequel_data_subset.fastq', 
                                    package = 'debar')
data2 = read_fastq(gz_fastq_example_file, keep_quality = FALSE)
#read in an unzipped fastq file
fastq_example_file = system.file('extdata/coi_sequel_data_subset.fastq', 
                                  package = 'debar')
data = read_fastq(fastq_example_file)
#read in a gzipped fastq file and do not keep the phred scores
gz_fastq_example_file = system.file('extdata/coi_sequel_data_subset.fastq', 
                                    package = 'debar')
data2 = read_fastq(gz_fastq_example_file, keep_quality = FALSE)

Output the denoised consensus sequence to a fasta file.

Description

Output the denoised consensus sequence to a fasta file.

Usage

write_fasta(x, ...)

## S3 method for class 'DNAseq'
write_fasta(x, ..., outfile = "denoised.fasta", append = TRUE)
write_fasta(x, ...)

## S3 method for class 'DNAseq'
write_fasta(x, ..., outfile = "denoised.fasta", append = TRUE)

Arguments

`x`	A DNAseq class object.
`...`	Additional arguments to be passed between methods.
`outfile`	The name of the file to output the data to. Default is "denoised.fasta".
`append`	Should the ccs consensus sequence be appended to the output file?(TRUE) Or overwrite the file?(FALSE) Default is TRUE.

Value

a class object of code"DNAseq"

Output the denoised sequence to a fastq format with placeholder phred scores.

Description

Output the denoised sequence to a fastq format with placeholder phred scores.

Usage

write_fastq(x, ...)

## S3 method for class 'DNAseq'
write_fastq(
  x,
  ...,
  outfile = "denoised.fastq",
  append = TRUE,
  keep_phred = TRUE,
  phred_placeholder = "#"
)
write_fastq(x, ...)

## S3 method for class 'DNAseq'
write_fastq(
  x,
  ...,
  outfile = "denoised.fastq",
  append = TRUE,
  keep_phred = TRUE,
  phred_placeholder = "#"
)

Arguments

`x`	A DNAseq class object.
`...`	Additional arguments to be passed between methods.
`outfile`	The name of the file to output the data to. Default is "denoised.fasta".
`append`	Should the ccs consensus sequence be appended to the output file?(TRUE) Or overwrite the file?(FALSE) Default is TRUE.
`keep_phred`	Should the original PHRED scores be kept in the output? Default is TRUE.
`phred_placeholder`	The character to input for the phred score line. Default is '#'.

Value

a class object of code"DNAseq"

Package 'debar'

Help Index

Translate a sequence and assess it for the presence of stop codons.

Description

Usage

Arguments

Details

Value

See Also

Examples

Adjust the sequences based on the nt path outputs.

Description

Usage

Arguments

Details

Value

See Also

Examples

Censored Translation of a DNA string.

Description

Usage

Arguments

See Also

Take the list of denoised sequences and obtain the consensus sequence.

Description

Usage

Arguments

Value

See Also

Examples

debar

Description

Details

Functions

Data

Author(s)

Run the denoiser pipeline for a sequence read.

Description

Usage

Arguments

Details

Value

Examples

Denoise sequence data from a given file.

Description

Usage

Arguments

Details

See Also

List-to-list denoising of COI barcode sequences.

Description

Usage

Arguments

See Also

Examples

Take an input sequence and align both the forward and reverse compliments to the PHMM.

Description

Usage

Arguments

Build a DNAseq object from a DNA sequence string.

Description

Usage

Arguments

Value

Examples

An example of a list of four coi5p sequences, each containing indel errors.

Description

Usage

Format

Example coi5p DNA sequence string.

Description

Usage

Format

Example coi5p DNA sequence string with insertion and deletion errors.

Description

Usage

Format

Take a DNAseq object and isolate the COI-5P region.

Description

Usage