adapter_filter {FastqCleaner} | R Documentation

## Remove full and partial adapters from a ShortReadQ object

### Description

This program can remove adapters and partial adapters from 3' and 5', using the functions `isMatchingEndingAt` and `isMatchingStartingAt` of Biostrings. The program extends the methodology of the `trimLRPatterns` function of Biostrings, being also capable of removing adapters present within reads. For a given position in the read, the two Biostrings functions return TRUE when a match is present between a substring of the read and the adapter. As `trimLRPatterns`, adapter_filter also selects region and goes up to the end of the sequence in the corresponding flank as the best match. If several valid matches are found, the function removes the largest subsequence. Adapters can be anchored or not. Two methods are available: one based on the exact matching of the adapter and the reads, and other in an error rate. When indels are allowed, the second method uses the 'edit distance' between the subsequences and the adapter

### Usage

    adapter_filter(input, Lpattern = "", Rpattern = "", method = c("exact",
    "er"), rc.L = FALSE, rc.R = FALSE, first = c("R", "L"),
    with_indels = FALSE, error_rate = 0.2, anchored = TRUE,
    fixed = "subject", remove_zero = TRUE, checks = TRUE,
    min_match_flank = 1L, ...)

### Arguments

`input` | `ShortReadQ` object
`Lpattern` | 5' pattern (character or `DNAString` object)
`Rpattern` | 3' pattern (character or `DNAString` object)
`method` | Method used for trimming. If 'exact' the method is based on the exact match between the possible subsequences of the subject and adapter(s). If 'er' the metod is based on the error-rate between the subsequences, allowing mismatches in any place
`rc.L` | Reverse complement Lpattern? default FALSE
`rc.R` | Reverse complement Rpatter? default FALSE
`first` | trim first right('R') or left ('L') side of sequences when both Lpattern and Rpattern are passed
`with_indels` | Allow indels? This feature is available only when the error_rate is not null
`error_rate` | Error rate (value in the range [0, 1] used for the 'er' method. The error rate is the proportion of mismatches allowed between the adapter and the aligned portion of the subject. For a given adapter A, the number of allowed mismatches between each subsequence s of A and the subject is computed as: error_rate * L_s, where L_s is the length of the subsequence s
`anchored` | Adapter or partial adapter within sequence (anchored = FALSE, default) or only in 3' and 5' terminals? (anchored = TRUE)
`fixed` | Parameter passed to `isMatchingStartingAt` or `isMatchingEndingAt`. Default 'subject', where only ambiguities in the pattern are interpreted as wildcard
`remove_zero` | Remove zero-length sequences? Default TRUE
`checks` | Perform checks? Default TRUE
`min_match_flank` | When a match is found between a subsequence of the subject and the adapter in the corresponding flank, which would be the minimum length of the overlapping region (threshold) used for trimming? Default is 1L (trim when >= 2 base(s) match).
`...` | additional parameters passed to `isMatchingStartingAt` or `isMatchingEndingAt`.

### Value

Filtered `ShortReadQ` object

### Author(s)

Leandro Roser learoser@gmail.com

### Examples

    require('Biostrings')
    require('ShortRead')

    # create 6 sequences of width 43
    set.seed(10)
    input <- random_seq(6, 43)

    # add adapter in 3' reverse complemented. In read 1,
    # it will appear the 5' adapter of read 2 reverse complemented.
    adapter <- 'ATCGACT'

    input <- paste0(input, as.character(reverseComplement(DNAString(adapter))))
    input <- DNAStringSet(input)

    # create qualities of width 50
    set.seed(10)
    input_q <- random_qual(c(30,40), slength = 6, swidth = 50,
    encod = 'Sanger')

    # create names
    input_names <- seq_names(length(input))

    # create ShortReadQ object
    my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names)

    # trim adapter
    filtered <- adapter_filter(my_read, Rpattern = adapter, rc.R = TRUE)

    # look at the filtered sequences
    sread(filtered)

    # adapter in the second strand of paired-end reads is reverse-complemented,
    # with adapter in the end of sequence
    adapterR <- as.character(reverseComplement(DNAString('ATCGACT')))
    adapterR <- DNAString(adapterR)
    inputR <- reverseComplement(input)

    # create qualities of width 50
    set.seed(10)
    inputqR <- random_qual(c(30,40), slength = 6, swidth = 50,
    encod = 'Sanger')

    my_readR <- ShortReadQ(sread = inputR, quality = inputqR, id = input_names)

    # trim adapter
    filteredR <- adapter_filter(my_readR, Rpattern = adapterR)

    # look at the filtered sequences
    sread(filteredR)

[Package FastqCleaner version 1.2.0 Index]