data.proc | R Documentation |
data.proc
is a function to process (quality checking, error and
chimeras filtering) data from a NGS run after these have been deconvoluted.
data.proc(
dir.in = NULL,
dir.out = NULL,
bp = 0,
truncQ = 2,
qrep = FALSE,
dada = TRUE,
pool = FALSE,
plot.err = FALSE,
chim = TRUE,
orderBy = "abundance",
verbose = TRUE
)
dir.in |
The directory where the fastq files are located. If NULL (default) an interactive window is used to select a folder |
dir.out |
The directory where to save the results. If NULL (default)
then |
bp |
An integer indicating the expected length (base-pairs) of the reads. If zero (default) no truncation is applied |
truncQ |
Truncate reads at the first instance of a quality score less
than or equal to truncQ when conducting quality filtering. See
|
qrep |
Logical. Should the quality report be generated? (default
|
dada |
Logical. Should the dada analysis be conducted? (default
|
pool |
Logical. Should samples be pooled together prior to sample
inference? (default |
plot.err |
Logical. Whether error rates obtained from |
chim |
Logical. Should the bimera search and removal be performed?
(default |
orderBy |
Character vector specifying how the returned sequence table
should be sorted. Default "abundance". See
|
verbose |
Logical. Whether information on progress should be outputted (default: TRUE) |
data.proc
locates the .fastq files (can be compressed) in the
directory indicated in dir.in
. If the directory path is not provided,
this will be selected using an interactive window.
It is currently limited to single-reads and assumes that adapters, primers and indexes have been already removed and that each file represents a sample.
The data.proc
pipeline is as follows: fastq files are read in. A
filter is applied to truncate reads at the first instance of a quality score
less than truncQ
, remove reads that are of low quality (currently the
threshold is hard-coded and reads are discarded if the expected errors is
higher than 3 - from documentation in the R package dada2
, the
expected errors are calculated from the nominal definition of the quality
score: EE = sum(10^(-Q/10)) - and remove reads that (after truncation) do not
match the target length. A quality report can be (optionally) generated with the
R package ShortReads
to verify the quality of the reads retained
after this step. Reads are then dereplicated. Optionally, the dada (Callahan
et al 2015) algoritm is applied and bimeras are searched and removed with
default settings of the relative functions in the package dada2
. The
sequences that were retained at completion of data.proc
are saved in
fasta files in the subfolder "Final_seqs" and a .csv with a summary of the
number of reads that have been retained in each step is also written. These
two outputs are also returned at the end of the function.
The sequence data handling is done by using functionalities from the packages
dada2
and ShortRead
, so make sure to cite them (in addition to
amplicR
of course!) if you report your results in a paper or report.
Return a list with several elements:
$luniseqsFinal: A list with unique sequences (names) that
were retained at completion of data.proc
and their abundance
(values).
$lsummary: A list where each element is a summary of the
number of reads that were retained in each step. This can be converted in a
data.frame
by running the following
summary <- plyr::join_all(lsummary, by="Sample", type="left")
$stable: The sequence table
$seq_list: The sequences and matching sequence IDs
$call: The function call
Several files are also returned, these include:
Seq_table.csv Sequence table
Seq_list.csv List of sequences and their matching IDs
data.proc.summary.csv A summary of the number of reads that were retained in each step
data.proc.rda R data file containing the list returned by data.proc (see above)
Benjamin J Callahan, Paul J McMurdie, Michael J Rosen, Andrew W Han, Amy J Johnson, Susan P Holmes (2015). DADA2: High resolution sample inference from amplicon data.
dada
, makeSequenceTable
# Select the directory where the example data are stored
example.data <- system.file("extdata", "HTJ", package="amplicR")
# Select a temporary directory where to store the outputs
out <- tempdir()
HTJ.test <- data.proc(example.data, out, bp=140)
# To clean up the temp directory
unlink(out, recursive=TRUE)
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