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