# Building an annotation database for metaseqR2

# Simple, flexible and reusable annotation for metaseqR2 pipeline

When using the first version of metaseqR, one had to either embed the annotation to the gene/exon/3’ UTR counts, or to download and construct on-the-fly the required annotation when starting from BAM files. Although the counting and gene model construction results could (anf still can) be saved and re-used with other analysis parameters changed (e.g. statistical algorithms), one could not easily add for example new data to an existing dataset without re-running the whole pipeline and re-downloading annotation. On top of that, many times, the main Ensembl servers (when using Ensembl annotations) do not respond well to biomaRt calls, so the whole pipeline may stall until the servers are back.

Another main issue with the annotation used by metaseqR was that there was no straightforward way, provided by metaseqR, to archive and version the annotation used by a specific analysis and was up to the user to take care of reproducibility at this level. Furthermore, there was no straightforward way for a user to plugin own annotation elements (e.g. in the form of a GTF file) and use it in the same manner as standard annotations supported by metaseqR, e.g. when analyzing data from not-so-often studied organisms such as insects. Plugging-in own annotation was possible but usually a painful procedure, which has become now very easy.

The annotation database builder for metaseqR2 remedies the above situations. The `buildAnnotationDatabase` function should be run once with the organisms one requires to have locally to work with and then that’s it! Of course you can manage your database by adding and removing specific annotations (and you even can play with an SQLite browser, although not advised, as the database structure is rather simple). Furthermore, you can use the metaseqR2 annotation database and management mechanism for any other type of analysis where you require to have a simple tab-delimited annotation file, acquired with very little effort.

# Supported organisms

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

- Human (Homo sapiens) genome version hg38
- Human (Homo sapiens) genome version hg19
- Human (Homo sapiens) genome version hg18
- Mouse (Mus musculus) genome version mm10
- Mouse (Mus musculus) genome version mm39
- 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

# Using the local database

## Installation of metaseqR2

To install the metaseqR2 package, start R and enter:

    if(!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")
    BiocManager::install("metaseqR2")

## Setup the database

By default, the database file will be written in the `system.file(package="metaseqR2")` directory. 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 the `localDb` argument pointing to the SQLite database file you created to every metaseqr2 call you perform, otherwise, the pipeline will download and use annotations on-the-fly.

In this vignette, we will build a minimal database comprising only the mouse mm39 genome version from Ensembl. The database will be build in a temporary directory inside session `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(metaseqR2)

    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(mm39=115)
    sources <- ifelse(.Platform$OS.type=="unix",c("ensembl","refseq"),"ensembl")

    # If the example is not running in a multicore system, rc is ignored
    #buildAnnotationDatabase(organisms,sources,forceDownload=FALSE,db=myDb,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="mm39",refdb="ensembl",level="gene",type="gene",
        db=myDb)
    genes

    # Load standard annotation based on 3' UTR coordinates
    utrs <- loadAnnotation(genome="mm39",refdb="ensembl",level="gene",type="utr",
        db=myDb)
    utrs

    # Load summarized exon annotation based used with RNA-Seq analysis
    sumEx <- loadAnnotation(genome="mm39",refdb="ensembl",level="gene",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="mm39",refdb="refseq",level="gene",
            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="mm39",refdb="ensembl",level="gene",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 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 `buildCustomAnnotation` 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` which is not available for Windows.

Let’s try a couple of exammples. The first one is a custom annotation for the Ebola virus from UCSC:

    # 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
        genePredToGtfEnv <- Sys.getenv("GENEPREDTOGTF_BINARY")
        if (genePredToGtfEnv == "") {
            genePredToGtf <- file.path(customDir,"genePredToGtf")
        } else {
            genePredToGtf <- file.path(genePredToGtfEnv)
        }
        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)
        buildCustomAnnotation(
            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",
            level="gene",type="gene",db=myDb)
        eboGenes
    }

Another example, the Atlantic cod 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)
        buildCustomAnnotation(
            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",
            level="gene",type="gene",db=myDb)
        gadGenes
    }

Another example, Armadillo from Ensembl. This should work irrespectively of operating system. We are downloading chromosomal information from UCSC.

    # 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"))

    # 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 files cannot be opened by scanBamParam, so for this example,
    # chromosome length information will not be available when running in Windows.
    bamForInfo <- NULL
    if (.Platform$OS.type == "unix")
        bamForInfo <- paste0("ftp://ftp.ensembl.org/pub/release-98/bamcov/",
            "dasypus_novemcinctus/genebuild/Dasnov3.broad.Ascending_Colon_5.1.bam")

    # Build with the metadata list filled (you can also provide a version)
    buildCustomAnnotation(
        gtfFile=file.path(customDir,"Dasypus_novemcinctus.Dasnov3.0.98.gtf.gz"),
        metadata=list(
            organism="dasNov3_test",
            source="ensembl_test",
            chromInfo=bamForInfo
        ),
        db=myDb
    )

    # Try to retrieve some data
    dasGenes <- loadAnnotation(genome="dasNov3_test",refdb="ensembl_test",
        level="gene",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=54,
        hg19=75,
        hg38=114:115,
        mm9=54,
        mm10=102,
        mm39=114:115,
        rn5=77,
        rn6=114:115,
        dm3=77,
        dm6=114:115,
        danrer7=77,
        danrer10=80,
        danrer11=114:115,
        pantro4=80,
        pantro5=114:115,
        susscr3=80,
        susscr11=114:115,
        equcab2=77
    )

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

    buildAnnotationDatabase(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 relrase) for users who do not wish to create annotation databases on their own. Root access may be required (depending on the metaseqR2 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 for metaseqR2 analyses (not recommended) or you wish to perform an analysis 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 above function does not handle custom annotations in GTF files. In a scenario where you want to use a custom annotation only once, you should supply the `annotation` argument to the `metaseqr2` function, which is almost the same as the `metadata` argument used in `buildCustomAnnotation`, actually augmented by a list member for the GTF file, that is:

    annotation <- list(
        gtf="PATH_TO_GTF",
        organism="ORGANISM_NAME",
        source="SOURCE_NAME",
        chromInfo="CHROM_INFO"
    )

The above argument can be passed to the metaseqr2 call in the respective position.

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

# Session Info

    sessionInfo()

    ## R version 4.6.0 (2026-04-24)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    ##  [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: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] splines   stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] metaseqR2_1.25.0            locfit_1.5-9.12
    ##  [3] limma_3.69.1                DESeq2_1.53.0
    ##  [5] SummarizedExperiment_1.43.0 Biobase_2.73.1
    ##  [7] MatrixGenerics_1.25.0       matrixStats_1.5.0
    ##  [9] GenomicRanges_1.65.0        Seqinfo_1.3.0
    ## [11] IRanges_2.47.2              S4Vectors_0.51.3
    ## [13] BiocGenerics_0.59.6         generics_0.1.4
    ## [15] BiocStyle_2.41.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] BiocIO_1.23.3             survcomp_1.63.0
    ##   [3] bitops_1.0-9              filelock_1.0.3
    ##   [5] tibble_3.3.1              R.oo_1.27.1
    ##   [7] preprocessCore_1.75.0     XML_3.99-0.23
    ##   [9] lifecycle_1.0.5           httr2_1.2.2
    ##  [11] pwalign_1.9.1             edgeR_4.11.1
    ##  [13] globals_0.19.1            MASS_7.3-65
    ##  [15] lattice_0.22-9            dendextend_1.19.1
    ##  [17] magrittr_2.0.5            plotly_4.12.0
    ##  [19] sass_0.4.10               rmarkdown_2.31
    ##  [21] jquerylib_0.1.4           yaml_2.3.12
    ##  [23] otel_0.2.0                DBI_1.3.0
    ##  [25] buildtools_1.0.0          RColorBrewer_1.1-3
    ##  [27] ABSSeq_1.67.0             harmonicmeanp_3.0.1
    ##  [29] abind_1.4-8               ShortRead_1.71.0
    ##  [31] purrr_1.2.2               R.utils_2.13.0
    ##  [33] rmeta_3.0                 RCurl_1.98-1.18
    ##  [35] rappdirs_0.3.4            lava_1.9.1
    ##  [37] seriation_1.5.8           survivalROC_1.0.3.1
    ##  [39] listenv_0.10.1            maketools_1.3.2
    ##  [41] genefilter_1.95.0         parallelly_1.47.0
    ##  [43] annotate_1.91.0           permute_0.9-10
    ##  [45] DelayedMatrixStats_1.35.0 codetools_0.2-20
    ##  [47] DelayedArray_0.39.3       DT_0.34.0
    ##  [49] SuppDists_1.1-9.9         tidyselect_1.2.1
    ##  [51] futile.logger_1.4.9       UCSC.utils_1.9.0
    ##  [53] farver_2.1.2              viridis_0.6.5
    ##  [55] TSP_1.2.7                 rmdformats_1.0.4
    ##  [57] BiocFileCache_3.3.0       webshot_0.5.5
    ##  [59] GenomicAlignments_1.49.0  jsonlite_2.0.0
    ##  [61] iterators_1.0.14          survival_3.8-6
    ##  [63] foreach_1.5.2             tools_4.6.0
    ##  [65] progress_1.2.3            Rcpp_1.1.1-1.1
    ##  [67] glue_1.8.1                prodlim_2026.03.11
    ##  [69] gridExtra_2.3             SparseArray_1.13.2
    ##  [71] BiocBaseUtils_1.15.1      xfun_0.57
    ##  [73] qvalue_2.45.0             GenomeInfoDb_1.49.1
    ##  [75] ca_0.71.1                 dplyr_1.2.1
    ##  [77] HDF5Array_1.41.0          NBPSeq_0.3.1
    ##  [79] formatR_1.14              BiocManager_1.30.27
    ##  [81] fastmap_1.2.0             latticeExtra_0.6-31
    ##  [83] rhdf5filters_1.25.0       caTools_1.18.3
    ##  [85] digest_0.6.39             R6_2.6.1
    ##  [87] gtools_3.9.5              jpeg_0.1-11
    ##  [89] biomaRt_2.69.0            RSQLite_3.53.1
    ##  [91] cigarillo_1.3.0           R.methodsS3_1.8.2
    ##  [93] h5mread_1.5.0             tidyr_1.3.2
    ##  [95] data.table_1.18.4         rtracklayer_1.73.0
    ##  [97] prettyunits_1.2.0         httr_1.4.8
    ##  [99] htmlwidgets_1.6.4         S4Arrays_1.13.0
    ## [101] pkgconfig_2.0.3           gtable_0.3.6
    ## [103] registry_0.5-1            blob_1.3.0
    ## [105] S7_0.2.2                  hwriter_1.3.2.1
    ## [107] XVector_0.53.0            sys_3.4.3
    ## [109] htmltools_0.5.9           bookdown_0.46
    ## [111] log4r_0.4.4               scales_1.4.0
    ## [113] png_0.1-9                 corrplot_0.95
    ## [115] knitr_1.51                lambda.r_1.2.4
    ## [117] reshape2_1.4.5            rjson_0.2.23
    ## [119] curl_7.1.0                zoo_1.8-15
    ## [121] cachem_1.1.0              rhdf5_2.57.1
    ## [123] stringr_1.6.0             KernSmooth_2.23-26
    ## [125] parallel_4.6.0            AnnotationDbi_1.75.0
    ## [127] vsn_3.81.0                restfulr_0.0.16
    ## [129] pillar_1.11.1             grid_4.6.0
    ## [131] vctrs_0.7.3               gplots_3.3.0
    ## [133] dbplyr_2.5.2              beachmat_2.29.0
    ## [135] xtable_1.8-8              evaluate_1.0.5
    ## [137] bsseq_1.49.0              VennDiagram_1.8.2
    ## [139] GenomicFeatures_1.65.0    cli_3.6.6
    ## [141] compiler_4.6.0            futile.options_1.0.1
    ## [143] Rsamtools_2.29.0          rlang_1.2.0
    ## [145] crayon_1.5.3              FMStable_0.1-4
    ## [147] future.apply_1.20.2       heatmaply_1.6.0
    ## [149] interp_1.1-6              aroma.light_3.43.0
    ## [151] affy_1.91.0               plyr_1.8.9
    ## [153] pander_0.6.6              stringi_1.8.7
    ## [155] viridisLite_0.4.3         deldir_2.0-4
    ## [157] BiocParallel_1.47.0       assertthat_0.2.1
    ## [159] txdbmaker_1.9.0           Biostrings_2.81.2
    ## [161] lazyeval_0.2.3            DSS_2.61.0
    ## [163] EDASeq_2.47.0             Matrix_1.7-5
    ## [165] BSgenome_1.81.0           hms_1.1.4
    ## [167] future_1.70.0             sparseMatrixStats_1.25.0
    ## [169] bit64_4.8.2               ggplot2_4.0.3
    ## [171] Rhdf5lib_2.1.0            KEGGREST_1.53.0
    ## [173] statmod_1.5.2             memoise_2.0.1
    ## [175] affyio_1.83.0             bslib_0.11.0
    ## [177] bootstrap_2019.6          bit_4.6.0