First panel

The first panel includes two elements: a dashboard for selection of trimming and filtering operations, and a menu for selection of the input file/s (Fig. 2).

Figure 2: Panel 1, with each compontent indicated with a number. See explanation for each element below

Second panel

The second panel (“file operations” panel, Fig. 4) shows the operations that were sucessfuly performed on the input file after running the program.

Figure 4: File operations panel, with its elements

The panel contains the following elements:

20. Files location: location of input and output files

21. Operations performed: operations perfomed on the input file. Each individual display indicates the number of reads that passed the corresponding filter

Third panel

The third panel ( “live results” panel, Fig. 5) shows interactive diagnostics plots for both input and output files. The program takes a random sample of reads for construction of the plots (default: 10000 reads).

Figure 5: Live results panel

The panel includes the following options in the menu located on the left:

22. Sample size: the sample size used for construction of the plots. Default: 10000 reads

23. Input / output: show diagnostics plots for input or output files?

24. Diagnostics plots: the plot to be shown, that can be one of the following:

    • Per cycle quality: quality plots across reads for each cycle (i.e., sequence position)

    • Per cycle mean quality: average quality across reads per base, for each cycle (i.e., sequence position)

    • Mean quality distribution: Quality distribution, using for the construction of the histogram the mean quality of each read

    • % reads with Phred scores > threshold: % of reads with all the quality values > threshold

    • Per cycle base proportion: Proportion of each base (average across reads) in each cycle. It also shows the proporion of N’s

    • CG content: % CG and % AT (average across reads) for each cycle

    • CG content distribution over all reads: histogram for % reads with a given % CG

    • Read length distribution: % reads vs read length (bp)

    • Read ocurrence distribution: % reads that ocurr at different frequencies values in the file. The plot also includes a table

    • Relative k-mer diversity: unique k-mers / all posible kmers for each cycle

25. Select k-mer size: k-mer size for the k-mers frequency plot

26. Top sequences in duplication level analysis: a list of duplicated sequences, ordered from high to low duplication level, can be desplegated from the “read ocurrence distribution” plot. The number selected here indicates how many sequences should be shown. Note that the frequency of reads are relative to the sample size selected (i.e., fold-times in relation to those reads present only once in the sample)

27. Plot panel

Main functions

• adapter_filter

Based on the Biostrings trimLRPatterns functions. It can remove adapters and partial adapters from the 3’ and 5’ sequence ends. Adapters can be anchored or not. When indels are allowed, the method is based on the “edit distance” of the sequences.

### Examples

require("Biostrings")
require("ShortRead")
require("FastqCleaner")

# create sequences
set.seed(15) 
# nota that the use of set.seed before the call to the 
# random generators allows reproducibility of the
# examples

input <- random_seq(6, 43)
input

## DNAStringSet object of length 6:
##     width seq
## [1]    43 TCATGAAGTACTTTAAGACAAAGCTTCCAGGGGCTTCTGAAGA
## [2]    43 GGGGAACTTAACATAAGGATTAAAGAATTGTATGCCTTAAAAA
## [3]    43 CAGAACCCACAGACAAACGTAATGCACCTTATGTTGGCGCCGC
## [4]    43 TCTGATCCTATCGCTCGGATTGATAATCAATTTAATTGTGAGA
## [5]    43 GATATAGGCGCCACTTGAACGCAGCACGGTGACTAAATGGTTA
## [6]    43 TCATCCGTTTATTTGTTCAGCACGAGCTTGCTGCCTTACTGGA

# 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))


### FULL ADAPTER IN 3'
adapter <- "ATCGACT"

# Create sequences with adapter
my_seqs <- paste0(input, adapter)
my_seqs <- DNAStringSet(my_seqs)
my_seqs

## DNAStringSet object of length 6:
##     width seq
## [1]    50 TCATGAAGTACTTTAAGACAAAGCTTCCAGGGGCTTCTGAAGAATCGACT
## [2]    50 GGGGAACTTAACATAAGGATTAAAGAATTGTATGCCTTAAAAAATCGACT
## [3]    50 CAGAACCCACAGACAAACGTAATGCACCTTATGTTGGCGCCGCATCGACT
## [4]    50 TCTGATCCTATCGCTCGGATTGATAATCAATTTAATTGTGAGAATCGACT
## [5]    50 GATATAGGCGCCACTTGAACGCAGCACGGTGACTAAATGGTTAATCGACT
## [6]    50 TCATCCGTTTATTTGTTCAGCACGAGCTTGCTGCCTTACTGGAATCGACT

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

# trim adapter
filtered <- adapter_filter(my_read, Lpattern = adapter)
sread(filtered)

## DNAStringSet object of length 6:
##     width seq
## [1]    50 TCATGAAGTACTTTAAGACAAAGCTTCCAGGGGCTTCTGAAGAATCGACT
## [2]    50 GGGGAACTTAACATAAGGATTAAAGAATTGTATGCCTTAAAAAATCGACT
## [3]    50 CAGAACCCACAGACAAACGTAATGCACCTTATGTTGGCGCCGCATCGACT
## [4]    50 TCTGATCCTATCGCTCGGATTGATAATCAATTTAATTGTGAGAATCGACT
## [5]    50 GATATAGGCGCCACTTGAACGCAGCACGGTGACTAAATGGTTAATCGACT
## [6]    50 TCATCCGTTTATTTGTTCAGCACGAGCTTGCTGCCTTACTGGAATCGACT

### PARTIAL ADAPTER IN 5'
adapter <- "ATCGACT"
subadapter <- subseq(adapter, 1, 4)

# Create sequences with adapter
my_seqs <- paste0(input, subadapter)
my_seqs <- DNAStringSet(my_seqs)
my_seqs

## DNAStringSet object of length 6:
##     width seq
## [1]    47 TCATGAAGTACTTTAAGACAAAGCTTCCAGGGGCTTCTGAAGAATCG
## [2]    47 GGGGAACTTAACATAAGGATTAAAGAATTGTATGCCTTAAAAAATCG
## [3]    47 CAGAACCCACAGACAAACGTAATGCACCTTATGTTGGCGCCGCATCG
## [4]    47 TCTGATCCTATCGCTCGGATTGATAATCAATTTAATTGTGAGAATCG
## [5]    47 GATATAGGCGCCACTTGAACGCAGCACGGTGACTAAATGGTTAATCG
## [6]    47 TCATCCGTTTATTTGTTCAGCACGAGCTTGCTGCCTTACTGGAATCG

# create ShortReadQ object
my_read <- ShortReadQ(sread = my_seqs, quality = subseq(input_q, 1, 47), 
id = input_names)

# trim adapter
filtered <- adapter_filter(my_read, Rpattern = adapter)
sread(filtered)

## DNAStringSet object of length 6:
##     width seq
## [1]    43 TCATGAAGTACTTTAAGACAAAGCTTCCAGGGGCTTCTGAAGA
## [2]    43 GGGGAACTTAACATAAGGATTAAAGAATTGTATGCCTTAAAAA
## [3]    43 CAGAACCCACAGACAAACGTAATGCACCTTATGTTGGCGCCGC
## [4]    43 TCTGATCCTATCGCTCGGATTGATAATCAATTTAATTGTGAGA
## [5]    43 GATATAGGCGCCACTTGAACGCAGCACGGTGACTAAATGGTTA
## [6]    43 TCATCCGTTTATTTGTTCAGCACGAGCTTGCTGCCTTACTGGA

Documentation of the function

• complex_filter

Removes low complexity sequences, computing the entropy with the dinucleotide frequency: H_i = −∑d_i * log₂(d_i)

where: $d_i = D_i/ \sum_i^n D_i$ represents the frequency of dinucleotides of the sequence i relative to the frequency in the whole pool of sequences.

The relation H_i/H_r between H_i and a reference entropy value H_r is computed, and the obtained relations are compared with a given complexity threshold. By default the program uses a reference entropy of 3.908, that corresponds to the entropy of the human genome in bits, and a complexity threshold of 0.5.

# 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

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)
input

## DNAStringSet object of length 9:
##     width seq
## [1]    40 CGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCG
## [2]    40 TGGTCCCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCG
## [3]    40 GGTGTTCTGGCGCGCGCGCGCGCGCGCGCGCGCGCGCGCG
## [4]    40 CGGAATAGGTACAGTCCGCGCGCGCGCGCGCGCGCGCGCG
## [5]    40 CAGTAATTGCCTCCCGCAGACGCGCGCGCGCGCGCGCGCG
## [6]    40 CGCTGGGTCCGGAATGCCCTTTCTGACGCGCGCGCGCGCG
## [7]    40 GCAGCTCCAGCCGTTTGACTTCGCGGAAAGCGCGCGCGCG
## [8]    40 TGAACTTAGATTCGGTCCTGAAACACGGTACTTCCACGCG
## [9]    40 CAGTCAACCCGCCGACTTGGAGAATTTATTAGCCGGGCGG

# plot relative entropy (E, Shannon 1948)
H_plot <- function(x, H_max = 3.908135) {
    freq <- dinucleotideFrequency(x)
    freq  <- freq /rowSums(freq)
    H <- -rowSums(freq  * log2(freq), na.rm = TRUE)
    plot(H/H_max, type="l", xlab = "Sequence", ylab= "E")
    points(H/H_max, col = "#1a81c2", pch = 16, cex = 2)
}

H_plot(input)

Figure 13: Relative entropy plot for the sequences before the operation

# create qualities of widths 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)
sread(filtered)

## DNAStringSet object 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

H_plot(sread(filtered))

Figure 14: Relative entropy plot for the sequences after the operation

Documentation of the function

• fixed_filter

Removes the specified number of bases from 3’ or 5’.

# create sequences, qualities and names of width 20
set.seed(10)
input <- random_seq(6, 20)
input

## DNAStringSet object of length 6:
##     width seq
## [1]    20 TGGTCCGGTGTTCTGGCGGA
## [2]    20 ATAGGTACAGTCCAGTAATT
## [3]    20 GCCTCCCGCAGACGCTGGGT
## [4]    20 CCGGAATGCCCTTTCTGAGC
## [5]    20 AGCTCCAGCCGTTTGACTTC
## [6]    20 GCGGAAAGTGAACTTAGATT

set.seed(10)
input_q <- random_qual(c(30,40), slength = 6, swidth = 20, 
    encod = "Sanger")

input_names <- seq_names(6)

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

# apply the filter 
filtered3 <- fixed_filter(my_read, trim5 = 5)
sread(filtered3)

## DNAStringSet object of length 6:
##     width seq
## [1]    15 TGGTCCGGTGTTCTG
## [2]    15 ATAGGTACAGTCCAG
## [3]    15 GCCTCCCGCAGACGC
## [4]    15 CCGGAATGCCCTTTC
## [5]    15 AGCTCCAGCCGTTTG
## [6]    15 GCGGAAAGTGAACTT

filtered5 <- fixed_filter(my_read, trim3 = 5)
sread(filtered5)

## DNAStringSet object of length 6:
##     width seq
## [1]    15 CGGTGTTCTGGCGGA
## [2]    15 TACAGTCCAGTAATT
## [3]    15 CCGCAGACGCTGGGT
## [4]    15 ATGCCCTTTCTGAGC
## [5]    15 CAGCCGTTTGACTTC
## [6]    15 AAGTGAACTTAGATT

filtered3and5 <- fixed_filter(my_read, trim3 = 10, trim5 = 5)
sread(filtered3and5)

## DNAStringSet object of length 6:
##     width seq
## [1]     5 TTCTG
## [2]     5 TCCAG
## [3]     5 GACGC
## [4]     5 CTTTC
## [5]     5 GTTTG
## [6]     5 AACTT

Documentation of the function

• length_filter

Removes sequences with a length lower than minimum threshold value or/and higher than a maximum threshold value.

# create  ShortReadQ object width widths between 1 and 60
set.seed(10)
input <- random_length(10, widths = 1:60)
sread(input)

## DNAStringSet object of length 10:
##      width seq
##  [1]    43 TCTGGCGGAATAGGTACAGTCCAGTAATTGCCTCCCGCAGACG
##  [2]     9 CTGGGTCCG
##  [3]    10 GAATGCCCTT
##  [4]    48 TCTGAGCAGCTCCAGCCGTTTGACTTCGCGGAAAGTGAACTTAGATTC
##  [5]    12 GGTCCTGAAACA
##  [6]    55 CGGTACTTCCACAGTCAACCCGCCGACTTGGAGAATTTATTAGCCGGGCGGTTAT
##  [7]     8 TCCCCTAG
##  [8]    54 TGATCTTACTAAGATTTGCAATACCTAAGCGAAGTGACAGATATGTTCGTCATT
##  [9]    39 CATCCAGGCAAGTGCGCGGACATCAATTACCACACAATT
## [10]    19 AAATATGACTCGCGTATCG

# apply the filter, removing sequences with length < 5 or length> 30
filtered <- length_filter(input, rm.min = 5, rm.max = 30)
sread(filtered)

## DNAStringSet object of length 5:
##     width seq
## [1]     9 CTGGGTCCG
## [2]    10 GAATGCCCTT
## [3]    12 GGTCCTGAAACA
## [4]     8 TCCCCTAG
## [5]    19 AAATATGACTCGCGTATCG

Documentation of the function

• n_filter

Wrapper of the ShortRead nFilter function. Removes all those sequences with a number of N’s > a given threshold.

# create 10 sequences of width 20
set.seed(10)
input <- random_seq(10, 20)
input

## DNAStringSet object of length 10:
##      width seq
##  [1]    20 TGGTCCGGTGTTCTGGCGGA
##  [2]    20 ATAGGTACAGTCCAGTAATT
##  [3]    20 GCCTCCCGCAGACGCTGGGT
##  [4]    20 CCGGAATGCCCTTTCTGAGC
##  [5]    20 AGCTCCAGCCGTTTGACTTC
##  [6]    20 GCGGAAAGTGAACTTAGATT
##  [7]    20 CGGTCCTGAAACACGGTACT
##  [8]    20 TCCACAGTCAACCCGCCGAC
##  [9]    20 TTGGAGAATTTATTAGCCGG
## [10]    20 GCGGTTATTCCCCTAGTGAT

# inject N's
set.seed(10)
input <- inject_letter_random(input, how_many_seqs = 1:5,
    how_many = 1:10)
input

## DNAStringSet object of length 10:
##      width seq
##  [1]    20 TGGTCCGGTGTTCTGGCGGA
##  [2]    20 ATAGGTACAGTCCAGTAATT
##  [3]    20 GCCTCCCGCAGACGCTGGGT
##  [4]    20 CCGGAATGCCCTTTCTGAGC
##  [5]    20 AGCTCCAGCCGTTTGACTTC
##  [6]    20 GCGGAAAGTGAACTTAGATT
##  [7]    20 CNGTCNNNAANCNNNNTACN
##  [8]    20 NCCANANTCAACNCGCCNAC
##  [9]    20 TTGGAGNATNTATTNGCNNN
## [10]    20 GCGGTTATTCCCCTAGTGAT

#'  
hist(letterFrequency(input, "N"), breaks = 0:10, 
    main  = "Ns Frequency", xlab = "# Ns",
    col = "#1a81c2")

Figure 15: N’s histogram for the sequences before the filtering operation

# Create qualities, names and ShortReadQ object
set.seed(10)
input_q <- random_qual(10, 20)
input_names <- seq_names(10)
my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names)

# Apply the filter 
filtered <- n_filter(my_read, rm.N = 3)
sread(filtered)

## DNAStringSet object of length 7:
##     width seq
## [1]    20 TGGTCCGGTGTTCTGGCGGA
## [2]    20 ATAGGTACAGTCCAGTAATT
## [3]    20 GCCTCCCGCAGACGCTGGGT
## [4]    20 CCGGAATGCCCTTTCTGAGC
## [5]    20 AGCTCCAGCCGTTTGACTTC
## [6]    20 GCGGAAAGTGAACTTAGATT
## [7]    20 GCGGTTATTCCCCTAGTGAT

hist(letterFrequency(sread(filtered), "N"), 
    main = "Ns distribution", xlab = "",
    col = "#1a81c2")

Figure 16: N’s histogram for the sequences after the filtering operation

Documentation of the function

• qmean_filter

Removes those sequences with quality < a give threshold.

# create 30 sequences of width 20, 15 with low quality and 15 with high quality
set.seed(10)
input <- random_seq(30, 20)

set.seed(10)
my_qual_H <- random_qual(c(30,40), slength = 15, swidth = 20,
    encod = "Sanger")

set.seed(10)
my_qual_L <- random_qual(c(5,30), slength = 15, swidth = 20, 
    encod = "Sanger")
input_q<- c(my_qual_H, my_qual_L)

input_names <- seq_names(30)
my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names)

# Plot of average qualities
qual_plot <- function(x, cutoff) {
q <- alphabetScore(x) / width(x)
plot(q, type="l", xlab = "Sequence", ylab= "Average quality", ylim = c(0, 40))
points(q, col = "#1a81c2", pch = 16, cex = 2)
lines(seq_along(q), rep(cutoff, length(q)), type="l", col = "red", lty=2)
text(length(q), cutoff+2, cutoff)
}

#' Average qualities before
qual_plot(my_read, cutoff = 30)

Figure 17: Average qualities before the filtering operation

# Apply the filter
filtered <- qmean_filter(my_read, minq = 30)

# Average qualities after
qual_plot(filtered, cutoff = 30)

Figure 18: Average qualities after the filtering operation

Documentation of the function

• seq_filter

Removes sequences that match those passed as argument.

# Generate random sequences
set.seed(10)
input <- random_length(30, 3:7)

# Remove sequences that contain the following patterns:
rm.seq  = c("TGGTC", "CGGT", "GTTCT", "ATA")
match_before <- unlist(lapply(rm.seq, function(x) grep(x, 
as.character(sread(input)))))
match_before

## [1] 14 17 27

filtered <- seq_filter(input,rm.seq =  rm.seq)

# Verify that matching sequences were removed
match_after <- unlist(lapply(rm.seq, function(x) {
                grep(x, as.character(sread(filtered)))}))
match_after

## [1] 14 26

Documentation of the function

• trim3q_filter

Removes from the 3’ ends in-tandem nucleotides with a quality < a threshold value.

# Create 6 sequences of width 20
set.seed(10)
input <- random_seq(6, 20)
input

## DNAStringSet object of length 6:
##     width seq
## [1]    20 TGGTCCGGTGTTCTGGCGGA
## [2]    20 ATAGGTACAGTCCAGTAATT
## [3]    20 GCCTCCCGCAGACGCTGGGT
## [4]    20 CCGGAATGCCCTTTCTGAGC
## [5]    20 AGCTCCAGCCGTTTGACTTC
## [6]    20 GCGGAAAGTGAACTTAGATT

# Create Phred+33 qualities of width 15 and paste to qualities of length 
# 5 used for the tails.
# for three of the sequences, put low qualities in tails
set.seed(10)
my_qual <- random_qual(c(30,40), slength = 6, swidth = 15, 
    encod = "Sanger")
set.seed(10)
tails <-   random_qual(c(30,40), slength = 6, swidth = 5, 
    encod = "Sanger")

# Low quality tails in sequences 2, 3 & 4
set.seed(10)
tails[2:4] <- random_qual(c(3, 20), slength = 3, swidth = 5,
    encod = "Sanger")
my_qual <- paste0(my_qual, tails)
input_q <- BStringSet(my_qual)
input_q

## BStringSet object of length 6:
##     width seq
## [1]    20 ECDG@BCBFDGFAFCECDG@
## [2]    20 D@BDIIFHCDGIBHC.*,1&
## [3]    20 EAAIDHI?GECB@HB)))0,
## [4]    20 A@EAHC?BEBFEEDE0/'/+
## [5]    20 @AEDIHFD@B@EGE@IFHCD
## [6]    20 FD?BBIEBFB@HCAAGIBHC

# Watch qualities before filtering
as.matrix(PhredQuality(input_q))

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,]   36   34   35   38   31   33   34   33   37    35    38    37    32    37
## [2,]   35   31   33   35   40   40   37   39   34    35    38    40    33    39
## [3,]   36   32   32   40   35   39   40   30   38    36    34    33    31    39
## [4,]   32   31   36   32   39   34   30   33   36    33    37    36    36    35
## [5,]   31   32   36   35   40   39   37   35   31    33    31    36    38    36
## [6,]   37   35   30   33   33   40   36   33   37    33    31    39    34    32
##      [,15] [,16] [,17] [,18] [,19] [,20]
## [1,]    34    36    34    35    38    31
## [2,]    34    13     9    11    16     5
## [3,]    33     8     8     8    15    11
## [4,]    36    15    14     6    14    10
## [5,]    31    40    37    39    34    35
## [6,]    32    38    40    33    39    34

# Create names and ShortReadQ object
input_names <- seq_names(6)
my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names)

# Apply the filter 
filtered <- trim3q_filter(my_read, rm.3qual = 28)
sread(filtered)

## DNAStringSet object of length 6:
##     width seq
## [1]    20 TGGTCCGGTGTTCTGGCGGA
## [2]    15 ATAGGTACAGTCCAG
## [3]    15 GCCTCCCGCAGACGC
## [4]    15 CCGGAATGCCCTTTC
## [5]    20 AGCTCCAGCCGTTTGACTTC
## [6]    20 GCGGAAAGTGAACTTAGATT

Documentation of the function

• unique_filter

Wrapper of the ShortRead occurrenceFilter function. that removes duplicated sequences.

# Create duplicated sequences
s <- random_seq(10, 10)
s <- sample(s, 30, replace = TRUE)

# Create a ShortReadQ object
q <- random_qual(30, 10)
n <- seq_names(30)
my_read <- ShortReadQ(sread = s, quality = q, id = n)

# Check presence of duplicates
isUnique(as.character(sread(my_read)))

##  [1] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE

# Apply the filter
filtered <- unique_filter(my_read)
isUnique(as.character(sread(filtered)))

## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Documentation of the function

Auxiliary functions

• random_seq

Create a vector of random sequences, for a set of specificied parameters.

Documentation of the function

• random_qual

Create a vector of random qualities for a given encoding and a set of specified parameters.

Documentation of the function

• seq_names

Create a vector of names for a set of sequences.

Documentation of the function

• random_length

Create a set of sequences with random lengths.

Documentation of the function

• inject_letter_random

Inject a character (e.g., ‘N’) at random positions, given a set of parameters.

Documentation of the function

• check_encoding

The function allows to check quality encoding. It detects encodings with the following formats:

Documentation of the function