| Title: | Simultaneous Analysis of Multiplexed Metabarcodes |
| Version: | 0.1.2 |
| Description: | A comprehensive set of wrapper functions for the analysis of multiplex metabarcode data. It includes robust wrappers for 'Cutadapt' and 'DADA2' to trim primers, filter reads, perform amplicon sequence variant (ASV) inference, and assign taxonomy. The package can handle single metabarcode datasets, datasets with two pooled metabarcodes, or multiple datasets simultaneously. The final output is a matrix per metabarcode, containing both ASV abundance data and associated taxonomic assignments. An optional function converts these matrices into 'phyloseq' and 'taxmap' objects. For more information on 'DADA2', including information on how DADA2 infers samples sequences, see Callahan et al. (2016) <doi:10.1038/nmeth.3869>. For more details on the demulticoder R package see Sudermann et al. (2025) <doi:10.1094/PHYTO-02-25-0043-FI>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Depends: | R (≥ 3.0.2) |
| Imports: | furrr, purrr, readr, stringr, tidyr, dplyr, ggplot2, tibble, utils |
| Suggests: | BiocManager, Biostrings, dada2, metacoder, ShortRead, phyloseq, rmarkdown, RcppParallel, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| URL: | https://grunwaldlab.github.io/demulticoder/, https://github.com/grunwaldlab/demulticoder |
| BugReports: | https://github.com/grunwaldlab/demulticoder/issues |
| RoxygenNote: | 7.3.2 |
| Config/Needs/website: | rmarkdown |
| NeedsCompilation: | no |
| Packaged: | 2025-04-30 21:59:31 UTC; marthasudermann |
| Maintainer: | Martha A. Sudermann <sudermam@oregonstate.edu> |
| Author: | Martha A. Sudermann [aut, cre, cph], Zachary S. L Foster [aut], Samantha Dawson [aut], Hung Phan [aut], Jeff H. Chang [aut], Niklaus Grünwald [aut, cph] |
| Repository: | CRAN |
| Date/Publication: | 2025-05-05 09:50:02 UTC |
Combine taxonomic assignments and bootstrap values for each metabarcode into single falsification vector
Description
Combine taxonomic assignments and bootstrap values for each metabarcode into single falsification vector
Usage
assignTax_as_char(tax_results, temp_directory_path, metabarcode)
Arguments
tax_results |
The dataframe containing taxonomic assignments |
Assign taxonomy functions
Description
Assign taxonomy functions
Usage
assign_tax(
analysis_setup,
asv_abund_matrix,
retrieve_files = FALSE,
overwrite_existing = FALSE,
db_rps10 = "oomycetedb.fasta",
db_its = "fungidb.fasta",
db_16S = "bacteriadb.fasta",
db_other1 = "otherdb1.fasta",
db_other2 = "otherdb2.fasta"
)
Arguments
analysis_setup |
An object containing directory paths and data tables,
produced by the |
asv_abund_matrix |
The final abundance matrix containing amplified sequence variants |
retrieve_files |
Logical, TRUE/FALSE whether to copy files from the temp directory to the output directory. Default is FALSE. |
overwrite_existing |
Logical, indicating whether to remove or overwrite
existing files and directories from previous runs. Default is |
db_rps10 |
The reference database for the rps10 metabarcode |
db_its |
The reference database for the ITS metabarcode |
db_16S |
The SILVA 16S-rRNA reference database provided by the user |
db_other1 |
The reference database for other metabarcode 1 (assumes format is like SILVA DB entries) |
db_other2 |
The reference database for other metabarcode 2 (assumes format is like SILVA DB entries) |
Details
At this point, 'DADA2' function assignTaxonomy is used to assign taxonomy to the inferred ASVs.
Value
Taxonomic assignments of each unique ASV sequence
Examples
# Assign taxonomies to ASVs on by metabarcode
analysis_setup <- prepare_reads(
data_directory = system.file("extdata", package = "demulticoder"),
output_directory = tempdir(),
overwrite_existing = TRUE
)
cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)
make_asv_abund_matrix(
analysis_setup,
overwrite_existing = TRUE
)
assign_tax(
analysis_setup,
asv_abund_matrix,
retrieve_files=FALSE,
overwrite_existing = TRUE
)
Assign taxonomy
Description
Assign taxonomy
Usage
assign_taxonomyDada2(
asv_abund_matrix,
temp_directory_path,
metabarcode = "metabarcode",
barcode_params
)
Arguments
asv_abund_matrix |
The final abundance matrix containing amplified sequence variants. |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow |
metabarcode |
The metabarcode used throughout the workflow (applicable options: 'rps10', 'its', 'r16S', 'other1', other2') |
Filter ASV abundance matrix and convert to 'taxmap' and 'phyloseq' objects
Description
Filter ASV abundance matrix and convert to 'taxmap' and 'phyloseq' objects
Usage
convert_asv_matrix_to_objs(
analysis_setup,
min_read_depth = 0,
minimum_bootstrap = 0,
save_outputs = FALSE
)
Arguments
analysis_setup |
An object containing directory paths and
data tables, produced by the |
min_read_depth |
ASV filter parameter. If mean read depth of across all samples is less than this threshold, ASV will be filtered. |
minimum_bootstrap |
Set threshold for bootstrap support value for taxonomic assignments. Below designated minimum bootstrap threshold, taxonomic assignments will be set to N/A |
save_outputs |
Logical, indicating whether to save the resulting phyloseq and 'taxmap' objects. If TRUE, the objects will be saved; if FALSE, they will only be available in the global environment. Default is FALSE. |
Value
ASV matrix converted to 'taxmap' object
Examples
# Convert final matrix to 'taxmap' and phyloseq objects for downstream analysis steps
analysis_setup <- prepare_reads(
data_directory = system.file("extdata", package = "demulticoder"),
output_directory = tempdir(),
overwrite_existing = TRUE
)
cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)
make_asv_abund_matrix(
analysis_setup,
overwrite_existing = TRUE
)
assign_tax(
analysis_setup,
asv_abund_matrix,
retrieve_files=FALSE,
overwrite_existing=TRUE
)
objs<-convert_asv_matrix_to_objs(
analysis_setup
)
Count overlap to see how well the reads were merged
Description
Count overlap to see how well the reads were merged
Usage
countOverlap(data_tables, merged_reads, barcode, output_directory_path)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
merged_reads |
Intermediate merged read RData file |
barcode |
The metabarcode used throughout the workflow (applicable options: 'rps10', 'its', 'r16S', 'other1', other2') |
output_directory_path |
The path to the directory where resulting files are output |
Value
A plot describing how well reads merged and information on overlap between reads
Make ASV sequence matrix
Description
Make ASV sequence matrix
Usage
createASVSequenceTable(merged_reads, orderBy = "abundance", barcode_params)
Arguments
merged_reads |
Intermediate merged read RData file |
orderBy |
(Optional). |
Value
raw_seqtab
Main command to trim primers using 'Cutadapt' and core 'DADA2' functions
Description
Main command to trim primers using 'Cutadapt' and core 'DADA2' functions
Usage
cut_trim(analysis_setup, cutadapt_path, overwrite_existing = FALSE)
Arguments
analysis_setup |
An object containing directory paths and data tables,
produced by the |
cutadapt_path |
Path to the 'Cutadapt' program. |
overwrite_existing |
Logical, indicating whether to remove or overwrite
existing files and directories from previous runs. Default is |
Details
If samples are comprised of two different metabarcodes (like ITS1 and rps10), reads will also be demultiplexed prior to 'DADA2'-specific read trimming steps.
Value
Trimmed reads, primer counts, quality plots, and ASV matrix.
Examples
# Remove remaining primers from raw reads, demultiplex pooled barcoded samples,
# and then trim reads based on specific 'DADA2' parameters
analysis_setup <- prepare_reads(
data_directory = system.file("extdata", package = "demulticoder"),
output_directory = tempdir(),
overwrite_existing = TRUE
)
cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)
Wrapper function for filterAndTrim function from 'DADA2', to be used after
primer trimming
Description
Wrapper function for filterAndTrim function from 'DADA2', to be used after
primer trimming
Usage
filter_and_trim(
output_directory_path,
temp_directory_path,
cutadapt_data_barcode,
barcode_params,
barcode
)
Arguments
output_directory_path |
The path to the directory where resulting files are output |
cutadapt_data_barcode |
Metabarcode-specific FASTQ read files trimmed of primers |
Value
Filtered and trimmed reads
Format ASV abundance matrix
Description
Format ASV abundance matrix
Usage
format_abund_matrix(
data_tables,
asv_abund_matrix,
seq_tax_asv,
output_directory_path,
metabarcode
)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
asv_abund_matrix |
The final abundance matrix containing amplified sequence variants |
seq_tax_asv |
An amplified sequence variants matrix with taxonomic information |
General functions to format user-specified databases
Description
General functions to format user-specified databases
Usage
format_database(
data_tables,
data_path,
output_directory_path,
temp_directory_path,
metabarcode,
db_its,
db_rps10,
db_16S,
db_other1,
db_other2
)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
data_path |
Path to the data directory |
output_directory_path |
The path to the directory where resulting files are output |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow |
metabarcode |
The metabarcode used throughout the workflow (applicable options: 'rps10', 'its', 'r16S', 'other1', other2') |
Value
Formatted database(s) for the specified metabarcode type(s)
An 16S database that has modified headers and is output in the reference_databases folder
Description
An 16S database that has modified headers and is output in the reference_databases folder
Usage
format_db_16S(
data_tables,
data_path,
output_directory_path,
temp_directory_path,
db_16S
)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
data_path |
Path to the data directory |
output_directory_path |
The path to the directory where resulting files are output |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow |
db_16S |
The SILVA 16S rRNA reference database provided by the user |
Value
The SILVA 16S rRNA database with modified headers
An ITS database that has modified headers and is output in the reference_databases folder
Description
An ITS database that has modified headers and is output in the reference_databases folder
Usage
format_db_its(
data_tables,
data_path,
output_directory_path,
temp_directory_path,
db_its
)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
data_path |
Path to the data directory |
output_directory_path |
The path to the directory where resulting files are output |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow |
db_its |
The UNITE ITS reference database provided by the user |
Value
The UNITE ITS database with modified headers
An other, user-specified database that is initially in the format specified by 'DADA2' with header containing taxonomic levels (kingdom down to species, separated by semi-colons)
Description
An other, user-specified database that is initially in the format specified by 'DADA2' with header containing taxonomic levels (kingdom down to species, separated by semi-colons)
Usage
format_db_other1(
data_tables,
data_path,
output_directory_path,
temp_directory_path,
db_other1
)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
data_path |
Path to the data directory |
output_directory_path |
The path to the directory where resulting files are output |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow |
db_other1 |
A reference database other than SILVA, UNITE, or oomyceteDB (assumes format is like SILVA DB entries) |
Value
The database with modified headers
An second user-specified database that is initially in the format specified by 'DADA2' with header containing taxonomic levels (kingdom down to species, separated by semi-colons)
Description
An second user-specified database that is initially in the format specified by 'DADA2' with header containing taxonomic levels (kingdom down to species, separated by semi-colons)
Usage
format_db_other2(
data_tables,
data_path,
output_directory_path,
temp_directory_path,
db_other2
)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
data_path |
Path to the data directory |
output_directory_path |
The path to the directory where resulting files are output |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow |
db_other2 |
A second reference database other than SILVA, UNITE, or oomyceteDB (assumes format is like SILVA DB entries) |
Value
The database with modified headers
Create modified reference rps10 database for downstream analysis
Description
Create modified reference rps10 database for downstream analysis
Usage
format_db_rps10(
data_tables,
data_path,
output_directory_path,
temp_directory_path,
db_rps10
)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
data_path |
Path to the data directory |
output_directory_path |
The path to the directory where resulting files are output |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow |
db_rps10 |
The oomyceteDB rps10 reference database provided by the user |
Value
The oomyceteDB database with modified headers
Retrieve the paths of the filtered and trimmed Fastq files
Description
Retrieve the paths of the filtered and trimmed Fastq files
Usage
get_fastq_paths(data_tables, my_direction, my_primer_pair_id)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
my_direction |
Whether primer is in forward or reverse direction |
my_primer_pair_id |
The specific metabarcode ID |
Get primer counts for reach sample after primer removal and trimming steps
Description
Get primer counts for reach sample after primer removal and trimming steps
Usage
get_post_trim_hits(primer_data, cutadapt_data, output_directory_path)
Arguments
primer_data |
Primer |
cutadapt_data |
FASTQ read files trimmed of primers |
output_directory_path |
The path to the directory where resulting files are output |
Value
Table of read counts across each sample
Get primer counts for reach sample before primer removal and trimming steps
Description
Get primer counts for reach sample before primer removal and trimming steps
Usage
get_pre_primer_hits(primer_data, fastq_data, output_directory_path)
Arguments
primer_data |
Primer |
fastq_data |
A |
output_directory_path |
The path to the directory where resulting files are output |
Value
The number of reads in which the primer is found
The number of reads in which the primer is found
Final inventory of read counts after each step from input to removal of chimeras. This function deals with if you have more than one sample. TODO optimize for one sample
Description
Final inventory of read counts after each step from input to removal of chimeras. This function deals with if you have more than one sample. TODO optimize for one sample
Usage
get_read_counts(
asv_abund_matrix,
temp_directory_path,
output_directory_path,
metabarcode
)
Arguments
asv_abund_matrix |
The final abundance matrix containing amplified sequence variants |
Function to infer ASVs, for multiple loci
Description
Function to infer ASVs, for multiple loci
Usage
infer_asv_command(
output_directory_path,
temp_directory_path,
data_tables,
barcode_params,
barcode
)
Arguments
output_directory_path |
The path to the directory where resulting files are output |
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
Core 'DADA2' function to learn errors and infer ASVs
Description
Core 'DADA2' function to learn errors and infer ASVs
Usage
infer_asvs(
data_tables,
my_direction,
my_primer_pair_id,
barcode_params,
output_directory_path
)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
my_direction |
Whether primer is in forward or reverse direction |
my_primer_pair_id |
The specific metabarcode ID |
output_directory_path |
The path to the directory where resulting files are output |
Value
asv_data
Quality filtering to remove chimeras and short sequences
Description
Quality filtering to remove chimeras and short sequences
Usage
make_abund_matrix(
raw_seqtab,
temp_directory_path,
barcode_params = barcode_params,
barcode
)
Arguments
raw_seqtab |
An RData file containing intermediate read data before chimeras were removed |
Value
asv_abund_matrix The returned final ASV abundance matrix
Make an amplified sequence variant (ASV) abundance matrix for each of the input barcodes
Description
Make an amplified sequence variant (ASV) abundance matrix for each of the input barcodes
Usage
make_asv_abund_matrix(analysis_setup, overwrite_existing = FALSE)
Arguments
analysis_setup |
An object containing directory paths and
data tables, produced by the |
overwrite_existing |
Logical, indicating whether to overwrite existing results. Default is FALSE. |
Details
The function processes data for each unique barcode separately, inferring ASVs, merging reads, and creating an ASV abundance matrix. To do this, the 'DADA2' core denoising alogrithm is used to infer ASVs.
Value
The ASV abundance matrix (asv_abund_matrix)
Examples
# The primary wrapper function for 'DADA2' ASV inference steps
analysis_setup <- prepare_reads(
data_directory = system.file("extdata", package = "demulticoder"),
output_directory = tempdir(),
overwrite_existing = TRUE
)
cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)
make_asv_abund_matrix(
analysis_setup,
overwrite_existing = TRUE
)
Prepare for primmer trimming with 'Cutadapt'. Make new sub-directories and specify paths for the trimmed and untrimmed reads
Description
Prepare for primmer trimming with 'Cutadapt'. Make new sub-directories and specify paths for the trimmed and untrimmed reads
Usage
make_cutadapt_tibble(fastq_data, metadata_primer_data, temp_directory_path)
Arguments
fastq_data |
A |
metadata_primer_data |
A |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow |
Value
Returns a larger data.frame containing paths to temporary read
directories, which is used as input when running 'Cutadapt'
Plots a histogram of read length counts of all sequences within the ASV matrix
Description
Plots a histogram of read length counts of all sequences within the ASV matrix
Usage
make_seqhist(asv_abund_matrix, output_directory_path)
Arguments
asv_abund_matrix |
The final abundance matrix containing amplified sequence variants |
Value
histogram with read length counts of all sequences within ASV matrix
Merge forward and reverse reads
Description
Merge forward and reverse reads
Usage
merge_reads_command(
output_directory_path,
temp_directory_path,
barcode_params,
barcode
)
Arguments
output_directory_path |
The path to the directory where resulting files are output |
Value
merged_reads Intermediate merged read RData file
Take in user's forward and reverse sequences and creates the complement,
reverse, reverse complement of primers in one data.frame
Description
Take in user's forward and reverse sequences and creates the complement,
reverse, reverse complement of primers in one data.frame
Usage
orient_primers(primers_params_path)
Arguments
primers_params_path |
A path to the CSV file that holds the primer information. |
Value
A data.frame with oriented primer information.
Wrapper script for plotQualityProfile after trim steps and primer removal.
Description
Wrapper script for plotQualityProfile after trim steps and primer removal.
Usage
plot_post_trim_qc(
cutadapt_data,
output_directory_path,
n = 5e+05,
barcode_params
)
Arguments
cutadapt_data |
FASTQ read files trimmed of primers |
output_directory_path |
The path to the directory where resulting files are output |
n |
(Optional). Default 500,000. The number of records to sample from the fastq file. |
Value
Quality profiles of reads after primer trimming
Wrapper function for plotQualityProfile function
Description
Wrapper function for plotQualityProfile function
Usage
plot_qc(cutadapt_data, output_directory_path, n = 5e+05, barcode_params)
Arguments
cutadapt_data |
FASTQ read files trimmed of primers |
output_directory_path |
The path to the directory where resulting files are output |
n |
(Optional). Default 500,000. The number of records to sample from the fastq file. |
Value
'DADA2' wrapper function for making quality profiles for each sample
Prepare final ASV abundance matrix
Description
Prepare final ASV abundance matrix
Usage
prep_abund_matrix(cutadapt_data, asv_abund_matrix, data_tables, metabarcode)
Arguments
asv_abund_matrix |
The final abundance matrix containing amplified sequence variants |
metabarcode |
The metabarcode used throughout the workflow (applicable options: 'rps10', 'its', 'r16S', 'other1', other2') |
Read metadata file from user and combine and reformat it, given primer data. Included in a larger function prepare_reads.
Description
Read metadata file from user and combine and reformat it, given primer data. Included in a larger function prepare_reads.
Usage
prepare_metadata_table(metadata_file_path, primer_data)
Arguments
metadata_file_path |
The path to the metadata file. |
primer_data |
Primer |
Value
A data.frame containing the merged metadata and primer data called metadata_primer_data.
Prepare reads for primer trimming using 'Cutadapt'
Description
Prepare reads for primer trimming using 'Cutadapt'
Usage
prepare_reads(
data_directory = "data",
output_directory = tempdir(),
tempdir_path = NULL,
tempdir_id = "demulticoder_run",
overwrite_existing = FALSE
)
Arguments
data_directory |
Directory path where the user has placed raw FASTQ (forward and reverse reads), metadata.csv, and primerinfo_params.csv files. Default is "data". |
output_directory |
User-specified directory for outputs. Default is tempdir(). |
tempdir_path |
Path to a temporary directory. If |
tempdir_id |
ID for temporary directories. The user can provide any helpful ID, whether it be a date or specific name for the run. Default is "demulticoder_run" |
overwrite_existing |
Logical, indicating whether to remove or overwrite
existing files and directories from previous runs. Default is |
Value
A list containing data tables, including metadata, primer sequences to search for based on orientation, paths for trimming reads, and user-defined parameters for all subsequent steps.
Examples
# Pre-filter raw reads and parse metadata and primer_information to prepare
# for primer trimming and filter
analysis_setup <- prepare_reads(
data_directory = system.file("extdata", package = "demulticoder"),
output_directory = tempdir(),
overwrite_existing = TRUE
)
Matching Order Primer Check
Description
Matching Order Primer Check
Usage
primer_check(fastq_data)
Arguments
fastq_data |
A |
Value
None
Run 'DADA2' taxonomy functions for single metabarcode
Description
Run 'DADA2' taxonomy functions for single metabarcode
Usage
process_single_barcode(
data_tables,
temp_directory_path,
output_directory_path,
asv_abund_matrix,
metabarcode = metabarcode,
barcode_params
)
Arguments
data_tables |
The data tables containing the paths to read files, metadata, and metabarcode information with associated primer sequences |
asv_abund_matrix |
The final abundance matrix containing amplified sequence variants |
Takes in the FASTQ files from the user and creates a data.frame with the
paths to files that will be created and used in the future. Included in a
larger 'read_prefilt_fastq' function.
Description
Takes in the FASTQ files from the user and creates a data.frame with the
paths to files that will be created and used in the future. Included in a
larger 'read_prefilt_fastq' function.
Usage
read_fastq(data_directory_path, temp_directory_path)
Arguments
data_directory_path |
The path to the directory containing raw FASTQ (forward and reverse reads), metadata.csv, and primerinfo_params.csv files. |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow. |
Value
A data.frame with the FASTQ file paths, primer orientations and
sequences, and parsed sample names.
Take in user's 'DADA2' parameters and make a data frame for downstream steps
Description
Take in user's 'DADA2' parameters and make a data frame for downstream steps
Usage
read_parameters_table(primers_params_path)
Arguments
primers_params_path |
A path to the CSV file that holds the primer information. |
Value
A data.frame with information on the 'DADA2' parameters.
A function for calling read_fastq, primer_check, and remove_ns functions. This will process and edit the FASTQ and make them ready for the trimming of primers with 'Cutadapt'. Part of a larger 'prepare_reads' function.
Description
A function for calling read_fastq, primer_check, and remove_ns functions. This will process and edit the FASTQ and make them ready for the trimming of primers with 'Cutadapt'. Part of a larger 'prepare_reads' function.
Usage
read_prefilt_fastq(
data_directory_path = data_directory_path,
multithread,
temp_directory_path
)
Arguments
data_directory_path |
The path to the directory containing raw FASTQ (forward and reverse reads), metadata.csv, and primerinfo_params.csv files |
multithread |
(Optional). Default is FALSE.
If TRUE, input files are filtered in parallel via |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow |
Value
Returns filtered reads that have no Ns
Wrapper function for core 'DADA2' filter and trim function for first filtering step
Description
Wrapper function for core 'DADA2' filter and trim function for first filtering step
Usage
remove_ns(fastq_data, multithread, temp_directory_path)
Arguments
fastq_data |
A |
multithread |
(Optional). Default is FALSE.
If TRUE, input files are filtered in parallel via |
temp_directory_path |
User-defined temporary directory to output unfiltered, trimmed, and filtered read directories throughout the workflow |
Value
Return prefiltered reads with no Ns
Core function for running 'Cutadapt'
Description
Core function for running 'Cutadapt'
Usage
run_cutadapt(
cutadapt_path,
cutadapt_data_barcode,
barcode_params,
minCutadaptlength
)
Arguments
cutadapt_path |
A path to the 'Cutadapt' program. |
minCutadaptlength |
Read lengths that are lower than this threshold will be discarded. Default is 0. |
Value
Trimmed read.
Set up directory paths for subsequent analyses
Description
This function sets up the directory paths for subsequent analyses. It checks whether the specified output directories exist or creates them if they don't. The function also provides paths to primer and metadata files within the data directory.
Usage
setup_directories(
data_directory = "data",
output_directory = tempdir(),
tempdir_path = NULL,
tempdir_id = "demulticoder_run"
)
Arguments
data_directory |
Directory path where the user has placed raw FASTQ (forward and reverse reads), metadata.csv, and primerinfo_params.csv files. Default is "data". |
output_directory |
User-specified directory path for outputs. Default is tempdir(). |
tempdir_path |
Path to a temporary directory. If |
tempdir_id |
ID for temporary directories. The user can provide any helpful ID, whether it be a date or specific name for the run. Default is "demulticoder_run". |
Value
A list with paths for data, output, temporary directories, primer, and metadata files.