RIPAT is an R package for Retroviral vector integration site analysis. This package is developed to
find integration regions on the target sequence and study biological meaning of the integration
pattern such as distance from important genomic factors and genomic features.
It’s distributed from our Github repository (https://github.com/bioinfo16/RIPAT) and Bioconductor.
RIPAT will be run on all R versions but, we strongly recommend that user install the R version 3.5.3 or higher. Plus, RIPAT needs these packages below to run. By the install command (writted below), RIPAT install all packages automatically. If user already install some of packages listed up, RIPAT will be reinstall proper version of these packages itself.
Package | Version |
---|---|
GenomicRanges | >= 1.34.4 |
ggplot2 | >= 3.1.0 |
grDevices | >= 3.5.3 |
karyoploteR | >= 1.6.3 |
openxlsx | >= 4.1.4 |
plyr | >= 1.8.4 |
RColorBrewer | >= 1.1-2 |
regioneR | >= 1.12.0 |
stats | >= 3.5.3 |
stringr | >=1.3.1 |
utils | >= 3.5.3 |
biomaRt | >= 2.38.0 |
rtracklayer | >= 1.42.2 |
We strongly recommand the way using devtools package in R.
But, you can download this package as zip-file format from our github repository.
Plus, we will upload this package to Bioconductor repository. It’s in progress.
Bioconductor (Recommended)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("RIPAT")
devtools::install_github("bioinfo16/RIPAT")
R CMD INSTALL [PACKAGE_DOWNLOAD_PATH]
RIPAT provides four types of site annotation analysis, Gene, CpG site, Repeat and pathogenic variant.
This R package uses Ensembl database, UCSC genome database and NCBI ClinVar in site annotation. Ensembl database is used to gene annotation. UCSC genome database is needed to annotation by gene, CpG site and repeat sequence. Pathogenic variant annotation uses from NCBI Clinvar database. This package can annotate integration sites on the human genome only. Avaliable target version is GRCh37 and GRCh38.
RIPAT can do integration pattern analysis with local alignment tools such as BLAST and BLAT. Each of cases makes the tab-delimited result file. Specific format for RIPAT is depicted to the table below.
In this manual, Gene annotation is written as a example.
Other annotation functions are working as a example case.
makeData RIPAT provides the function that download genomic data files. When user enters target genome version to this function, all data are downloaded automatically.
library(RIPAT)
# Download gene data file
makeData(organism = 'GRCh37', dataType = 'gene')
makeInputObj
Annotation funcions in this package need specific format of inputs.
So, this function converts raw local alignment result to R object with the avaliable format.
User can use BLAST(output format 6 in command line version) and BLAT output format specifically.
blast_obj=makeInputObj(inFile='blast_result.txt',
vectorPos='front',
mapTool='blast',
outPath='.',
outFileName='A5_15856M_BLAST')
annoByGene
This function uses Gene and TSS data from Ensembl to check the feature of integration regions.
User can get site information inserted within genes and distribution from gene and TSS coordinations.
Plus, user can do random distribution analysis by this function.
With random integration site analysis
blast_gene10K=annoByGene(hits=blast_obj,
organism='GRCh37',
interval=5000,
range=c(-20000, 20000),
doRandom=TRUE,
randomSize=10000,
includeUndecided=FALSE,
outPath='.',
outFileName='A5_15856M_BLAST_10K')
[ Integration site distribution from Genes ]
[ Integration site distribution from transcription start site (TSS) ]
Without random integration site analysis
blast_gene_norandom=annoByGene(hits=blast_obj,
organism='GRCh37',
interval=5000,
range=c(-20000, 20000),
doRandom=FALSE,
includeUndecided=FALSE,
outPath='.',
outFileName='A5_15856M_BLAST_norandom')
[ Integration site distribution from Genes ]
[ Integration site distribution from transcription start site (TSS) ]
drawingKaryo
To see the retroviral vector integration site at glance, we procide the karyoplot with integration mark.
## Karyogram marked genomic spots
karyo_obj_gene = drawingKaryo(hits=blast_obj,
organism="GRCh37",
feature=blast_gene10K$Gene_data,
includeUndecided=FALSE,
outPath=getwd(),
outFileName='A5_15856M_BLAST_karyo_gene')
[ Integration sites marked with genes ]
makeDocument
RIPAT rearrange the result of each annotation function. This function is for users who are not familar to deal R for RIPAT. This function makes two documents about exprimental(observed) data and random set data. In addition, it show the histogram to see the distribution from featres and draw a p-value plot by p-value to see the result of randoma set analysis at glance.
makeDocument(res = blast_gene10K,
dataType = 'gene',
excelOut = TRUE,
includeUndecided = FALSE,
outPath = getwd(),
outFileName = 'A5_15856M_BLAST_10K')
[ Histogram of experimental data ]
[ Histogram of random data ]
In p-value plot, full-colored circle means significant difference between exprimental data and random data (p-value <= 0.05). Circle size is p-value transformation score for visualization.
[ P-value plot ]
Example codes in this vignette was run under the following conditions:
R version 3.5.3 (2019-03-11)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Matrix products: default
locale:
[1] LC_COLLATE=Korean_Korea.949 LC_CTYPE=Korean_Korea.949
[3] LC_MONETARY=Korean_Korea.949 LC_NUMERIC=C
[5] LC_TIME=Korean_Korea.949
attached base packages:
[1] parallel stats4 stats
[4] graphics grDevices utils
[7] datasets methods base
other attached packages:
[1] GenomicRanges_1.34.0 GenomeInfoDb_1.18.2 IRanges_2.16.0
[4] RIPAT_0.99.0 S4Vectors_0.20.1 BiocGenerics_0.28.0
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 ellipsis_0.3.0
[3] rprojroot_1.3-2 biovizBase_1.30.1
[5] htmlTable_1.13.3 XVector_0.22.0
[7] base64enc_0.1-3 fs_1.3.1
[9] dichromat_2.0-0 rstudioapi_0.11
[11] remotes_2.1.1 bit64_0.9-7
[13] AnnotationDbi_1.44.0 fansi_0.4.1
[15] splines_3.5.3 knitr_1.28
[17] pkgload_1.0.2 Formula_1.2-3
[19] Rsamtools_1.34.1 cluster_2.0.7-1
[21] BiocManager_1.30.10 compiler_3.5.3
(...skipped...)
If you want to use RIPAT in your work, write it in your publications:
##
## To cite package 'RIPAT' in publications use:
##
## Min-Jeong Baek, Kwang-il Lim and In-Geol Choi. RIPAT (Retroviral
## Integration Pattern Analysis Tool): an R package for analyzing
## retroviral integration sites on human genome. Seoul, Korea (2020).
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {RIPAT : Retroviral Integration Pattern Analysis Tool},
## author = {Min-Jeong Baek and Kwang-il Lim and In-Geol Choi},
## year = {2020},
## }