SiTaDelA - Simple, flexible and reusable tab-delimited genome annotations

Next Generation Sequencing has introduced a massive need for working with integer interval data which correspond to actual chromosomal regions, depicted in linear representations. As a result, previously under-developed algorithms for working with such data have tremendously evolved. Maybe the most common application where genomic intervals are used is overlapping a set of query intervals with a set of reference intervals. One typical example is counting the reads produced e.g. from an RNA-Seq experiment and assigning them to genes of interest through overlapping their mapped coordinates with those of the genes over a reference genome. As a result, collections of such reference genomic regions for several reference organisms are essential for the quick interrogation of the latter.

The generation of genomic coordinate systems are nowadays mainstream. Typical ways of reference genomic region representations are:

• BED files, which are simple tab-delimited files with at least 3 columns including the main reference sequence name (e.g. a chromosome), its start and its end.
• More complex structured files such as GTF and GFF which also contain structures such as exons, different transcripts anf untranslated regions.

Bioconductor offers great infrastructures for fast genomic interval calculations which are now very mature, high-level and cover most needs. It also offers many comprehensive and centrally maintained genomic interval annotation packages as well as tools to quickly create custom annotation packages, such as AnnotationForge. These packages, are primarily designed to capture genomic structures (genes, transcripts, exons etc.) accurately and place them in a genomic interval content suitable for fast calculations. While this is more than sufficient for many users and work out-of-the-box, especially for less experienced R users, they may miss certain characteristics which may be also useful for many users. Such additional elements are often required by tools that report e.g. transcript biotypes (such as those in Ensembl) and do not gather mappings between elements of the same annotation (e.g. gene, transcript, exon ids) in one place in a more straightforward manner. More specifically, some elements which are not directly achievable with standard Bioconductor annotation packages include:

• Simple tab-delimited (or in GRanges objects) genomic interval annotations capturing several characteristics of these annotations (biotype, GC content).
• Centralization of simple tab-delimited annotations for many organisms and several genomic interval types in one package.
• Versioning of these annotations under the same database instead of many, dispersed packages which may be difficult to track and upgrade, especially when transitioning between Bioconductor versions.
• Gene and transcript versioning (when available, e.g. in NCBI annotations) which is essential for applications related to precision medicine and diagnostic procedures.
• A unified interface to several genomic interval annotation sources.

SiTaDelA (Simple Tab Delimited Annotations), through efficient and extensive usage of Bioconductor facilites offers these additional functionalities along with certain levels of automation. More specifically, the sitadela package offers:

• Simple tab-delimited (easily output also as GRanges objects) genomic interval annotations for several transcription unit types with additional characteristics (gene GC content, biotypes).
• A centralized annotation building and retrieval system, supporting several organisms, versions and annotation resources as well as custom user annotations coming in GTF/GFF format.
• Versioning of the annotation builds to improve reproducibility and tracking.
• A unified interface to several genomic interval annotation sources which automates database build but also fetches annotations on-the-fly if not already present in the build.
• Centralized gene and transcript versioning where available (e.g. NCBI), especially useful for genomics precision medicine appplications and the respective diagnostic processes.
• Additional portability from Bioconductor to other applications through the simple database schema adopted.
• Additional attributes such as corrected feature lengths (i.e. corrected gene lengths based on sum of lengths of coding regions, to be used e.g. for RNA abundance estimation and normalization).

The sitadela annotation database building is extremely simple. The user defines a list of desired annotations (organisms, sources, versions) and supplies them to the addAnnotation function which in turn creates a new or updates a current database. A custom, non-directly supported organism annotation can be imported through the addCustomAnnotation function and annotations not needed anymore can be removed with the removeAnnotation function. Finally, as the built can require some time, especially if many organisms and sources are required for a local database, we maintain pre-built databases which are built periodically (e.g. upon a new Ensembl release).

Supported organisms

The following organisms (essentially genome versions) are supported for automatic database builds:

• Human (Homo sapiens) genome version hg38 (or GRCh38)
• Human (Homo sapiens) genome version hg19 (or GRCh37)
• Human (Homo sapiens) genome version hg18
• Mouse (Mus musculus) genome version mm10 (or GRCm37)
• Mouse (Mus musculus) genome version mm9
• Rat (Rattus norvegicus) genome version rn6
• Rat (Rattus norvegicus) genome version rn5
• Fruitfly (Drosophila melanogaster) genome version dm6
• Fruitfly (Drosophila melanogaster) genome version dm3
• Zebrafish (Danio rerio) genome version danRer7
• Zebrafish (Danio rerio) genome version danRer10
• Zebrafish (Danio rerio) genome version danRer11
• Chimpanzee (Pan troglodytes) genome version panTro4
• Chimpanzee (Pan troglodytes) genome version panTro5
• Pig (Sus scrofa) genome version susScr3
• Pig (Sus scrofa) genome version susScr11
• Horse (Equus cabalus) genome version equCab2
• Arabidopsis (Arabidobsis thaliana) genome version TAIR10

Please note that if genomic annotations from UCSC, RefSeq or NCBI are required, the following BSgenome packages are required (depending on the organisms to be installed) in order to calculate GC content for gene annotations. Also note that there is no BSgenome package for some of the sitadela supported organisms and therefore GC contents will not be available anyway.

• BSgenome.Hsapiens.UCSC.hg18
• BSgenome.Hsapiens.UCSC.hg19
• BSgenome.Hsapiens.UCSC.hg38
• BSgenome.Mmusculus.UCSC.mm9
• BSgenome.Mmusculus.UCSC.mm10
• BSgenome.Rnorvegicus.UCSC.rn5
• BSgenome.Rnorvegicus.UCSC.rn6
• BSgenome.Dmelanogaster.UCSC.dm3
• BSgenome.Dmelanogaster.UCSC.dm6
• BSgenome.Drerio.UCSC.danRer7
• BSgenome.Drerio.UCSC.danRer10

Is is therefore advised to install these BSgenome packages in advance.

Building and using a SiTaDelA local database

Installation of sitadela

To install the sitadela package, one should start R and enter:

if(!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("sitadela")

To install the latest (perhaps non-stable) version:

library(remotes)
remotes::install_github("pmoulos/sitadela")

Setup the database

By default, the database file will be written in the tools::R_user_dir("sitadela","data") directory and the file is called "annotation.sqlite". By default the build function will ask for the path where the database will be installed. You can specify another prefered destination for it using the db argument in the function call, but if you do that, you will have to supply an argument pointing to the SQLite database file you created to every sitadela package function call you perform, or any other function that uses sitadela annotations, otherwise, the annotation will be downloaded and formatted on-the-fly instead of using the local database. Upon loading sitadela, an option is added to the R environment pointing to the default sitadela annotation database. If you wish to change that location and do not wish to supply the database to other function calls, you can change the default location of the annotation to your preferred location with the setDbPath function in the beginning of your script/function that uses the annotation database.

In this example, we will build a minimal database comprising only the mouse mm9 genome version from Ensembl. The database will be built in a temporary directory inside the current R session’s tempdir().

Important note: As the annotation build function makes use of Kent utilities for creating 3’UTR annotations from RefSeq and UCSC, the latter cannot be built in Windows. Therefore it is advised to either build the annotation database in a Linux system or use our pre-built databases.

library(sitadela)

buildDir <- file.path(tempdir(),"test_anndb")
dir.create(buildDir)

# The location of the custom database
myDb <- file.path(buildDir,"testann.sqlite")

# Since we are using Ensembl, we can also ask for a version
organisms <- list(mm9=67)
sources <- ifelse(.Platform$OS.type=="unix",c("ensembl","refseq"),"ensembl")

# If the example is not running in a multicore system, rc is ignored
addAnnotation(organisms,sources,forceDownload=FALSE,db=myDb,rc=0.5)

## Alternatively
# setDbPath(myDb)
# addAnnotation(organisms,sources,forceDownload=FALSE,rc=0.5)

Use the database

Now, that a small database is in place, let’s retrieve some data. Remember that since the built database is not in the default location, we need to pass the database file in each data retrieval function. The annotation is retrieved as a GRanges object by default.

# Load standard annotation based on gene body coordinates
genes <- loadAnnotation(genome="mm9",refdb="ensembl",type="gene",db=myDb)
genes

# Load standard annotation based on 3' UTR coordinates
utrs <- loadAnnotation(genome="mm9",refdb="ensembl",type="utr",db=myDb)
utrs

# Load summarized exon annotation based used with RNA-Seq analysis
sumEx <- loadAnnotation(genome="mm9",refdb="ensembl",type="exon",
    summarized=TRUE,db=myDb)
sumEx

# Load standard annotation based on gene body coordinates from RefSeq
if (.Platform$OS.type=="unix") {
    refGenes <- loadAnnotation(genome="mm9",refdb="refseq",type="gene",
        db=myDb)
    refGenes
}

Or as a data frame if you prefer using asdf=TRUE. The data frame however does not contain metadata like Seqinfo to be used for any susequent validations:

# Load standard annotation based on gene body coordinates
genes <- loadAnnotation(genome="mm9",refdb="ensembl",type="gene",db=myDb,
    asdf=TRUE)
head(genes)

Add a custom annotation

Apart from the supported organisms and databases, you can add a custom annotation. Such an annotation can be:

• A non-supported organism (e.g. an insect or another mammal e.g. dog)
• A modification or further curation you have done to existing/supported annotations
• A supported organism but from a different source
• Any other case where the provided annotations are not adequate

This can be achieved through the usage of GTF/GFF files, along with some simple metadata that you have to provide for proper import to the annotation database. This can be achieved through the usage of the addCustomAnnotation function. Details on required metadata can be found in the function’s help page.

Important note: Please note that importing a custom genome annotation directly from UCSC (UCSC SQL database dumps) is not supported in Windows as the process involves using the genePredToGtf program which is not available for Windows.

Let’s try a couple of examples. The first one uses example GTF files shipped with the package. These are sample chromosomes from:

• Atlantic cod (Gadus morhua), sequence HE567025
• Armadillo (Dasypus novemcinctus), sequence JH569334
• European bass (Dicentrarchus labrax), chromosome LG3

Below, we test custom building with reference sequence HE567025 of Atlantic cod:

gtf <- system.file(package="sitadela","extdata",
    "gadMor1_HE567025.gtf.gz")
chrom <- system.file(package="sitadela","extdata",
    "gadMor1_HE567025.txt.gz")
chromInfo <- read.delim(chrom,header=FALSE,row.names=1)
names(chromInfo) <- "length"
metadata <- list(
    organism="gadMor1_HE567025",
    source="sitadela_package",
    chromInfo=chromInfo
)
tmpdb <- tempfile()

addCustomAnnotation(gtfFile=gtf,metadata=metadata,db=tmpdb)

# Try to retrieve some data
g <- loadAnnotation(genome="gadMor1_HE567025",refdb="sitadela_package",
    type="gene",db=tmpdb)
g

# Delete the temporary database
unlink(tmpdb)

The next one is part of a custom annotation for the Ebola virus from UCSC:

gtf <- system.file(package="sitadela","extdata",
    "eboVir3_KM034562v1.gtf.gz")
chrom <- system.file(package="sitadela","extdata",
    "eboVir3_KM034562v1.txt.gz")
chromInfo <- read.delim(chrom,header=FALSE,row.names=1)
names(chromInfo) <- "length"
metadata <- list(
    organism="gadMor1_HE567025",
    source="sitadela_package",
    chromInfo=chromInfo
)
tmpdb <- tempfile()

addCustomAnnotation(gtfFile=gtf,metadata=metadata,db=tmpdb)

# Try to retrieve some data
g <- loadAnnotation(genome="gadMor1_HE567025",refdb="sitadela_package",
    type="gene",db=tmpdb)
g

# Delete the temporary database
unlink(tmpdb)

Again, please note that complete annotations from UCSC require the genePredToGtf tool from the UCSC tools base and runs only on Linux. The tool is required only for building 3’ UTR annotations from UCSC, RefSeq and NCBI, all of which are being retrieved from the UCSC databases. The next example (full EBOLA virus annotation) demonstrates how this is done in a Unix based machine:

# Setup a temporary directory to download files etc.
customDir <- file.path(tempdir(),"test_custom")
dir.create(customDir)

# Convert from GenePred to GTF - Unix/Linux only!
if (.Platform$OS.type == "unix" && !grepl("^darwin",R.version$os)) {
    # Download data from UCSC
    goldenPath="http://hgdownload.cse.ucsc.edu/goldenPath/"
    # Gene annotation dump
    download.file(paste0(goldenPath,"eboVir3/database/ncbiGene.txt.gz"),
        file.path(customDir,"eboVir3_ncbiGene.txt.gz"))
    # Chromosome information
    download.file(paste0(goldenPath,"eboVir3/database/chromInfo.txt.gz"),
        file.path(customDir,"eboVir3_chromInfo.txt.gz"))

    # Prepare the build
    chromInfo <- read.delim(file.path(customDir,"eboVir3_chromInfo.txt.gz"),
        header=FALSE)
    chromInfo <- chromInfo[,1:2]
    rownames(chromInfo) <- as.character(chromInfo[,1])
    chromInfo <- chromInfo[,2,drop=FALSE]
    
    # Coversion from genePred to GTF
    genePredToGtf <- file.path(customDir,"genePredToGtf")
    if (!file.exists(genePredToGtf)) {
        download.file(
        "http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/genePredToGtf",
            genePredToGtf
        )
        system(paste("chmod 775",genePredToGtf))
    }
    gtfFile <- file.path(customDir,"eboVir3.gtf")
    tmpName <- file.path(customDir,paste(format(Sys.time(),"%Y%m%d%H%M%S"),
        "tgtf",sep="."))
    command <- paste0(
        "zcat ",file.path(customDir,"eboVir3_ncbiGene.txt.gz"),
        " | ","cut -f2- | ",genePredToGtf," file stdin ",tmpName,
        " -source=eboVir3"," -utr && grep -vP '\t\.\t\.\t' ",tmpName," > ",
        gtfFile
    )
    system(command)

    # Build with the metadata list filled (you can also provide a version)
    addCustomAnnotation(
        gtfFile=gtfFile,
        metadata=list(
            organism="eboVir3_test",
            source="ucsc_test",
            chromInfo=chromInfo
        ),
        db=myDb
    )

    # Try to retrieve some data
    eboGenes <- loadAnnotation(genome="eboVir3_test",refdb="ucsc_test",
        type="gene",db=myDb)
    eboGenes
}

Another example, the full Atlantic cod genome annotation from UCSC. The same things apply for the operating system.

if (.Platform$OS.type == "unix") {
    # Gene annotation dump
    download.file(paste0(goldenPath,"gadMor1/database/augustusGene.txt.gz"),
        file.path(customDir,"gadMori1_augustusGene.txt.gz"))
    # Chromosome information
    download.file(paste(goldenPath,"gadMor1/database/chromInfo.txt.gz",sep=""),
        file.path(customDir,"gadMori1_chromInfo.txt.gz"))

    # Prepare the build
    chromInfo <- read.delim(file.path(customDir,"gadMori1_chromInfo.txt.gz"),
        header=FALSE)
    chromInfo <- chromInfo[,1:2]
    rownames(chromInfo) <- as.character(chromInfo[,1])
    chromInfo <- chromInfo[,2,drop=FALSE]
    
    # Coversion from genePred to GTF
    genePredToGtf <- file.path(customDir,"genePredToGtf")
    if (!file.exists(genePredToGtf)) {
        download.file(
        "http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/genePredToGtf",
            genePredToGtf
        )
        system(paste("chmod 775",genePredToGtf))
    }
    gtfFile <- file.path(customDir,"gadMori1.gtf")
    tmpName <- file.path(customDir,paste(format(Sys.time(),"%Y%m%d%H%M%S"),
        "tgtf",sep="."))
    command <- paste0(
        "zcat ",file.path(customDir,"gadMori1_augustusGene.txt.gz"),
        " | ","cut -f2- | ",genePredToGtf," file stdin ",tmpName,
        " -source=gadMori1"," -utr && grep -vP '\t\.\t\.\t' ",tmpName," > ",
        gtfFile
    )
    system(command)

    # Build with the metadata list filled (you can also provide a version)
    addCustomAnnotation(
        gtfFile=gtfFile,
        metadata=list(
            organism="gadMor1_test",
            source="ucsc_test",
            chromInfo=chromInfo
        ),
        db=myDb
    )

    # Try to retrieve some data
    gadGenes <- loadAnnotation(genome="gadMor1_test",refdb="ucsc_test",
        type="gene",db=myDb)
    gadGenes
}

Another example, Armadillo from Ensembl. This should work irrespectively of operating system. We are downloading chromosomal information from UCSC. Again, a small dataset included in the package is included in this vignette. See the commented code below for the full annotation case.

## Gene annotation dump from Ensembl
#download.file(paste0("ftp://ftp.ensembl.org/pub/release-98/gtf/",
#    "dasypus_novemcinctus/Dasypus_novemcinctus.Dasnov3.0.98.gtf.gz"),
#    file.path(customDir,"Dasypus_novemcinctus.Dasnov3.0.98.gtf.gz"))
#
# gtfFile <- file.path(customDir,"Dasypus_novemcinctus.Dasnov3.0.98.gtf.gz")
#
## Chromosome information will be provided from the following BAM file
## available from Ensembl. We have noticed that when using Windows as the OS,
## a remote BAM file cannot be opened by scanBamParam, so for this example,
## chromosome length information will not be available when running in Windows.
#chromInfo <- NULL
#if (.Platform$OS.type == "unix")
#    chromInfo <- paste0("ftp://ftp.ensembl.org/pub/release-98/bamcov/",
#        "dasypus_novemcinctus/genebuild/Dasnov3.broad.Ascending_Colon_5.1.bam")

gtfFile <- system.file(package="sitadela","extdata",
    "dasNov3_JH569334.gtf.gz")
chromInfo <- read.delim(system.file(package="sitadela",
    "extdata","dasNov3_JH569334.txt.gz"),header=FALSE)

# Build with the metadata list filled (you can also provide a version)
addCustomAnnotation(
    gtfFile=gtfFile,
    metadata=list(
        organism="dasNov3_test",
        source="ensembl_test",
        chromInfo=chromInfo
    ),
    db=myDb
)

# Try to retrieve some data
dasGenes <- loadAnnotation(genome="dasNov3_test",refdb="ensembl_test",
    type="gene",db=myDb)
dasGenes

A complete build

A quite complete build (with latest versions of Ensembl annotations) would look like (supposing the default annotation database location):

organisms <- list(
    hg18=67,
    hg19=75,
    hg38=101:102,
    mm9=67,
    mm10=101:102,
    rn5=77,
    rn6=101:102,
    dm3=77,
    dm6=101:102,
    danrer7=77,
    danrer10=91,
    danrer11=101:102,
    pantro4=90,
    pantro5=101:102,
    susscr3=89,
    susscr11=101:102,
    equcab2=94,
    equcab3=101:102
)

sources <- c("ensembl","ucsc","refseq","ncbi")

addAnnotation(organisms,sources,forceDownload=FALSE,rc=0.5)

The aforementioned complete built can be found here Complete builts will become available from time to time (e.g. with every new Ensembl release) for users who do not wish to create annotation databases on their own. Root access may be required (depending on the sitadela library location) to place it in the default location where it can be found automatically.

Annotations on-the-fly

If for some reason you do not want to build and use an annotation database but you wish to benefit from the sitadela simple formats nonetheless, or even to work with an organism that does not yet exist in the database, the loadAnnotation function will perform all required actions (download and create a GRanges object) on-the-fly as long as there is an internet connection. However, the aforementioned function does not handle custom annotations in GTF files. In that case, you should use the importCustomAnnotation function with a list describing the GTF file, that is:

metadata <- list(
    organism="ORGANISM_NAME",
    source="SOURCE_NAME",
    chromInfo="CHROM_INFO"
)

The above argument can be passed to the importCustomAnnotation call in the respective position.

For further details about custom annotations on the fly, please check addCustomAnnotation and importCustomAnnotation functions.