An Introduction to FastqCleaner • FastqCleaner

Abstract

FastqCleaner is an interactive application for both quality control and pre-processing of FASTQ files. The interface, based on a Bioconductor back-end, provides diagnostic information for the input and output data and a series of filtering and trimming operations in an interactive framework.

# Launching the application

The interactive application can be launched in R with the following command:

    library('FastqCleaner')
    launch_fqc()

As an alternative method, an RStudio addin (RStudio version 0.99.878 or higher required) installed with the package can be found in the Addins menu (Figure 1). This button allows the direct launch of the application with a single click.

Figure 1: addin of the app in RStudio (RStudio version >= 0.99.878 required)

# Description of the application

The application contains three main panels, as described below.

## 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

### Selecting operations

The “operations menu” (Fig. 2, elements 1 to 8) shows the available operations for file processing:

1. Remove by N(s): removes sequences with a number of Ns (non identified bases) above a selected threshold value

2. Remove low complexity sequences: remove sequences with a value of complexity above a threshold value

3. Remove adapters: removes adapters and partial adapters. Adapter sequences from both ends of single or paired read reads can be selected. Sequences can be reverse-complemented before processing. The program also allows to consider indels and/or anchored adapters.

4. Filter by average quality: computes the average quality of sequences and removes those with a value below a given threshold

5. Trim low quality 3’ tails: removes the 3’ tails of sequences that are below a given threshold

6. Trim 3’ or 5’ by a fixed number: removes a fixed number of bases from the 3’ and/or 5’ ends in the complete set of sequences

7. Filter sequences by length: removes all the sequences with a number of bases below a threshold value

8. Remove duplicated sequences: removes duplicated reads, conserving only one copy of each sequence present in the file

### Loading files

The “file selection menu” (Fig. 2, elements 9 to 17) contains options to handle the input file (type of file, file selection), buttons to run, clear and reset the aplication, and the “advanced” submenu:

9. Single-end reads / paired-end reads: type of input files

10. “FILE” button: to select an input file

11. “RUN!” button: to run the program

12. Output format: to select whether the output file should be compressed (.gz) or not

13. “CLEAR” button: to clear the configuration of the operations menu that have been selected in the first panel, but keeping the input file(s)

14. “RESET” button: to restart the application, removing the input file(s) and the selected configurations

15. Selection notificator: information of the path of the selected file/s

16. Encoding notificator: information of the input file/s encoding

17. Advanced options button: to select a custom encoding and set the number of reads included in each chunk for processing, as described below

### Advanced options

The “advanced options submenu” (Fig. 3) allows to customize some fine aspects of the trimming and filtering process:

Figure 3: Advanced options submenu

18. Encoding menu: in addition to the default approach used by the program (auto-detection of file encoding), users can select a standard encoding from a list

19. Chunk size: the program takes this number of reads at random from the file (default: 1000000), for encoding detection

## 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

# A worked example: FASTQ processing in a nutshell

A sample FASTQ (gz-compressed) file ‘example.fastq.gz’ can be downloaded with the following command in R:

    download.file("https://goo.gl/hb4Kr9", "example_fastq.gz")

A direct download is provided in this link .

A tipical FastqCleaner workflow starts with the input file/s upload (Fig. 6).

Figure 6: File input menu. The example shows a single-end reads case (sample file ‘example.fastq.gz’). For paired-end reads, the selection of the corresponding library type generates an additional button to upload the second file.

The file encoding is automatically detected by the program, but it can also be manually specified in the advanced submenu (Fig. 7). This menu also offers an option to customize the chunk size used for processing.

Figure 7: Advanced submanu

Next, the operations to be performed on the input file are selected from the operations menu (Fig. 8).

Figure 8: Selection of operations. A dialog box shows the input expected for the program. To use a filter, the “Use filter?” checkbox must be checked. A filter in use is indicated with a checkmark in the filter box

The program then starts to run after pressing the “RUN!” button (Fig. 9).

Figure 9: “RUN!” button action

Post-processing results are shown in the second panel (Fig. 10).

Figure 10: Second panel of the app, showing the operations performed and the paths of the input and output files

The type of plot to be displayed and the options for the construction of the plot are available in the third panel (Fig. 11). This panel also show the selected plot/s.

Figure 11: Third panel, showing as example a “CG” content plot. for the output file

To clean the operations, for example to run a different configuration, the “CLEAN” i(Fig. 11) must be pressed. The “RESET” button (Fig. 11) restarts the interface.

Additional help can be found in the “help” button located at the top-right of the app (Fig. 12).

Figure 12: help button. A webpage with information will be open

# Advanced use of the package

FastqCleaner separates the interface from the implementation. In consequence, the processing functions of the package can be used as standard functions from the command line. Most of the functions make intensive use of Biostrings and ShortRead. Trimming and filtering is performed on ShortReadQ objects. A complete documentation for the functions is available in this link

The functions included in the package are described in the following section.

## 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(10)
    # 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

    ##   A DNAStringSet instance of length 6
    ##     width seq
    ## [1]    43 TGGTCCGGTGTTCTGGCGGAATAGGTACAGTCCAGTAATTGCC
    ## [2]    43 TCCCGCAGACGCTGGGTCCGGAATGCCCTTTCTGAGCAGCTCC
    ## [3]    43 AGCCGTTTGACTTCGCGGAAAGTGAACTTAGATTCGGTCCTGA
    ## [4]    43 AACACGGTACTTCCACAGTCAACCCGCCGACTTGGAGAATTTA
    ## [5]    43 TTAGCCGGGCGGTTATTCCCCTAGTGATCTTACTAAGATTTGC
    ## [6]    43 AATACCTAAGCGAAGTGACAGATATGTTCGTCATTCATCCAGG

    # 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

    ##   A DNAStringSet instance of length 6
    ##     width seq
    ## [1]    50 TGGTCCGGTGTTCTGGCGGAATAGGTACAGTCCAGTAATTGCCATCGACT
    ## [2]    50 TCCCGCAGACGCTGGGTCCGGAATGCCCTTTCTGAGCAGCTCCATCGACT
    ## [3]    50 AGCCGTTTGACTTCGCGGAAAGTGAACTTAGATTCGGTCCTGAATCGACT
    ## [4]    50 AACACGGTACTTCCACAGTCAACCCGCCGACTTGGAGAATTTAATCGACT
    ## [5]    50 TTAGCCGGGCGGTTATTCCCCTAGTGATCTTACTAAGATTTGCATCGACT
    ## [6]    50 AATACCTAAGCGAAGTGACAGATATGTTCGTCATTCATCCAGGATCGACT

    # 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)

    ##   A DNAStringSet instance of length 6
    ##     width seq
    ## [1]    50 TGGTCCGGTGTTCTGGCGGAATAGGTACAGTCCAGTAATTGCCATCGACT
    ## [2]    50 TCCCGCAGACGCTGGGTCCGGAATGCCCTTTCTGAGCAGCTCCATCGACT
    ## [3]    50 AGCCGTTTGACTTCGCGGAAAGTGAACTTAGATTCGGTCCTGAATCGACT
    ## [4]    50 AACACGGTACTTCCACAGTCAACCCGCCGACTTGGAGAATTTAATCGACT
    ## [5]    50 TTAGCCGGGCGGTTATTCCCCTAGTGATCTTACTAAGATTTGCATCGACT
    ## [6]    50 AATACCTAAGCGAAGTGACAGATATGTTCGTCATTCATCCAGGATCGACT

    ### 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

    ##   A DNAStringSet instance of length 6
    ##     width seq
    ## [1]    47 TGGTCCGGTGTTCTGGCGGAATAGGTACAGTCCAGTAATTGCCATCG
    ## [2]    47 TCCCGCAGACGCTGGGTCCGGAATGCCCTTTCTGAGCAGCTCCATCG
    ## [3]    47 AGCCGTTTGACTTCGCGGAAAGTGAACTTAGATTCGGTCCTGAATCG
    ## [4]    47 AACACGGTACTTCCACAGTCAACCCGCCGACTTGGAGAATTTAATCG
    ## [5]    47 TTAGCCGGGCGGTTATTCCCCTAGTGATCTTACTAAGATTTGCATCG
    ## [6]    47 AATACCTAAGCGAAGTGACAGATATGTTCGTCATTCATCCAGGATCG

    # 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)

    ##   A DNAStringSet instance of length 6
    ##     width seq
    ## [1]    43 TGGTCCGGTGTTCTGGCGGAATAGGTACAGTCCAGTAATTGCC
    ## [2]    43 TCCCGCAGACGCTGGGTCCGGAATGCCCTTTCTGAGCAGCTCC
    ## [3]    43 AGCCGTTTGACTTCGCGGAAAGTGAACTTAGATTCGGTCCTGA
    ## [4]    43 AACACGGTACTTCCACAGTCAACCCGCCGACTTGGAGAATTTA
    ## [5]    43 TTAGCCGGGCGGTTATTCCCCTAGTGATCTTACTAAGATTTGC
    ## [6]    43 AATACCTAAGCGAAGTGACAGATATGTTCGTCATTCATCCAGG

Documentation of the function

- complex_filter

Removes low complexity sequences, computing the entropy with the dinucleotide frequency: \[H_i = -\sum d_i * log_2(d_i)\]

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

    ##   A DNAStringSet instance 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)

    ##   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

    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

    ##   A DNAStringSet instance 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)

    ##   A DNAStringSet instance 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)

    ##   A DNAStringSet instance 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)

    ##   A DNAStringSet instance 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)

    ##   A DNAStringSet instance 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)

    ##   A DNAStringSet instance 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

    ##   A DNAStringSet instance 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

    ##   A DNAStringSet instance 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)

    ##   A DNAStringSet instance 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

    ##   A DNAStringSet instance 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

    ##   A BStringSet instance of length 6
    ##     width seq
    ## [1]    20 EGFEDIBEH@C@DD?EAAID
    ## [2]    20 I?EGDHIBEG?BHFG,%),4
    ## [3]    20 ACCFBBFCI?I@HBC402+,
    ## [4]    20 CGIAFGB@?AIDF@I14)2+
    ## [5]    20 IB@ACAAC?AGEDDHC?BEB
    ## [6]    20 BH?GFFHHG?DABECFEEDE

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

    # 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)

    ##   A DNAStringSet instance 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 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
    ## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
    ## [25] FALSE FALSE FALSE FALSE FALSE  TRUE

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

    ##  [1] TRUE 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:

Format | Expected range
--- | ---
Sanger | [0, 40]
Illumina 1.8 | [0, 41]
Illumina 1.5 | [0, 40]
Illumina 1.3 | [3, 40]
Solexa | [-5, 40]

Documentation of the function

# Contact information

Mantainer: Leandro Roser - learoser@gmail.com