Main command to trim primers using Cutadapt and core DADA2 functions
Source:R/cut_primers_trim_reads.R
cut_trim.Rd
Main command to trim primers using Cutadapt and core DADA2 functions
Details
If samples are comprised of two different barcodes (like ITS1 and rps10), reads will also be demultiplexed prior to DADA2 trimming steps.
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(),
tempdir_path = tempdir(),
tempdir_id = "demulticoder_run_temp",
overwrite_existing = TRUE
)
#> Rows: 2 Columns: 23
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): primer_name, forward, reverse
#> dbl (16): minCutadaptlength, maxN, maxEE_forward, maxEE_reverse, truncLen_fo...
#> lgl (4): already_trimmed, count_all_samples, multithread, verbose
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> Rows: 2 Columns: 23
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): primer_name, forward, reverse
#> dbl (16): minCutadaptlength, maxN, maxEE_forward, maxEE_reverse, truncLen_fo...
#> lgl (4): already_trimmed, count_all_samples, multithread, verbose
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> Rows: 4 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): sample_name, primer_name, organism
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> Creating output directory: /tmp/RtmpRgOGJ7/demulticoder_run_temp/prefiltered_sequences
cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)
#> Running Cutadapt 3.5 for its sequence data
#> Read in 2564 paired-sequences, output 1479 (57.7%) filtered paired-sequences.
#> Read in 1996 paired-sequences, output 1215 (60.9%) filtered paired-sequences.
#> Running Cutadapt 3.5 for rps10 sequence data
#> Read in 1830 paired-sequences, output 1429 (78.1%) filtered paired-sequences.
#> Read in 2090 paired-sequences, output 1506 (72.1%) filtered paired-sequences.