About

An interactive graphical illustration of genetic associations and their biological context.

Disclaimer

LocusExplorer should be used for illustrative purposes only. Any results provided by LocusExplorer should be used with caution.

Availability

The source code and installation instructions for LocusExplorer are available at https://github.com/oncogenetics/LocusExplorer.

LocusExplorer is made available under the MIT license.

Required Software

LocusExplorer runs in the R environment but is designed to be an easy to use interface that does not require familiarity with R as a prerequisite. LocusExplorer is platform agnostic and able to run on any operating system for which R is available.

LocusExplorer requires R version 3.2.2 to run and can be downloaded by following the instructions at https://www.r-project.org/. Some required packages are not available for earlier versions of R.

After installation of the R software, R packages used by LocusExplorer must be installed prior to use. This may take a few minutes, but is only required on the first occasion. To install packages, open the R program, copy the following code into the R console and hit Return:

#install CRAN packages, if missing
packages <- c("shiny","dplyr","tidyr","lazyeval","data.table","ggplot2","ggrepel","knitr","markdown","DT","lattice","acepack","cluster","DBI","colourpicker","igraph","visNetwork", "devtools")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())), dependencies = TRUE)  
} else { print("All required CRAN packages installed")}

#install Bioconductor packages if missing
source("https://bioconductor.org/biocLite.R")
bioc <- c("ggbio","GenomicRanges","TxDb.Hsapiens.UCSC.hg19.knownGene","org.Hs.eg.db","rtracklayer")
if (length(setdiff(bioc, rownames(installed.packages()))) > 0) {
  biocLite(setdiff(bioc, rownames(installed.packages())))  
} else { print("All required Bioconductor packages installed")}

#install GitHub packages:
devtools::install_github("oncogenetics/oncofunco")

• In cases when user do not have admin rights, pop up window will prompt to set a personal library location for installation of packages, please click yes.
• If using R GUI then user might get prompted to choose CRAN mirror to use for package downloads, please choose the city nearer to your location.
• If prompted to “Update packages all/some/none [a/s/n]”, type “n” and hit Return.

Launch LocusExplorer

LocusExplorer runs through a web browser and uses an intuitive interface that does not require high level computational skills to operate.

1. Using runGitHub() within RStudio

Open RStudio (start a new R session) copy the following code into the console and hit Return:

library(shiny)  
runGitHub("LocusExplorer", "oncogenetics", launch.browser = TRUE)

2. Using Download as Zip (Recommended)

Click on Download as ZIP button, this will download the repisotory locally as a zip file LocusExplorer-master.zip. Unzip the folder. Open ui.R file in RStudio (start a new R session) and click on Run App (Please ensure Run External option is selected for full functionality) button at top right corner, or run below code.

library(shiny)  
runApp(launch.browser = TRUE)

Cite LocusExplorer

LocusExplorer: a user-friendly tool for integrated visualisation of genetic association data and biological annotations
Tokhir Dadaev¹, Daniel A Leongamornlert¹, Edward J Saunders¹, Rosalind Eeles^1,2 , Zsofia Kote-Jarai¹

¹Department of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
²Royal Marsden NHS Foundation Trust, London, UK

Bioinformatics first published online November 20, 2015 doi:10.1093/bioinformatics/btv690

Abstract

Summary: In this article we present LocusExplorer, a data visualisation and exploration tool for genetic association data. LocusExplorer is written in R using the Shiny library, providing access to powerful R-based functions through a simple user interface. LocusExplorer allows users to simultaneously display genetic, statistical and biological data for humans in a single image and allows dynamic zooming and customisation of the plot features. Publication quality plots may then be produced in a variety of file formats.
Availability and implementation: LocusExplorer is open source and runs through R and a web browser. It is available at www.oncogenetics.icr.ac.uk/LocusExplorer/ or can be installed locally and the source code accessed from https://github.com/oncogenetics/LocusExplorer.

Publications: LocusExplorer plots

• Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans. Al Olama AA, et al.

  • Figure 1

• Accepted, not published yet:

  • Fine-mapping of Prostate Cancer Susceptibility Loci in a Large Meta-Analysis Identifies Candidate Causal Variants. Tokhir Dadaev, et al.
    
  • Prostate cancer meta-analysis of more than 140,000 men identifies 63 novel prostate cancer susceptibility loci. Fredrick R. Schumacher, et al.
    
  • Germline Variation at 8q24 and Prostate Cancer Risk in Men of European Ancestry. Marco Matejcic, et al.
    

Frequently asked questions

See FAQ.

Contact

Questions, suggestions, and bug reports are welcome and appreciated.

• Submit suggestions and bug-reports
  
• Send pull request
  
• Contact email T Dadaev
  

To-do List

https://github.com/oncogenetics/LocusExplorer/issues

Input File Format

Input File Specifications and Format:

• Input files must be delimited flat text files
  
• Headers are required and should use the exact column names described below and used in the example input files
  
• Example files are available at: LocusExplorer/Data/CustomDataExample
  

1. Association File
Association File is mandatory for plot generation. All other files are optional but enhance plot aesthetics and interpretation

• CHR - Chromosome on which variant is located preceded by “chr”. e.g chr2, chrX
• SNP - Variant ID. e.g. rs12345, chr10:104329988:D
• BP - Start coordinate of variant (does not include chromosome or end coordinate for in/del variants). e.g. 104356185
• P - P-value for specified variant
• TYPED - Use code 2 for typed and 1 for imputed variants

2. LD File
LD File is not mandatory but is recommended for more informative plots. If user supplied LD data is not available, see Make LD file tab for instructions of how LD data relative to the index SNP(s) can be obtained from the 1000 Genomes Project Phase 3 Dataset.

• CHR_A - Chromosome on which Index SNP is located (n.b. do not include “chr”). e.g. 2, 23
• BP_A - Index SNP start coordinate (Hg19, do not include chromosome or end coordinate for in/del variants). e.g. 104356185
• SNP_A - Index SNP ID
• CHR_B - Chromosome for SNP in LD with Index SNP (SNP_A)
• BP_B - Start coordinate (Hg19, do not include chromosome or end coordinate for in/del variants) of SNP in LD with Index SNP (SNP_A). e.g. 104315667
• SNP_B - ID of SNP in LD with Index SNP (SNP_A). e.g. rs10786679, chr10:104329988:D
• R2 - LD score between SNP_A and SNP_B (0 to 1). e.g. 1, 0.740917

Note: Lead SNP must be defined relative to itself for plotting purposes, e.g.:

CHR_A   BP_A    SNP_A   CHR_B   BP_B    SNP_B   R2
2   173309618   rs13410475  2   173309618   rs13410475  1
2   173309618   rs13410475  2   172827293   rs148800555 0.0906124

When using plink or LDlink method this does not need to be manually added.

3. [! Disabled !] Custom bedGraph Track
Note: This feature is currently disabled, and will be available in version 0.8. See related GitHub issue.

The first four required bedGraph fields are:

chrom - The name of the chromosome (e.g. chr3, chrY).
chromStart - The starting position of the feature in the chromosome.
chromEnd - The ending position of the feature in the chromosome.
score - A score, any number.

See BedGraph Track Format for more details.

File is tab separated and has no header. This file will be used to create a bar chart. Score is the height, e.g.:

chr2    173292313   173371181   -100
chr2    173500000   173520000   1000

R Session Info

Session info Windows

Application tested on 24/10/2020 11:10

R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)

Matrix products: default

locale:
[1] LC_COLLATE=English_United Kingdom.1252  LC_CTYPE=English_United Kingdom.1252   
[3] LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] oncofunco_0.0.0.9000                    rtracklayer_1.48.0                     
 [3] org.Hs.eg.db_3.11.4                     TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [5] GenomicFeatures_1.40.1                  AnnotationDbi_1.50.3                   
 [7] Biobase_2.48.0                          GenomicRanges_1.40.0                   
 [9] GenomeInfoDb_1.24.2                     IRanges_2.22.2                         
[11] S4Vectors_0.26.1                        ggbio_1.36.0                           
[13] BiocGenerics_0.34.0                     visNetwork_2.0.9                       
[15] igraph_1.2.6                            colourpicker_1.1.0                     
[17] DBI_1.1.0                               cluster_2.1.0                          
[19] acepack_1.4.1                           lattice_0.20-41                        
[21] DT_0.16                                 markdown_1.1                           
[23] knitr_1.30                              ggrepel_0.8.2                          
[25] ggplot2_3.3.2                           data.table_1.13.2                      
[27] lazyeval_0.2.2                          tidyr_1.1.2                            
[29] dplyr_1.0.2                             shiny_1.5.0                            

loaded via a namespace (and not attached):
  [1] colorspace_1.4-1            ellipsis_0.3.1              biovizBase_1.36.0          
  [4] htmlTable_2.1.0             XVector_0.28.0              base64enc_0.1-3            
  [7] dichromat_2.0-0             rstudioapi_0.11             farver_2.0.3               
 [10] bit64_4.0.5                 xml2_1.3.2                  splines_4.0.2              
 [13] Formula_1.2-4               jsonlite_1.7.1              Rsamtools_2.4.0            
 [16] dbplyr_1.4.4                png_0.1-7                   graph_1.66.0               
 [19] BiocManager_1.30.10         compiler_4.0.2              httr_1.4.2                 
 [22] backports_1.1.10            assertthat_0.2.1            Matrix_1.2-18              
 [25] fastmap_1.0.1               later_1.1.0.1               htmltools_0.5.0            
 [28] prettyunits_1.1.1           tools_4.0.2                 gtable_0.3.0               
 [31] glue_1.4.2                  GenomeInfoDbData_1.2.3      reshape2_1.4.4             
 [34] rappdirs_0.3.1              Rcpp_1.0.5                  vctrs_0.3.4                
 [37] Biostrings_2.56.0           nlme_3.1-148                crosstalk_1.1.0.1          
 [40] xfun_0.18                   stringr_1.4.0               mime_0.9                   
 [43] miniUI_0.1.1.1              lifecycle_0.2.0             ensembldb_2.12.1           
 [46] XML_3.99-0.5                zlibbioc_1.34.0             scales_1.1.1               
 [49] BSgenome_1.56.0             VariantAnnotation_1.34.0    ProtGenerics_1.20.0        
 [52] hms_0.5.3                   promises_1.1.1              RBGL_1.64.0                
 [55] SummarizedExperiment_1.18.2 AnnotationFilter_1.12.0     RColorBrewer_1.1-2         
 [58] yaml_2.2.1                  curl_4.3                    memoise_1.1.0              
 [61] gridExtra_2.3               biomaRt_2.44.4              rpart_4.1-15               
 [64] reshape_0.8.8               latticeExtra_0.6-29         stringi_1.5.3              
 [67] RSQLite_2.2.1               checkmate_2.0.0             BiocParallel_1.22.0        
 [70] rlang_0.4.8                 pkgconfig_2.0.3             matrixStats_0.57.0         
 [73] bitops_1.0-6                purrr_0.3.4                 labeling_0.4.2             
 [76] GenomicAlignments_1.24.0    htmlwidgets_1.5.2           bit_4.0.4                  
 [79] tidyselect_1.1.0            GGally_2.0.0                plyr_1.8.6                 
 [82] magrittr_1.5                R6_2.4.1                    generics_0.0.2             
 [85] Hmisc_4.4-1                 DelayedArray_0.14.1         mgcv_1.8-31                
 [88] pillar_1.4.6                foreign_0.8-80              withr_2.3.0                
 [91] survival_3.1-12             RCurl_1.98-1.2              nnet_7.3-14                
 [94] tibble_3.0.4                crayon_1.3.4                OrganismDbi_1.30.0         
 [97] BiocFileCache_1.12.1        jpeg_0.1-8.1                progress_1.2.2             
[100] grid_4.0.2                  blob_1.2.1                  digest_0.6.26              
[103] xtable_1.8-4                httpuv_1.5.4                openssl_1.4.3              
[106] munsell_0.5.0               askpass_1.1  

Prostate raw data

PRACTICAL Data

Prostate summary data can be downloaded at The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium website:

• Meta analysis summary data
• OncoArray summary data
  
• iCOGS chip
  

Contact PRACTICAL

Frequently Asked Quesitons

Q1. Can you tell me the naming convention for the genomic features?

I don't know what uc031tcg.1 is (but I do understand PCAT1, I think that naming convention is RefSeq, are the others also?).

A: I am trying to re-build gene symbols by collapsing transcripts into genes, when transcripts do not overlap with gene symbols, they get named as transcript names - in this case something like uc031tcg.1 - this is UCSC ID. See, udf_GeneSymbol for details. This part of the script is quite heavy and we are working on it.

Q2: Can you tell me how to interpret the wavy lines that cross the plot (for text in the figure legend)?

So far I am using this sentence: “The colored lines spanning the plotting region indicate the extent of LD for the lead SNPs with the same color designation, where the height of the line represents __ and the length of the line represents ___.” Can you send me a better sentence to describe how these lines should be interpreted if I am not on the right track here?

A: It is a loess smoothing for matching hit SNP. If there are 2 SNPs marked with red and green shape and fill, then we will have 2 matching loess lines red and green. Smoothing is using LD values from 1000G phase EUR subset. We can use different cut-offs of LD: LD=0, LD > 0.1, LD >= 0.2, etc., usually LD = 0, i.e.: include all SNP LDs works best.

Y axis is 0 to 1, as in minimum and maximum value for LD - R^2. When wavy lines have similar shape, we can safely assume, those SNPs are the same signal. There is also an “R^2” track, the darker the lines the higher the LD, this track also helps visually see how hit SNPs overlap.

See Manhattan.R.

# LD smooth per hit SNP - optional
if("LDSmooth" %in% input$ShowHideTracks) {
  gg_out <- gg_out +
    geom_smooth(data=plotDatLD(),aes(x=BP,y=R2_Adj,col=LDSmoothCol),
                method="loess",se=FALSE)}

Q3. Can you tell me what is included (maybe even just the data source) for the histone and DNase panels?

What the colour interpretation is for the histone panel?

A: Data from ENCODE project, see links for more info.

• Histone - LocusExplorer README.md, UCSC tables

• DNaseI - LocusExplorer README.md, UCSC tables

Q4: Which operating system it is tested on?

A: See RSessionInfo.md

Q5: “Please download Histone bigWig files” message on the plot?

A: As GitHub has limitations on size of the repositories and files, Histone BigWig files are not included in LocusExplorer/Data/EncodeBigWig/. These files are public and can be downloaded from UCSC golden path - total ~2.5GB. Downloaded bigWig files must be saved in LocusExplorer/Data/EncodeBigWig/ folder.

We are working on server version of LocusExplorer, expected to be live by December 2015. Keep an eye on https://github.com/oncogenetics/LocusExplorer page. This will resolve the issues of different R versions. R packages, and no limits on anntation data - such as bigWig files.

Q6: RStudio crashes when clicked on Plot Settings

After succesfully uploading data using Input Data tab, when clicked on Plot Settings RStudio crashes. This happens even when we run RGUI.

A: It is hard to guess the cause of the crash, it could be package dependencies with different versions. We can try to re-install packages.

!!! WARNING: Below steps will remove all of your installed packages !!!

Try below steps:

1. Run .libPaths() and get library path, choose the one where the user has a write access. e.g.: On my machine I will choose the first of those two listed folders.
   

> .libPaths()
[1] "C:/Users/tdadaev/Documents/R/win-library/3.2" "C:/Program Files/R/R-3.2.2/library"  

myLibraryLocation <- .libPaths()[1]

1. Make list of installed and required packages.

# all packages excluding base
allPackages <- installed.packages()
allPackages <- allPackages[ is.na(allPackages[,4]), 1]

# CRAN packages required by this Application.
cranPackages <- c("shiny", "shinyjs", "data.table", "dplyr", "tidyr", 
                  "ggplot2", "knitr", "markdown", "stringr","DT","seqminer",
                  "lattice","cluster")
# Bioconductor packages required by this Application.
bioPackages <-  c("ggbio","GenomicRanges","TxDb.Hsapiens.UCSC.hg19.knownGene",
                  "org.Hs.eg.db","rtracklayer")

1. Remove all packages, excluding base.

# remove all packages
sapply(allPackages, remove.packages)

1. Reinstall only required packages in myLibraryLocation folder.

#reinstall CRAN packages
sapply(cranPackages, install.packages, lib = myLibraryLocation)

#reinstall Bioconductor packages
source("http://bioconductor.org/biocLite.R")
biocLite("BiocInstaller")
biocLite(bioPackages)

Q7: Plink Error: Duplicate ID '.'

When creating LD file using “LD Tutorial”, getting “Duplicate ID '.'” error.

A: Here is a guide on how to get LD files when we have duplicated ID issue in the 1000 genomes vcf files. I am using your example snplist.txt file

We need to get 1000 genomes vcf for the region, usually I give 250Kb flank based on my SNPs in snplist.txt.

tabix -fh ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20100804/ALL.2of4intersection.20100804.genotypes.vcf.gz 17:1-71324000 > genotypes.vcf

This as you described give us an error.

plink --vcf  genotypes.vcf \
--r2 \
--ld-snp-list snplist.txt \
--ld-window-kb 1000 \
--ld-window 99999 \
--ld-window-r2 0 \
--out myLD

# ...
# Error: Duplicate ID '.'.

As some SNP names are actually dots ".", they don't have an RS ID attached to them. So we need to give them unique names.

Convert to plink format

plink --vcf  genotypes.vcf \
--make-bed \
--out genotypes

Here is the cause of the problem, dots as SNP IDs:

head -n5 genotypes.bim
# 17      .       0       56      T       C
# 17      .       0       78      C       G
# 17      .       0       355     A       G
# 17      .       0       684     T       C
# 17      rs62053745      0       828     T       C

Run this R script to make unique names:

Rscript makeSNPnamesUnique.R genotypes.bim 
head -n5 genotypes.bim 
# 17      17_56_T_C       0       56      T       C
# 17      17_78_C_G       0       78      C       G
# 17      17_355_A_G      0       355     A       G
# 17      17_684_T_C      0       684     T       C
# 17      rs62053745      0       828     T       C

Now, the SNP IDs are fixed, we run plink LD as usual:

plink --bfile genotypes \
--r2 \
--ld-snp-list snplist.txt \
--ld-window-kb 1000 \
--ld-window 99999 \
--ld-window-r2 0 \                   # to have smaller output file: --ld-window-r2 0.2
--out myLD

Note: If the output file is too big, then consider setting higher R2 filter value, for example --ld-window-r2 0.2, meaning only output R2 values that are more than 0.2.

Check the output

wc -l myLD.ld
# 49172 myLD.ld

head -n5 myLD.ld
#  CHR_A         BP_A             SNP_A  CHR_B         BP_B             SNP_B           R2
#     17          834         rs9747082     17           56         17_56_T_C   0.00137027
#     17          834         rs9747082     17          355        17_355_A_G   0.00151223
#     17          834         rs9747082     17          684        17_684_T_C   0.00127126
#     17          834         rs9747082     17          828        rs62053745     0.678207

Finally, we can use this file as input for LocusExplorer.