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 FastQC1 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)
Default summary of FastQC flags.

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]])
Example showing the Per_base_sequence_quality plot for a single FastqcData object.

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)
Example showing the Mean Per Base Squence Qualities for a set of FastQC reports.

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]])
Example plot showing Per_sequence_quality_scores for an individual file.

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)
Example heatmaps showing Per_sequence_quality_scores for a set of files.

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]])
Individual Per_base_sequence_content plot

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)
Combined Per_base_sequence_content plot

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]])
Adapter Content plot for a single FastQC report

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)
Heatmap showing Total Adapter Content by position across a set of FastQC reports

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]])
Example Sequence Duplication Levels plot for an individual file.

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)
Sequence Duplication Levels for multiple files

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   
## # … with 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")
Example GC Content plot using the Hsapiens Transcriptome for the theoretical distribution.

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)
Example GC content showing the difference between observed and theoretical GC content across multiple files.

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")
Example GC content plot represented as a line plot instead of a heatmap.

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 ncan 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

Mapping and alignment

Transcript/gene quantification

Genome assembly

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")
Example Bowtie logs for PE and SE sequencing

Figure 15: Example Bowtie logs for PE and SE sequencing

plotAlignmentSummary(starLog, type = "star")
Example STAR aligner logs

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")
Example plot after running BUSCO v3 on the Drosophila melanogaster reference genome

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")
Example plot after running quast on two shortread assemblies

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

plotAssemblyStats(quastLog, type = "quast", plotType = "paracoord")
Example parallel coordinate plot after running quast on two shortread assemblies

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

Session info

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
## 
## 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       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] pander_0.6.4        dplyr_1.0.7         ngsReports_1.10.0  
## [4] tibble_3.1.5        ggplot2_3.3.5       BiocGenerics_0.40.0
## [7] BiocStyle_2.22.0   
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.7             lattice_0.20-45        lubridate_1.8.0       
##  [4] tidyr_1.1.4            zoo_1.8-9              Biostrings_2.62.0     
##  [7] assertthat_0.2.1       digest_0.6.28          utf8_1.2.2            
## [10] R6_2.5.1               GenomeInfoDb_1.30.0    stats4_4.1.1          
## [13] evaluate_0.14          highr_0.9              httr_1.4.2            
## [16] pillar_1.6.4           zlibbioc_1.40.0        rlang_0.4.12          
## [19] lazyeval_0.2.2         data.table_1.14.2      jquerylib_0.1.4       
## [22] magick_2.7.3           S4Vectors_0.32.0       DT_0.19               
## [25] rmarkdown_2.11         labeling_0.4.2         stringr_1.4.0         
## [28] htmlwidgets_1.5.4      RCurl_1.98-1.5         munsell_0.5.0         
## [31] compiler_4.1.1         xfun_0.27              pkgconfig_2.0.3       
## [34] htmltools_0.5.2        tidyselect_1.1.1       GenomeInfoDbData_1.2.7
## [37] bookdown_0.24          IRanges_2.28.0         fansi_0.5.0           
## [40] viridisLite_0.4.0      crayon_1.4.1           withr_2.4.2           
## [43] MASS_7.3-54            bitops_1.0-7           grid_4.1.1            
## [46] jsonlite_1.7.2         gtable_0.3.0           lifecycle_1.0.1       
## [49] DBI_1.1.1              magrittr_2.0.1         scales_1.1.1          
## [52] cli_3.0.1              stringi_1.7.5          farver_2.1.0          
## [55] XVector_0.34.0         bslib_0.3.1            ellipsis_0.3.2        
## [58] ggdendro_0.1.22        generics_0.1.1         vctrs_0.3.8           
## [61] RColorBrewer_1.1-2     tools_4.1.1            forcats_0.5.1         
## [64] glue_1.4.2             purrr_0.3.4            fastmap_1.1.0         
## [67] yaml_2.2.1             colorspace_2.0-2       BiocManager_1.30.16   
## [70] plotly_4.10.0          knitr_1.36             sass_0.4.0