Contents

1 Preliminary notes

The Shiny Variant Explorer (tSVE) was primarily developped to demonstrate features implemented in the TVTB, not as a production environment. As a result, a few important considerations should be made to clarify what should and should not be expected from the web-application:

2 Pre-requisites

The Shiny Variant Explorer suggests a few additional package dependencies compared to the package, to support certain forms of data input and display.

Input

Display

3 Launching the Shiny Variant Explorer

The TVTB::tSVE() method launches the web-application.

4 Overall layout of the web-application

Overall, the web-application is implemented as a web-page with a top level navigation bar organised from left to right to reflect progression through a typical analysis, with the exception of the last two menu items Settings and Session, which may be useful to check and update at any point.

Here is a brief overview of the menu items:

5 Input panel

The Input panel controls the major input parameters of the analysis, including phenotypes (and therefore samples), genomic ranges, and fields to import from VCF file(s). Those inputs are useful to import only data of interest, as well as to limit memory usage and duration of calculations.

5.1 Phenotypes

Phenotypes are critical to define groups of samples that may be compared in summary statistics, tables, and plots. Moreover, phenotypes also implicitely define the set of samples required in the analysis (unique sample identifiers usually set as rownames of the phenotypes).

The web-application accepts phenotypes stored in a text file, with the following requirements:

  • Fields must be delimited by “white space” (default separator for the read.table function).
  • The first column of the file must contain unique sample identifiers, as syntactically valid rownames.
  • The first row of the file must phenotype names, as syntactically valid colnames.

When provided, phenotypes will be used to import from VCF file(s) only genotypes for the corresponding samples identifiers. Moreover, an error message will be displayed if any of the sample identifiers present in the phenotypes is absent from the VCF file(s).

Note that the web-application does not absolutely require phenotype information. In the absence of phenotype information, all samples are imported from VCF file(s).

Action:

  • Click on the Browse action button
  • Navigate to the extdata folder of the TVTB installation directory
  • Select the file integrated_samples.txt

Alternatively: click the Sample file button

Notes

  • The TVTB installation directory can be identified using the following command in an R session:
system.file("extdata", package = "TVTB")
  • The file selection pop-up window that is open by the action button browses files on the server side. This point is only relevant if the package/web-application is run on a remote server.

5.2 Genomic ranges

Genomic ranges are critical to import only variants in targeted genomic regions or features (e.g. genes, transcripts, exons), as well as to limit memory usage and duration of calculations.

The Shiny Variant Explorer currently supports three types of input to define genomic ranges:

  • BED file
  • UCSC-style text input
  • EnsDb annotation packages

Currently, the web-application uses genomic ranges solely to query the corresponding variants from VCF file(s). In the future, those genomic ranges may also be used to produce faceted summary statistics and plots.

Notes:

  • The web-application does not absolutely require genomic ranges. In the absence of genomic ranges, all variants are imported from VCF file(s). Caution recommended with large files!
  • When VCF file(s) are parsed (in a later section), only the genomic ranges from the currently selected input mode are
  • The active genomic ranges are only taken from the currently selected input mode

5.2.1 BED file

If a BED file is supplied, the web-application parses it using the rtracklayer import.bed method. Therefore the file must respect the BED file format guidelines.

Action:

  • Click on the Browse action button
  • Navigate to the extdata folder of the TVTB installation directory
  • Select the file SLC24A5.bed

Alternatively: click the Sample file button

Notes:

  • The BED file defines the same genomic range that was used to extract variant from the 1000 Genomes Project Phase 3 release VCF file used in this vignette.
  • The file selection window that is open by the action button browses files on the server side (see Phenotypes section above).

5.2.2 UCSC format

Sequence names (i.e. chromosomes), start, and end positions of one or more genomic ranges may be defined in the text field, with individual regions separated by ";".

Action:

  • Paste 15:48,413,169-48,434,869 in the text field

Alternatively: click the Sample input button

Notes:

  • The web-application automatically trims "," characters from the text input, before coercing the start and end positions to numeric
  • Multiple genomic ranges may be supplied (e.g. 1:123-456;2:234-345;2:456-789)

5.2.3 Ensembl-based annotation packages

Currently, genomic ranges encoding only gene-coding regions may be retrieved from an Ensembl-based database. This feature was adapted from the web-application implemented in the ensembldb package.

Action:

  • Paste SLC24A5 in the text field

Alternatively: click the Sample input button

5.3 Variants

At the core of the TVTB package, variants must be imported from one or more VCF file(s) annotated by the Ensembl Variant Effect Predictor (VEP) script (McLaren et al. 2010).

Considering the large size of most VCF file(s), it is common practice to split genetic variants into multiple files, each file used to store variants located on a single chromosome (more generally; a single sequence). The Shiny Variant Explorer supports two situations:

  • All variants are stored in a single VCF file (“Single-VCF” mode).
  • Variants are split into one file per sequence, with the requirement that files be named with a pattern including the sequence name (must match the seqnames slot of the genomic ranges described above (“Multi-VCF mode”).

In addition, VCF files can store a plethora of information in their various fields. It is often useful to select only a subset of fields relevant for a particular analysis, to limit memory usage. The web-application uses the VariantAnnotation scanVcfHeader to parse the header of the VCF file (Single-VCF mode) or the first VCF file (Multi-VCF mode), to display the list of available fields that users may choose to import. A few considerations must be made:

  • The web-application requires that Ensembl VEP predictions be present in the INFO field.
  • The web-application requires that the "GT" key be present in the FORMAT field.

5.3.1 Single-VCF mode

This mode display an action button that must be used to select the VCF file from which to import variants.

Action:

  • Click on the Browse action button
  • Navigate to the extdata folder of the TVTB installation directory
  • Select the file chr15.phase3_integrated.vcf.gz

Alternatively: click the Sample file button

5.3.2 Multi-VCF mode

This mode requires two pieces of information:

  • The path to the folder that contains one or more VCF file(s).
  • The naming pattern of VCF file(s), with the following requirement:
    • The pattern must include "%s" to declare the emplacement of the sequence (i.e. chromosome) name in the pattern.

Note that a summary of VCF file(s) detected using the given the folder and pattern is displayed on the right, to help users determine whether the parameters are correct. In addition, the content of the given folder is displayed at the bottom of the page, beside the same content filtered for the VCF file naming pattern.

Action:

None. The text fields should already be filled with default values, pointing to the single example VCF file (chr15.phase3_integrated.vcf.gz).

5.3.3 VCF scan parameters

This panel allows users to select the INFO and FORMAT fields to import (in the info and geno slots of the VCF object, respectively).

It is important to note that the FORMAT/GT and INFO/ fields—where <vep> stands for the INFO key where Ensembl VEP predictions are stored—are implicitely imported from the VCF. Similarly, the mandatory FIXED fields CHROM, POS, ID, REF, ALT, QUAL, and FILTER are automatically imported to populate the rowRanges slot of the VCF object.

Action:

  • Click the Deselect all action button under the INFO fields selection input to import only the INFO/CSQ and FORMAT/GT fields.
  • Click the Import variants action button

A summary of variants, phenotypes, and samples imported will appear beside the action button.

5.4 Annotations

This panel allows users to select a pre-installed annotation package. Currently, only EnsDb annotation packages are supported, and only gene-coding regions may be queried.

Action:

  • If none of the EnsDb packages are installed, it will simply not be possible to use the ensembl interface of the Genomic ranges input tab.
  • If the EnsDb.Hsapiens.v75 package is the only EnsDb packages installed, no action is required; the package should already be pre-selected.
  • If the EnsDb.Hsapiens.v75 package is not the only EnsDb packages installed, users should select it in the list of choices.

6 Frequencies panel

This panel demonstrates the use of three methods implemented in the TVTB package, namely addFrequencies, addOverallFrequencies, and addPhenoLevelFrequencies.

6.1 Overall frequencies

This panel allows users to Add and Remove INFO fields that contain genotype counts (i.e. homozygote reference, heterozygote, homozygote alternate) and allele frequencies (i.e. alternate allele frequency, minor allele frequency) calculated across all the samples and variants imported. The web-application uses the homozygote reference, heterozygote, and homozygote alternate genotypes defined in the Advanced settings panel.

Importantly, the name of the INFO keys that are used to store the calculated values can be defined in the Advanced settings panel.

Action:

  • Click the Add action button
  • See the Latest changes message update at the top of the screen.
  • Optionally, the Views panel can be used to examine the new fields

6.2 Phenotype-level frequencies

This panel allows users to Refresh the list of INFO fields that contain genotype counts and allele frequencies calculated within groups of samples associated with various levels of a given phenotype.

Action:

  • Select super_pop in the list of phenotypes
  • Click the Select all action button
  • Click the Refresh action button
  • See the Latest changes message update at the top of the screen.
  • Optionally, the Views panel can be used to examine the new fields

7 Filters panel

One of the flagship features of the TVTB package are the VCF filter rules, extending the S4Vectors FilterRules class to new classes of filter rules that can be evaluated within environments defined by the various slots of VCF objects.

Generally speaking, FilterRules greatly facilitate the design and combination of powerful filter rules for table-like objects, such as the fixed and info slots of VariantAnnotation VCF objects, as well as Ensembl VEP predictions stored in the meta-columns of GRanges returned by the ensemblVEP parseCSQToGRanges method.

A separate vignette describes in greater detail the use of classes that contain VCF filter rules. A simple example is shown below.

Action:

  • Select VEP as the Type of filter
  • Paste grepl("missense",Consequence) in the text field
  • Leave the Active? checkbox ticked
  • Click the Add filter action button
  • See the list of rules update at the bottom of the screen
  • Click the Apply filters action button
  • See the summary of filtered variants update beside the action button
  • Optionally, the Views panel can be used to examine the new fields

Alternatively: click the Sample input button

8 Views panel

This panel offers the chance to examine the main objects of the session, namely:

Action:

  • In the various panels, select fields to examine each object
    • In particular, note the INFO fields that contain genotype counts and allele frequencies calculated earlier
  • Go to the Heatmap tab of the Genotypes panel
  • Click the Go! action button to calculate and display the heatmap

9 Plots panel

This panel demonstrates the use of two methods implemented in the TVTB package, namely tabulateVepByPhenotype and densityVepByPhenotype.

10 Settings panel

This panel stores more advanced settings that users may not need to edit as frequently, if at all. Those settings are divided in two sub-panels:

10.1 Advanced settings

10.1.1 Genotypes

It is critical to accurately identify and define how the different genotypes—homozygote reference, heterozygote, and homozygote alternate—are encoded in the VCF file, to produce accurate genotypes counts and frequencies, for instance. This generally requires examining the content of the FORMAT/GT field outside of the web-application. For instance, the functions unique and table may be used to identify (and count) all the distinct genotype codes in the geno slot ("GT" key) of a VCF object.

The default selected values are immediately compatible with the demonstration data set. Users who wish to select genotypes codes not yet available among the current choices may either contact the package maintainer to add them in a future release, or edit the Global configuration file of the web-application locally.

10.1.2 INFO key suffixes

Currently, the three calculated genotypes counts and two allele frequencies require five INFO fields to store their respective values.

Considering that TVTB offers the possibility to calculate counts and frequencies for the overall data set, and for each level of each phenotype, it is important to define a clear and consistent naming mechanism that does not conflict with INFO keys imported from the VCF file(s). In the TVTB package, a suffix is required for each type of genotype and frequency calculated, to generate INFO as follows:

  • Overall counts and frequencies are stored in INFO keys named <suffix>
  • Counts and frequencies calculated for individual levels of selected phenotypes are stored under INFO keys formed as <phenotype>_<level>_<suffix>

Again, the default values are immediately compatible with the demonstration data set. For other data sets, it may be necessary to change those values, either by preference, or to avoid conflict with INFO keys imported from the VCF file(s).

10.1.3 Miscellaneous settings

Other rarely used settings in this panel include:

  • VCF yield size

10.2 Parallel settings

Several functionalities of the TVTB package are applied to independent subsets of data (e.g. counting genotypes in various levels of a given phenotype). Such processes can benefit from multi-threaded calculations. Multi-threading settings in the Shiny web-application are somewhat experimental, as they have been validated only on a small set of operating systems, while some issues have been reported for others.

Report Operating System Cluster Class Cluster type # Cores
OK Ubuntu 14.04 Multicore FORK 2
OK Scientific Linux 6.7 Multicore FORK 2
Hang1 OS X El Capitan Snow SOCK 2
  1. Application hangs while CPUs work infinitely at full capacity.

11 Session information

The last panel of the Shiny Variant Explorer offers detailed views of objects and settings in the current session, including:

12 Global configuration

Most default values are stored in the global.R file of the web-application. All the files of the web-application are stored in the extdata/shinyApp folder of the TVTB installation directory (see an earlier section to identify this directory).

Users who wish to change the default values of certain input widgets (e.g. genotype codes) may edit the global.R file accordingly. However, the file will be reset at each package update.

13 Vignette session

Here is the output of sessionInfo() on the system on which this document was compiled:

sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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] TVTB_1.16.0      knitr_1.30       BiocStyle_2.18.0
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_1.4-1            ellipsis_0.3.1             
##   [3] biovizBase_1.38.0           htmlTable_2.1.0            
##   [5] XVector_0.30.0              GenomicRanges_1.42.0       
##   [7] base64enc_0.1-3             dichromat_2.0-0            
##   [9] rstudioapi_0.11             farver_2.0.3               
##  [11] bit64_4.0.5                 AnnotationDbi_1.52.0       
##  [13] xml2_1.3.2                  splines_4.0.3              
##  [15] Formula_1.2-4               Rsamtools_2.6.0            
##  [17] cluster_2.1.0               dbplyr_1.4.4               
##  [19] png_0.1-7                   BiocManager_1.30.10        
##  [21] compiler_4.0.3              httr_1.4.2                 
##  [23] backports_1.1.10            assertthat_0.2.1           
##  [25] Matrix_1.2-18               lazyeval_0.2.2             
##  [27] limma_3.46.0                htmltools_0.5.0            
##  [29] prettyunits_1.1.1           tools_4.0.3                
##  [31] gtable_0.3.0                glue_1.4.2                 
##  [33] GenomeInfoDbData_1.2.4      reshape2_1.4.4             
##  [35] dplyr_1.0.2                 rappdirs_0.3.1             
##  [37] Rcpp_1.0.5                  Biobase_2.50.0             
##  [39] vctrs_0.3.4                 Biostrings_2.58.0          
##  [41] rtracklayer_1.50.0          xfun_0.18                  
##  [43] stringr_1.4.0               ensemblVEP_1.32.0          
##  [45] lifecycle_0.2.0             ensembldb_2.14.0           
##  [47] XML_3.99-0.5                zlibbioc_1.36.0            
##  [49] scales_1.1.1                BSgenome_1.58.0            
##  [51] VariantAnnotation_1.36.0    hms_0.5.3                  
##  [53] MatrixGenerics_1.2.0        ProtGenerics_1.22.0        
##  [55] parallel_4.0.3              SummarizedExperiment_1.20.0
##  [57] AnnotationFilter_1.14.0     RColorBrewer_1.1-2         
##  [59] yaml_2.2.1                  curl_4.3                   
##  [61] memoise_1.1.0               gridExtra_2.3              
##  [63] ggplot2_3.3.2               pander_0.6.3               
##  [65] biomaRt_2.46.0              rpart_4.1-15               
##  [67] reshape_0.8.8               latticeExtra_0.6-29        
##  [69] stringi_1.5.3               RSQLite_2.2.1              
##  [71] S4Vectors_0.28.0            checkmate_2.0.0            
##  [73] GenomicFeatures_1.42.0      BiocGenerics_0.36.0        
##  [75] BiocParallel_1.24.0         GenomeInfoDb_1.26.0        
##  [77] rlang_0.4.8                 pkgconfig_2.0.3            
##  [79] matrixStats_0.57.0          bitops_1.0-6               
##  [81] evaluate_0.14               lattice_0.20-41            
##  [83] purrr_0.3.4                 labeling_0.4.2             
##  [85] GenomicAlignments_1.26.0    htmlwidgets_1.5.2          
##  [87] bit_4.0.4                   tidyselect_1.1.0           
##  [89] GGally_2.0.0                plyr_1.8.6                 
##  [91] magrittr_1.5                bookdown_0.21              
##  [93] R6_2.4.1                    magick_2.5.0               
##  [95] IRanges_2.24.0              generics_0.0.2             
##  [97] Hmisc_4.4-1                 DelayedArray_0.16.0        
##  [99] DBI_1.1.0                   pillar_1.4.6               
## [101] foreign_0.8-80              survival_3.2-7             
## [103] RCurl_1.98-1.2              nnet_7.3-14                
## [105] tibble_3.0.4                crayon_1.3.4               
## [107] BiocFileCache_1.14.0        rmarkdown_2.5              
## [109] jpeg_0.1-8.1                progress_1.2.2             
## [111] grid_4.0.3                  data.table_1.13.2          
## [113] blob_1.2.1                  digest_0.6.27              
## [115] EnsDb.Hsapiens.v75_2.99.0   openssl_1.4.3              
## [117] stats4_4.0.3                munsell_0.5.0              
## [119] Gviz_1.34.0                 askpass_1.1

References

McLaren, W., B. Pritchard, D. Rios, Y. Chen, P. Flicek, and F. Cunningham. 2010. “Deriving the Consequences of Genomic Variants with the Ensembl API and SNP Effect Predictor.” Journal Article. Bioinformatics 26 (16):2069–70. https://doi.org/10.1093/bioinformatics/btq330.