# An Introduction To ngsReports
Date: 2023-10-24

Christopher Ward1, Hien To2 and Stevie Pederson3,4,5*

1School of Biological Sciences, University of Adelaide
2Bioinformatics Hub, University of Adelaide
3Black Ochre Data Laboratories, Telethon Kids Institute, Adelaide, Australia
4Dame Roma Mitchell Cancer Researc Laboratories, University of Adelaide
5John Curtin School of Medical Research, Australian National University

* stephen.pederson.au@gmail.com

#### 24 October 2023

#### Package

ngsReports 2.4.0

# Contents

# 1 Introduction

The package `ngsReports` is designed to bolt into data processing pipelines and produce combined plots for multiple FastQC reports generated across an entire set of libraries or samples. The primary functionality of the package is parsing FastQC reports, with import methods also implemented for log files produced by tools as as `STAR`, `hisat2` and others. In addition to parsing files, default plotting methods are implemented. Plots applied to a single file will replicate the default plots from `FastQC` 11 1 https://www.bioinformatics.babraham.ac.uk/projects/fastqc/, whilst methods applied to multiple FastQC reports summarise these and produce a series of custom plots.

Plots are produced as standard ggplot2 objects, with an interactive option available using plotly. As well as custom summary plots, tables of read counts and the like can also be easily generated.

# 2 Basic Usage

## 2.1 Using the Shiny App

In addition to the usage demonstrated below, a `shiny` app has been developed for interactive viewing of FastQC reports. This can be installed using:

    remotes::install_github("UofABioinformaticsHub/shinyNgsreports")

A vignette for this app will be installed with the `shinyNgsreports` package.

## 2.2 The default report

In it’s simplest form, a default summary report can be generated simply by specifying a directory containing the output from FastQC and calling the function `writeHtmlReport()`.

    library(ngsReports)

    fileDir <- file.path("path", "to", "your", "FastQC", "Reports")
    writeHtmlReport(fileDir)

This function will transfer the default template to the provided directory and produce a single `.html` file containing interactive summary plots of any FastQC output found in the directory. FastQC output can be `*fastqc.zip` files or the same files extracted as individual directories.

The default template is provided as `ngsReports_Fastqc.Rmd` in the package directory . This template can be easily modified and supplied as an alternate template to the above function using your modified file as the template RMarkdown file.

    altTemplate <- file.path("path", "to", "your", "new", "template.Rmd")
    writeHtmlReport(fileDir, template = altTemplate)

# 3 Advanced Usage

## 3.1 Classes Defined in the Package

The package `ngsReports` introduces two main `S4` classes:

- `FastqcData` & `FastqcDataList`

`FastqcData` objects hold the parsed data from a single report as generated by the stand-alone tool `FastQC`. These are then extended into lists for more than one file as a `FastqcDataList`. For most users, the primary class of interest will be the `FastqcDataList`.

## 3.2 Loading FastQC Data Into `R`

To load a set of `FastQC` reports into `R` as a `FastqcDataList`, specify the vector of file paths, then call the function `FastqcDataList()`. In the rare case you’d like an individual file, this can be performed by calling `FastqcData()` on an individual file, or subsetting the output from `FastqcDataList()` using the `[[]]` operator as with any list object.

    fileDir <- system.file("extdata", package = "ngsReports")
    files <- list.files(fileDir, pattern = "fastqc.zip$", full.names = TRUE)
    fdl <- FastqcDataList(files)

From here, all FastQC modules can be obtained as a `tibble` (i.e. `data.frame`) using the function `getModule()` and choosing one of the following modules:

- `Summary` (The PASS/WARN/FAIL status for each module)
- `Basic_Statistics`
- `Per_base_sequence_quality`
- `Per_sequence_quality_scores`
- `Per_base_sequence_content`
- `Per_sequence_GC_content`
- `Per_base_N_content`
- `Sequence_Length_Distribution`
- `Sequence_Duplication_Levels`
- `Overrepresented_sequences`
- `Adapter_Content`
- `Kmer_Content`
- `Per_tile_sequence_quality`

    getModule(fdl[[1]], "Summary")

    ## # A tibble: 12 × 3
    ##    Filename      Status Category
    ##    <chr>         <chr>  <chr>
    ##  1 ATTG_R1.fastq PASS   Basic Statistics
    ##  2 ATTG_R1.fastq FAIL   Per base sequence quality
    ##  3 ATTG_R1.fastq WARN   Per tile sequence quality
    ##  4 ATTG_R1.fastq PASS   Per sequence quality scores
    ##  5 ATTG_R1.fastq FAIL   Per base sequence content
    ##  6 ATTG_R1.fastq FAIL   Per sequence GC content
    ##  7 ATTG_R1.fastq PASS   Per base N content
    ##  8 ATTG_R1.fastq PASS   Sequence Length Distribution
    ##  9 ATTG_R1.fastq FAIL   Sequence Duplication Levels
    ## 10 ATTG_R1.fastq FAIL   Overrepresented sequences
    ## 11 ATTG_R1.fastq FAIL   Adapter Content
    ## 12 ATTG_R1.fastq FAIL   Kmer Content

Capitalisation and spelling of these module names follows the default patterns from FastQC reports with spaces replaced by underscores. One additional module is available and taken directly from the text within the supplied reports

- `Total_Duplicated_Percentage`

In addition, the read totals for each file in the library can be obtained using `readTotals()`, which can be easily used to make a table of read totals. This essentially just returns the first two columns from `getModule(x, "Basic_Statistics")`.

    reads <- readTotals(fdl)

The packages `dplyr` and `pander` can also be extremely useful for manipulating and displaying imported data. To show only the R1 read totals, you could do the following

    library(dplyr)
    library(pander)
    reads %>%
        dplyr::filter(grepl("R1", Filename)) %>%
        pander(
            big.mark = ",",
            caption = "Read totals from R1 libraries",
            justify = "lr"
        )

Read totals from R1 libraries
Filename | Total_Sequences
--- | ---
ATTG_R1.fastq | 24,978
CCGC_R1.fastq | 22,269
GACC_R1.fastq | 10,287

## 3.3 Generating Plots For One or More Fastqc Files

Plots created from a single `FastqcData` object will resemble those generated by the `FastQC` tool, whilst those created from a `FastqcDataList` will be combined summaries across a library of files. In addition, all plots are able to be generated as interactive plots using the argument `usePlotly = TRUE`.

All FastQC modules have been enabled for plotting using default `S4` dispatch, with the exception of `Per_tile_sequence_quality`.

### 3.3.1 Inspecting the PASS/WARN/FAIL Status of each module

The simplest of the plots is to summarise the `PASS/WARN/FAIL` flags as produced by `FastQC` for each module. This plot can be simply generated using `plotSummary()`

    plotSummary(fdl)

Figure 1: Default summary of FastQC flags

### 3.3.2 Visualising Read Totals

The next most informative plot may be to summarise the total numbers of reads in each associated Fastq file. By default, the number of duplicated sequences from the `Total_Duplicated_Percentage` module are shown, but this can be disabled by setting `duplicated = FALSE`.

    plotReadTotals(fdl)

As these are `ggplot2` objects, the output can be modified easily using conventional `ggplot2` syntax. Here we’ll move the legend to the top right as an example.

    plotReadTotals(fdl) +
        theme(
            legend.position = c(1, 1),
            legend.justification = c(1, 1),
            legend.background = element_rect(colour = "black")
        )

### 3.3.3 Per Base Sequence Qualities

Turning to the `Per base sequence quality` scores is the next most common step for most researchers, and these can be obtained for an individual file by selecting this as an element (i.e. `FastqcData` object ) of the main `FastqcDataList` object. This plot replicates the default plots from a FastQC report.

    plotBaseQuals(fdl[[1]])

Figure 2: Example showing the Per_base_sequence_quality plot for a single FastqcData object

When working with multiple FastQC reports, these are summarised as a heatmap using the mean quality score at each position.

    plotBaseQuals(fdl)

Figure 3: Example showing the Mean Per Base Squence Qualities for a set of FastQC reports

Boxplots of any combinations can also be drawn from a `FastqcDataList` by setting the argument `plotType = "boxplot"`. However, this may be not suitable for datasets with a large number of libraries.

    plotBaseQuals(fdl[1:4], plotType = "boxplot")

### 3.3.4 Mean Sequence Quality Per Read

Similarly, the Mean Sequence Quality Per Read plot can be generated to replicate plots from a FastQC report by selecting the individual file from the `FastqcDataList` object.

    plotSeqQuals(fdl[[1]])

Figure 4: Example plot showing Per_sequence_quality_scores for an individual file

A heatmap of mean sequence qualities can be generated when inspecting multiple reports.

    plotSeqQuals(fdl)

Figure 5: Example heatmaps showing Per_sequence_quality_scores for a set of files

An alternative view may be to plot these as overlaid lines, which can be simply done by setting `plotType = "line"`. Again, discretion should be shown when choosing this option for many samples.

    r2 <- grepl("R2", names(fdl))
    plotSeqQuals(fdl[r2], plotType = "line")

### 3.3.5 Per Base Sequence Content

The `Per_base_sequence_content` module can also be plotted for an individual report with the layout being identical to that from FastQC.

    plotSeqContent(fdl[[1]])

Figure 6: Individual Per_base_sequence_content plot

These are then combined across multiple files as a heatmap showing a composite colour for each position. Colours are combined using `rgb()` with `A`,`C`, `G` and `T` being represented by green, blue, black and red respectively.

    plotSeqContent(fdl)

Figure 7: Combined Per_base_sequence_content plot

NB These plots can be very informative setting the argument `usePlotly = TRUE`, however they can be slow to render given the nature of how the package `plotly` renders graphics.

Again, supplying multiple files and setting `plotType = "line"` will replicate multiple individual plots from the original FastQC reports. The argument `nc` is passed to `facet_wrap()` from the package `ggplot2` to determine the number of columns in the final plot.

    plotSeqContent(fdl[1:2], plotType = "line", nc = 1)

### 3.3.6 Adapter Content

Adapter content as identified by FastQC is also able to be plotted for an individual file.

    plotAdapterContent(fdl[[1]])

Figure 8: Adapter Content plot for a single FastQC report

When producing a heatmap across a set of FastQC reports, this will default to Total Adapter Content, instead of showing the individual adapter types.

    plotAdapterContent(fdl)

Figure 9: Heatmap showing Total Adapter Content by position across a set of FastQC reports

### 3.3.7 Sequence Duplication Levels

As with all modules, the Sequence Duplication Levels plot is able to be replicated for an individual file.

    plotDupLevels(fdl[[1]])

Figure 10: Example Sequence Duplication Levels plot for an individual file

When plotting across multiple FastQC reports, duplication levels are shown as a heatmap based on each default bin included in the initial FastQC reports. By default, the plotted values are the “Pre” de-duplication values. Note that values are converted to percentages instead of read numbers to ensure comparability across files. In the plot below, `CCGC_R2` shows very low duplication levels, whilst `ATTG_R1` shows high levels of duplication. The commonly observed ‘spikes’ around `>10` are also evident as the larger red blocks.

    plotDupLevels(fdl)

Figure 11: Sequence Duplication Levels for multiple files

## 3.4 GC content

### 3.4.1 The class TheoreticalGC

A selection of Theoretical GC content is supplied with the package in the object `gcTheoretical`, which has been defined with the additional `S4` class `TheoreticalGC`. GC content was calculated using scripts obtained from
https://github.com/mikelove/fastqcTheoreticalGC. Available genomes and transcriptomes can be obtained using the function `gcAvail()` on the object `gcTheoretical` and specifying the type.

    gcAvail(gcTheoretical, "Genome")

    ## # A tibble: 24 × 4
    ##    Name          Group    Source Version
    ##    <chr>         <chr>    <chr>  <chr>
    ##  1 Alyrata       Plants   JGI    V1.0
    ##  2 Amellifera    Animals  UCSC   apiMel2
    ##  3 Athaliana     Plants   TAIR   Arapot11
    ##  4 Btaurus       Animals  UCSC   bosTau8
    ##  5 Celegans      Animals  UCSC   ce11
    ##  6 Cfamiliaris   Animals  UCSC   canFam3
    ##  7 Dmelanogaster Animals  UCSC   dm6
    ##  8 Drerio        Animals  UCSC   danRer10
    ##  9 Ecoli         Bacteria NCBI   2008/08/05
    ## 10 Gaculeatus    Other    UCSC   gasAcu1
    ## # ℹ 14 more rows

### 3.4.2 Inspecting GC Content

As with all modules, data for an individual file replicates the default plot from a FastQC report, but with one key difference. This is that the Theoretical GC content has been provided in the object `gcTheoretical` based on 100bp reads. This empirically determined content is shown as the Theoretical GC content line.

    plotGcContent(fdl[[1]], species = "Hsapiens", gcType = "Transcriptome")

Figure 12: Example GC Content plot using the Hsapiens Transcriptome for the theoretical distribution

Again, data is summarised as a heatmap when plotting across multiple reports, with the default value being the difference between the observed and the theoretical GC content.

    plotGcContent(fdl)

Figure 13: Example GC content showing the difference between observed and theoretical GC content across multiple files

Line plots can also be produce an alternative viewpoint, with read totals displayed as percentages instead of raw counts.

    plotGcContent(fdl, plotType = "line",  gcType = "Transcriptome")

Figure 14: Example GC content plot represented as a line plot instead of a heatmap

Customized theoretical GC content can be generated using input DNA sequences from a supplied fasta file.

    faFile <- system.file(
        "extdata", "Athaliana.TAIR10.tRNA.fasta",
        package = "ngsReports")
    plotGcContent(fdl, Fastafile = faFile, n = 1000)

## 3.5 Overrepresented Sequences

When inspecting the Overrepresented Sequence module, the top `n`can be plotted for an individual file, again broken down by their possible source, and coloured based on their `WARN/FAIL` status.

    plotOverrep(fdl[[1]])

When applying this across multiple files, instead of identifying common sequences across a set of libraries, overrepresented sequences are summarised by their possible source as defined by FastQC.

    plotOverrep(fdl)

In addition to the above, the most abundant `n` overrepresented sequences can be exported as a FASTA file for easy submission to `blast`.

    overRep2Fasta(fdl, n = 10)

# 4 Importing Log Files

A selection of log files as produced by tools such as `STAR`, `hisat2`, `bowtie`, `bowtie2` and `picard duplicationMetrics`, can be easily imported using the `importNgsLogs()` function.

Tool can be specified by the user using the argument `type`, however if no `type` is provided we will attempt to auto-detect from the file’s structure. Note: only a single log file type can be imported at any time.

The `importNgsLogs()` function currently supports log files from the following tools.

Adapter removal and trimming

- AdapterRemoval
- cutadapt
- trimmomatic

Mapping and alignment

- bowtie
- bowtie2
- hisat2
- samtools flagstat
- STAR
- picard MarkDuplicates

Transcript/gene quantification

- featureCounts

Genome assembly

- BUSCO
- Quast

## 4.1 Adapter removal and trimming

    fl <- c("Sample1.trimmomaticPE.txt")
    trimmomaticLogs <- system.file("extdata", fl, package = "ngsReports")
    df <- importNgsLogs(trimmomaticLogs)

    df %>%
        dplyr::select("Filename", contains("Surviving"), "Dropped") %>%
        pander(
            split.tables = Inf,
            style = "rmarkdown",
            big.mark = ",",
            caption = "Select columns as an example of output from trimmomatic."
        )

Select columns as an example of output from trimmomatic.
Filename | Both_Surviving | Forward_Only_Surviving | Reverse_Only_Surviving | Dropped
--- | --- | --- | --- | ---
Sample1.trimmomaticPE.txt | 38243521 | 2791713 | 1464192 | 1663808

## 4.2 Mapping and alignment

### 4.2.1 Log file import

Bowtie log files can be parsed and imported

    fls <- c("bowtiePE.txt", "bowtieSE.txt")
    bowtieLogs <- system.file("extdata", fls, package = "ngsReports")
    df <- importNgsLogs(bowtieLogs, type = "bowtie")

    df %>%
        dplyr::select("Filename", starts_with("Reads")) %>%
        pander(
            split.tables = Inf,
            style = "rmarkdown",
            big.mark = ",",
            caption = "Select columns as an example of output from bowtie."
        )

Select columns as an example of output from bowtie.
Filename | Reads_Processed | Reads_With_At_Least_One_Reported_Alignment | Reads_That_Failed_To_Align | Reads_With_Alignments_Suppressed_Due_To_-m
--- | --- | --- | --- | ---
bowtiePE.txt | 42,867,915 | 21,891,058 | 20,748,783 | 228,074
bowtieSE.txt | 440,372 | 193,962 | 246,410 | NA

STAR log files can be parsed and imported

    starLog <- system.file("extdata", "log.final.out", package = "ngsReports")
    df <- importNgsLogs(starLog, type = "star")

Select columns as output from STAR
Filename | Uniquely_Mapped_Reads_Number | Uniquely_Mapped_Reads_Percent
--- | --- | ---
log.final.out | 68,471,490 | 85.84

The output of the `samtools flagstat` module can be parsed and imported

    flagstatLog <- system.file("extdata", "flagstat.txt", package = "ngsReports")
    df <- importNgsLogs(flagstatLog, type = "flagstat")

Flagstat output for a single file
Filename | QC-passed | QC-failed | flag
--- | --- | --- | ---
flagstat.txt | 67,405,447 | 0 | in total
flagstat.txt | 5,275,834 | 0 | secondary
flagstat.txt | 0 | 0 | supplementary
flagstat.txt | 0 | 0 | duplicates
flagstat.txt | 67,405,447 | 0 | mapped
flagstat.txt | 62,129,613 | 0 | paired in sequencing
flagstat.txt | 31,065,014 | 0 | read1
flagstat.txt | 31,064,599 | 0 | read2
flagstat.txt | 62,128,918 | 0 | properly paired
flagstat.txt | 62,128,918 | 0 | with itself and mate mapped
flagstat.txt | 695 | 0 | singletons
flagstat.txt | 0 | 0 | with mate mapped to a different chr
flagstat.txt | 0 | 0 | with mate mapped to a different chr (mapQ>=5)

In addition to the files produced by the above alignment tools, the output from Duplication Metrics (`picard`) can also be imported. This is imported as a list with a `tibble` containing the detailed output in the list element `$metrics` and the histogram data included as the second elements.

    sysDir <- system.file("extdata", package = "ngsReports")
    fl <- list.files(sysDir, "Dedup_metrics.txt", full.names = TRUE)
    dupMetrics <- importNgsLogs(fl, type = "duplicationMetrics", which = "metrics")
    str(dupMetrics)

    ## tibble [1 × 10] (S3: tbl_df/tbl/data.frame)
    ##  $ Library                       : chr "~/myExpt/2_alignedData/bams/Sample1.bam"
    ##  $ Unpaired Reads Examined       : int 93
    ##  $ Read Pairs Examined           : int 43490914
    ##  $ Secondary Or Supplementary Rds: int 5366030
    ##  $ Unmapped Reads                : int 0
    ##  $ Unpaired Read Duplicates      : int 79
    ##  $ Read Pair Duplicates          : int 11494386
    ##  $ Read Pair Optical Duplicates  : int 642648
    ##  $ Percent Duplication           : num 0.264
    ##  $ Estimated Library Size        : int 69609522

### 4.2.2 Plot log file imports

Summaries of log files from select mapping and alignment tools can be plot using the function `plotAlignemntSummary()`.

    plotAlignmentSummary(bowtieLogs, type = "bowtie")

Figure 15: Example Bowtie logs for PE and SE sequencing

    plotAlignmentSummary(starLog, type = "star")

Figure 16: Example STAR aligner logs

## 4.3 Genome assembly

Assembly ‘completeness’ and summary statistic information from the tools BUSCO and quast can also be plot using the function `plotAssemblyStats()`

    buscoLog <- system.file("extdata", "short_summary_Dmelanogaster_Busco.txt", package = "ngsReports")
    plotAssemblyStats(buscoLog, type = "busco")

Figure 17: Example plot after running BUSCO v3 on the Drosophila melanogaster reference genome

    fls <- c("quast1.tsv", "quast2.tsv")
    quastLog <- system.file("extdata", fls, package = "ngsReports")
    plotAssemblyStats(quastLog, type = "quast")

Figure 18: Example plot after running quast on two shortread assemblies

    plotAssemblyStats(quastLog, type = "quast", plotType = "paracoord")

Figure 19: Example parallel coordinate plot after running quast on two shortread assemblies

# Session info

    ## R version 4.3.1 (2023-06-16)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 22.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_GB              LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: America/New_York
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] pander_0.6.5        dplyr_1.1.3         ngsReports_2.4.0
    ## [4] tibble_3.2.1        patchwork_1.1.3     ggplot2_3.4.4
    ## [7] BiocGenerics_0.48.0 BiocStyle_2.30.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] gtable_0.3.4            xfun_0.40               bslib_0.5.1
    ##  [4] htmlwidgets_1.6.2       lattice_0.22-5          vctrs_0.6.4
    ##  [7] tools_4.3.1             bitops_1.0-7            generics_0.1.3
    ## [10] stats4_4.3.1            fansi_1.0.5             pkgconfig_2.0.3
    ## [13] data.table_1.14.8       RColorBrewer_1.1-3      S4Vectors_0.40.0
    ## [16] lifecycle_1.0.3         GenomeInfoDbData_1.2.11 farver_2.1.1
    ## [19] compiler_4.3.1          stringr_1.5.0           Biostrings_2.70.0
    ## [22] munsell_0.5.0           GenomeInfoDb_1.38.0     htmltools_0.5.6.1
    ## [25] sass_0.4.7              RCurl_1.98-1.12         yaml_2.3.7
    ## [28] lazyeval_0.2.2          plotly_4.10.3           pillar_1.9.0
    ## [31] crayon_1.5.2            jquerylib_0.1.4         tidyr_1.3.0
    ## [34] MASS_7.3-60             DT_0.30                 cachem_1.0.8
    ## [37] magick_2.8.1            tidyselect_1.2.0        digest_0.6.33
    ## [40] stringi_1.7.12          reshape2_1.4.4          purrr_1.0.2
    ## [43] bookdown_0.36           labeling_0.4.3          forcats_1.0.0
    ## [46] fastmap_1.1.1           grid_4.3.1              colorspace_2.1-0
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    ## [58] httr_1.4.7              zoo_1.8-12              evaluate_0.22
    ## [61] knitr_1.44              IRanges_2.36.0          viridisLite_0.4.2
    ## [64] rlang_1.1.1             ggdendro_0.1.23         Rcpp_1.0.11
    ## [67] glue_1.6.2              BiocManager_1.30.22     jsonlite_1.8.7
    ## [70] plyr_1.8.9              R6_2.5.1                zlibbioc_1.48.0