# Remove sequences with low complexity

`complex_filter.Rd`

The program removes low complexity sequences, computing the entropy with the observed frequency of dinucleotides.

    complex_filter(input, threshold = 0.5, referenceEntropy = 3.908135)

## Arguments

input | `ShortReadQ` object
threshold | A threshold value computed as the relation of the H of the sequences and the reference H. Default is 0.5
referenceEntropy | Reference entropy. By default, the program uses a value of 3.908, that corresponds to the entropy of the human genome in bits

## Value

Filtered `ShortReadQ` object

## Examples

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

    # create  sequences of different width
    set.seed(10)
    input <- lapply(c(0, 6, 10, 16, 20, 26, 30, 36, 40),
                   function(x) random_seq(1, x))

    # create repetitive 'CG' sequences with length adequante
    # for a total length:
    # input +  CG = 40

    set.seed(10)
    CG <- lapply(c(20, 17, 15, 12, 10, 7, 5, 2, 0),
                function(x) paste(rep('CG', x), collapse = ''))

    # concatenate input and CG
    input  <- mapply('paste', input, CG, sep = '')
    input <- DNAStringSet(input)

    # plot relative entropy (E, Shannon 1948)

    freq <- dinucleotideFrequency(input)
    freq  <- freq /rowSums(freq)
    H <- -rowSums(freq  * log2(freq), na.rm = TRUE)
    H_max <- 3.908135  # max entropy
    plot(H/H_max, type='b', xlab = 'Sequence', ylab= 'E')

    # create qualities of width 40

    set.seed(10)
    input_q <- random_qual(c(30,40), slength = 9, swidth = 40,
                           encod = 'Sanger')

    # create names
    input_names <- seq_names(9)

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

    # apply the filter
    filtered <- complex_filter(my_read)

    # look at the filtered sequences
    sread(filtered)#>   A DNAStringSet instance of length 7
    #>     width seq
    #> [1]    40 GGTGTTCTGGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCG
    #> [2]    40 CGGAATAGGTACAGTCCGCGCGCGCGCGCGCGCGCGCGCG
    #> [3]    40 CAGTAATTGCCTCCCGCAGACGCGCGCGCGCGCGCGCGCG
    #> [4]    40 CGCTGGGTCCGGAATGCCCTTTCTGACGCGCGCGCGCGCG
    #> [5]    40 GCAGCTCCAGCCGTTTGACTTCGCGGAAAGCGCGCGCGCG
    #> [6]    40 TGAACTTAGATTCGGTCCTGAAACACGGTACTTCCACGCG
    #> [7]    40 CAGTCAACCCGCCGACTTGGAGAATTTATTAGCCGGGCGG