Simplifying Genomic Annotations in R

Introduction to CellbaseR

This R package makes use of the exhaustive RESTful Web service API that has been implemented for the Cellabase database. It enable researchers to query and obtain a wealth of biological information from a single database saving a lot of time. Another benefit is that researchers can easily make queries about different biological topics and link all this information together as all information is integrated.

knitr::opts_chunk$set(echo = TRUE)

CellbaseR Classes and Methods

The CellbaseR class

This is an S4 class that holds the basic configuration for connecting to the Cellbase web services. Let us start by loading the library.

# to get the default CellbaseR object (human data, from genome GRCh37)
# A default cellbaseR object is created as follows
cb <- CellBaseR()

The CellbaseR Methods

The cellbaseR package provide many methods to query the cellbase webservices, they include:

In all cases the user is expected to provide the ids for the query and the resource to be queried, in addition to the CellbaseQuery object they created. For example, a query through the cbGene will look something like this


cb <- CellBaseR()
genes <- c("TP73","TET1")
res <- getGene(object = cb, ids = genes, resource = "info")
# as you can see the res dataframe also contains a transcripts column 
# which is in fact a list column of nested dataframes, to get the
# trasnscripts data.frame for first gene
TET1_transcripts <- res$transcripts[[1]]


# making a query through cbRegion to get all the clinically relevant variants 
# in a specific region
cb <- CellBaseR()
res <- getRegion(object=cb,ids="17:1000000-1005000",
# to get all conservation data in this region
res <- getRegion(object=cb,ids="17:1000000-1005000",
#likewise to get all the regulatory data for the same region
res <- getRegion(object=cb,ids="17:1000000-1005000", resource="regulatory")


Getting annotation for a specific variant

cb <- CellBaseR()
res2 <- getVariant(object=cb, ids="1:169549811:A:G", resource="annotation")
# to get the data 
res2 <- cbData(res2)
str(res2, 1)

A very powerfull feature of cellbaseR is ability to fetch a wealth of clinical data, as well as abiliy to fiter these data by gene, phenotype, rs, etc…


cb <- CellBaseR()
# First we have to specify aour param, we do that by creating an object of
# class CellbaseParam
cbparam <- CellBaseParam(feature = "TET1", assembly = "GRCh38", limit = 50)
# Note that cbClinical does NOT require any Ids to be passed, only the param
# and of course the CellbaseQuery object
res <- getClinical(object=cb,param=cbparam)

cellbaseR wrappers

cellbaseE package also provides many wrapper functions around commomn cellbaseR queries. These include:
- getClinicalByGene - getTranscriptByGene - getGeneInfo - getSnpByGene - getProteinInfo - getClinicalByRegion - getConservationByRegion - getRegulatoryByRegion - getTfbsByRegion - getCaddScores - getVariantAnnotation

CellbaseR utilities


A usefull utility for fast building of gene models, compatible with Gviz package for genomic visualization

cb <- CellBaseR()
test <- createGeneModel(object = cb, region = "17:1500000-1550000")
  testTrack <- Gviz::GeneRegionTrack(test)
  Gviz::plotTracks(testTrack, transcriptAnnotation='symbol')


This utility allows users to annoate genomic variants from small to medium-sized vcf files directly from the cellbase server, with a wealth of genomic annotations including riche clinical annotations like clinvar, gwas, and cosmic data, as well as conservation and functional scores like phast and cadd scores , without the need to download any files.

cb <- CellBaseR()
fl <- system.file("extdata", "hapmap_exome_chr22_200.vcf.gz",package = "cellbaseR" )
res <- AnnotateVcf(object=cb, file=fl, BPPARAM = bpparam(workers=2),batch_size = 100)
vcf <- readVcf(fl, "hg19")

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cellbaseR documentation built on Nov. 8, 2020, 5:38 p.m.