The Shiny Variant Explorer (original) (raw)

Contents

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:

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

Launching the Shiny Variant Explorer

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

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:

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.

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 rownamesof the phenotypes).

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

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:

Alternatively: click the Sample file button

Notes

system.file("extdata", package = "TVTB")

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:

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:

BED file

If a BED file is supplied, the web-application parses it using the_rtracklayer_ import.bed method. Therefore the file must respect theBED file formatguidelines.

Action:

Alternatively: click the Sample file button

Notes:

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:

Alternatively: click the Sample input button

Notes:

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:

Alternatively: click the Sample input button

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:

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:

Single-VCF mode

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

Action:

Alternatively: click the Sample file button

Multi-VCF mode

This mode requires two pieces of information:

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).

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:

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

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:

Frequencies panel

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

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 theAdvanced settingspanel.

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

Action:

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:

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 GRangesreturned 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:

Alternatively: click the Sample input button

Views panel

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

Action:

Plots panel

This panel demonstrates the use of two methods implemented in the_TVTB_ package, namely tabulateVepByPhenotypeand densityVepByPhenotype.

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:

Advanced settings

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 accurategenotypes 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 fileof the web-application locally.

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:

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).

Miscellaneous settings

Other rarely used settings in this panel include:

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.

  1. Application hangs while CPUs work infinitely at full capacity.

Session information

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

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/shinyAppfolder 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.

Vignette session

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

sessionInfo()
## R version 4.5.1 Patched (2025-08-23 r88802)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
## 
## 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] TVTB_1.36.0      knitr_1.50       BiocStyle_2.38.0
## 
## loaded via a namespace (and not attached):
##   [1] RColorBrewer_1.1-3          rstudioapi_0.17.1          
##   [3] jsonlite_2.0.0              magrittr_2.0.4             
##   [5] magick_2.9.0                GenomicFeatures_1.62.0     
##   [7] farver_2.1.2                rmarkdown_2.30             
##   [9] BiocIO_1.20.0               vctrs_0.6.5                
##  [11] memoise_2.0.1               Rsamtools_2.26.0           
##  [13] RCurl_1.98-1.17             base64enc_0.1-3            
##  [15] tinytex_0.57                htmltools_0.5.8.1          
##  [17] S4Arrays_1.10.0             progress_1.2.3             
##  [19] curl_7.0.0                  SparseArray_1.10.0         
##  [21] Formula_1.2-5               sass_0.4.10                
##  [23] bslib_0.9.0                 htmlwidgets_1.6.4          
##  [25] plyr_1.8.9                  Gviz_1.54.0                
##  [27] httr2_1.2.1                 cachem_1.1.0               
##  [29] GenomicAlignments_1.46.0    lifecycle_1.0.4            
##  [31] pkgconfig_2.0.3             Matrix_1.7-4               
##  [33] R6_2.6.1                    fastmap_1.2.0              
##  [35] MatrixGenerics_1.22.0       digest_0.6.37              
##  [37] colorspace_2.1-2            GGally_2.4.0               
##  [39] AnnotationDbi_1.72.0        S4Vectors_0.48.0           
##  [41] Hmisc_5.2-4                 GenomicRanges_1.62.0       
##  [43] RSQLite_2.4.3               labeling_0.4.3             
##  [45] filelock_1.0.3              httr_1.4.7                 
##  [47] abind_1.4-8                 compiler_4.5.1             
##  [49] withr_3.0.2                 bit64_4.6.0-1              
##  [51] pander_0.6.6                htmlTable_2.4.3            
##  [53] S7_0.2.0                    backports_1.5.0            
##  [55] BiocParallel_1.44.0         DBI_1.2.3                  
##  [57] ggstats_0.11.0              biomaRt_2.66.0             
##  [59] rappdirs_0.3.3              DelayedArray_0.36.0        
##  [61] rjson_0.2.23                tools_4.5.1                
##  [63] foreign_0.8-90              nnet_7.3-20                
##  [65] glue_1.8.0                  restfulr_0.0.16            
##  [67] grid_4.5.1                  checkmate_2.3.3            
##  [69] reshape2_1.4.4              cluster_2.1.8.1            
##  [71] generics_0.1.4              gtable_0.3.6               
##  [73] BSgenome_1.78.0             tidyr_1.3.1                
##  [75] ensembldb_2.34.0            data.table_1.17.8          
##  [77] hms_1.1.4                   XVector_0.50.0             
##  [79] BiocGenerics_0.56.0         pillar_1.11.1              
##  [81] stringr_1.5.2               limma_3.66.0               
##  [83] dplyr_1.1.4                 BiocFileCache_3.0.0        
##  [85] lattice_0.22-7              deldir_2.0-4               
##  [87] rtracklayer_1.70.0          bit_4.6.0                  
##  [89] EnsDb.Hsapiens.v75_2.99.0   biovizBase_1.58.0          
##  [91] tidyselect_1.2.1            Biostrings_2.78.0          
##  [93] gridExtra_2.3               bookdown_0.45              
##  [95] IRanges_2.44.0              Seqinfo_1.0.0              
##  [97] ProtGenerics_1.42.0         SummarizedExperiment_1.40.0
##  [99] stats4_4.5.1                xfun_0.53                  
## [101] Biobase_2.70.0              statmod_1.5.1              
## [103] matrixStats_1.5.0           stringi_1.8.7              
## [105] UCSC.utils_1.6.0            lazyeval_0.2.2             
## [107] yaml_2.3.10                 evaluate_1.0.5             
## [109] codetools_0.2-20            cigarillo_1.0.0            
## [111] interp_1.1-6                tibble_3.3.0               
## [113] BiocManager_1.30.26         cli_3.6.5                  
## [115] rpart_4.1.24                jquerylib_0.1.4            
## [117] dichromat_2.0-0.1           Rcpp_1.1.0                 
## [119] GenomeInfoDb_1.46.0         dbplyr_2.5.1               
## [121] png_0.1-8                   XML_3.99-0.19              
## [123] parallel_4.5.1              ggplot2_4.0.0              
## [125] blob_1.2.4                  prettyunits_1.2.0          
## [127] jpeg_0.1-11                 latticeExtra_0.6-31        
## [129] AnnotationFilter_1.34.0     bitops_1.0-9               
## [131] VariantAnnotation_1.56.0    scales_1.4.0               
## [133] purrr_1.1.0                 crayon_1.5.3               
## [135] rlang_1.1.6                 KEGGREST_1.50.0

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.

Appendix