According to your wetlab and sequencing protocol each fastq files can contain one or more sample-replicates, and sequences may or may not contain tags (for demultiplexing) and primer sequences. In the following sections I show 3 different scenarios to obtain the read_count_df data frame, which the input to the filtering steps.
Load library
library(vtamR)Set path to third party programs
# Example for Windows
cutadapt_path <- "C:/Users/Public/cutadapt"
vsearch_path <- "C:/Users/Public/vsearch-2.23.0-win-x86_64/bin/vsearch"
pigz_path <- "C:/Users/Public/pigz-win32/pigz" # optional to speed file compression# Example for Linux
cutadapt_path <- "~/miniconda3/envs/vtam/bin/cutadapt" # v3.4
vsearch_path <- "~/miniconda3/envs/vtam/bin/vsearch" # v2.15.1
pigz_path <- "~/miniconda3/envs/vtam/bin/pigz" # optional to speed file compressionAdapt the path to third party programs according to your installation (See Installation).
If third party programs are in your PATH (See Installation),
simply omit the cutadapt_path, pigz_path,
vsearch_path argument when calling the vtamR
functions.
compress = FALSE In the tutorial we will use
uncompressed files, but see To
compress or not to compress for the best strategy for you. See also
Files to keep
on how to handle the large intermediate files that should be compressed
or deleted once the analyses are complete.
In this scenario, each pair of fasta files correspond to a sample (or a replicate of a sample if you have replicates), so no demultiplexing is necessary.
The reads has been trimmed from all artificial add-ons, such as adapters, tags, indices and also from primers.
Read pairs should be quality filtered,
merged and written to fasta format.
This can be done by the Merge function.
See the help (?Merge) for setting the correct parameters
for quality filtering.
Set input
Merge is the list of the fastq file pairs that should be
merged. The tag_fw, primer_fw,
tag_rv, primer_rv are irrelevant in this case,
just fill them with NA.fastq_dir: Directory containing the input fastq
files.Merge. It is the updated version of fastqinfo,
where fastq file names have been replaced by fasta file names and the
read counts are included for each file.outdir: Name of the output directory.The demo files below are included with the vtamR
package, which is why we use system.file() to access them
in this tutorial. When using your own data, simply provide the file and
directory names (e.g. ~/vtamR/fastq). Make sure there is
no space in the path and file names.
fastq_dir <- system.file("extdata/demo/fastq", package = "vtamR")
fastqinfo <- system.file("extdata/demo/fastqinfo1.csv", package = "vtamR")
outdir <- "vtamR_demo_case1"
merged_dir <- file.path(outdir, "merged")Merge fastq file pairs and quality filter reads
sampleinfo_df <- Merge(fastqinfo,
fastq_dir=fastq_dir,
vsearch_path=vsearch_path, # can be omitted if VSEARCH is in the PATH
outdir=merged_dir,
compress=FALSE)Dereplicate
The fasta files produced by Merge can be read to a data
frame and be dereplicated by the
Dereplicate function. See the help
(?Dereplicate) and tutorial more more
information.
outfile <- file.path(outdir, "1_before_filter.csv")
read_count_df <- Dereplicate(sampleinfo_df,
dir=merged_dir,
outfile=outfile)This is one of the most frequent case. Each pair of fasta files correspond to a sample (or a replicate of a sample if you have replicates), so no demultiplexing is necessary.
The reads has been trimmed from all artificial add-ons, such as adapters, tags, BUT they still have the primers.
Read pairs should be quality filtered,
merged and written to fasta format by
Merge function as in the previous section.
Then the TrimPrimer function will trim the
primers from the reads. See the help (?TrimPrimer)
for setting the correct parameters for primer trimming.
Set input
Merge is the list of the fastq file
pairs that should be merged. The primer_fw,
primer_rv columns are irrelevant in this case, just fill
them with NA.fastq_dir: Directory containing the input fastq
files.Merge. It is the updated version of
fastqinfo, where fastq file names have been replaced by
fasta file names.fasta_dir: Directory containing the input fasta files
for TrimPrimer. This directory is created by
Merge.check_reverse is TRUE, TrimPrimer
checks the reverse complementary strand as well.sampleinfo_df: is updated version of fastainfo. This
data frame and the files listed in it are the input for
Dereplicate.outdir: Name of the output directory.The demo files below are included with the vtamR
package, which is why we use system.file() to access them
in this tutorial. When using your own data, simply provide the file and
directory names (e.g. ~/vtamR/fastq). Make sure there is
no space in the path and file names.
fastq_dir <- system.file("extdata/demo/fastq", package = "vtamR")
fastqinfo <- system.file("extdata/demo/fastqinfo2.csv", package = "vtamR")
outdir <- "vtamR_demo_case2"
merged_dir <- file.path(outdir, "merged")Merge fastq file pairs and quality filter reads
fastainfo_df <- Merge(fastqinfo,
fastq_dir=fastq_dir,
vsearch_path=vsearch_path, # can be omitted if VSEARCH is in the PATH
outdir=merged_dir,
compress=FALSE)Trim primers
demultiplexed_dir <- file.path(outdir, "demultiplexed")
sampleinfo_df <- TrimPrimer(fastainfo_df,
fasta_dir=merged_dir,
outdir=demultiplexed_dir,
cutadapt_path=cutadapt_path, # can be omitted if CUTADAPT is in the PATH
vsearch_path=vsearch_path, # can be omitted if VSEARCH is in the PATH
check_reverse=T,
primer_to_end=F,
compress=FALSE)Dereplicate
The fasta files produced by TrimPrimer can be read to a
data frame and be dereplicated by the
Dereplicate function. See the help
(?Dereplicate) and tutorial more more
information.
outfile <- file.path(outdir, "1_before_filter.csv")
read_count_df <- Dereplicate(sampleinfo_df,
dir=demultiplexed_dir,
outfile=outfile)