The Bioc.gff package provides support for the General Feature Format (GFF) file format, which is widely used for representing genomic features and annotations. This package allows users to read, write, and manipulate GFF versions 1, 2 (GTF), and 3 in R, making it easier to work with genomic data.
While the
rtracklayer
package offers robust GFF support, it is a large package with many
features beyond file import. Bioc.gff fills a specific niche in the
Bioconductor ecosystem by providing a lightweight, focused solution with
minimal dependencies. This modularity is beneficial for developers of
other packages who need to parse GFF files without inheriting the
extensive dependency footprint of rtracklayer. For the end user, it
offers a simple and direct interface for GFF manipulation.
Note that much of the code in the package is ported and adapted from the
rtracklayer
Bioconductor package. The intention is that the GFF functionality
residing in
rtracklayer will
be removed from that package in favor of using Bioc.gff, thereby
reducing its size and complexity.
You can install the Bioc.gff package from Bioconductor using the
following:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Bioc.gff")library(Bioc.gff)
library(GenomicRanges)The Bioc.gff package provides functions to read and write GFF files,
as well as to manipulate GFF data structures. The following sections
provide some examples of how to use the package.
You can read GFF files using the readGFF function. This function
supports GFF versions 1, 2, and 3. The following example demonstrates
how to read a GFF file:
gff_file <- system.file("extdata", "genes.gff3", package = "Bioc.gff")The import function deduces that the input string is a GFF file type
and calls the appropriate method for reading the GFF file.
import(gff_file)
#> GRanges object with 31 ranges and 10 metadata columns:
#> seqnames ranges strand | source type score phase ID Name geneName Alias
#> <Rle> <IRanges> <Rle> | <factor> <factor> <numeric> <integer> <character> <character> <character> <CharacterList>
#> [1] chr10 92828-95504 - | rtracklayer gene 5 <NA> GeneID:347688 TUBB8 tubulin, beta 8 FLJ40100,TUBB8
#> [2] chr10 92828-95178 - | rtracklayer mRNA NA <NA> 873 TUBB8 <NA>
#> [3] chr10 92828-95504 - | rtracklayer mRNA NA <NA> 872 TUBB8 <NA>
#> [4] chr10 92828-94054 - | rtracklayer exon NA <NA> <NA> <NA> <NA>
#> [5] chr10 92997-94054 - | rtracklayer CDS NA <NA> <NA> <NA> <NA>
#> ... ... ... ... . ... ... ... ... ... ... ... ...
#> [27] chr12 89675-89827 + | rtracklayer CDS NA <NA> <NA> <NA> <NA>
#> [28] chr12 90587-90655 + | rtracklayer exon NA <NA> <NA> <NA> <NA>
#> [29] chr12 90587-90655 + | rtracklayer CDS NA <NA> <NA> <NA> <NA>
#> [30] chr12 90796-91263 + | rtracklayer exon NA <NA> <NA> <NA> <NA>
#> [31] chr12 90796-91263 * | rtracklayer CDS NA <NA> <NA> <NA> <NA>
#> genome Parent
#> <character> <CharacterList>
#> [1] hg19
#> [2] <NA> GeneID:347688
#> [3] <NA> GeneID:347688
#> [4] <NA> 872,873
#> [5] <NA> 872,873
#> ... ... ...
#> [27] <NA> 4644
#> [28] <NA> 4644
#> [29] <NA> 4644
#> [30] <NA> 4644
#> [31] <NA> 4644
#> -------
#> seqinfo: 2 sequences from an unspecified genome; no seqlengthsHere the output object is a GRanges class, which contains genomic
coordinates and associated metadata. The GRanges object indicates the
ranges with the seqnames, ranges, and strand, columns. The
associated metadata is stored in the mcols slot, which (in this
example) includes attributes like source, type, phase, ID,
Name, geneName, Alias and genome.
You can also selectively import ranges from the GFF file using the
which argument. This allows you to filter the data based on specific
criteria, such as chromosome or strand. For example, to import only the
ranges on chromosome 1:
which <- GRanges("chr10:90000-93000")
import(gff_file, which = which, genome = "hg19")
#>
#> Attaching package: 'rtracklayer'
#> The following objects are masked from 'package:Bioc.gff':
#>
#> asGFF, export.gff, export.gff1, export.gff2, export.gff3, GFF1File, GFF2File, GFF3File, GFFcolnames, GFFFile, GTFFile,
#> GVFFile, import.gff, import.gff1, import.gff2, import.gff3, readGFF
#> GRanges object with 5 ranges and 10 metadata columns:
#> seqnames ranges strand | source type score phase ID Name geneName Alias
#> <Rle> <IRanges> <Rle> | <factor> <factor> <numeric> <integer> <character> <character> <character> <CharacterList>
#> [1] chr10 92828-95504 - | rtracklayer gene 5 <NA> GeneID:347688 TUBB8 tubulin, beta 8 FLJ40100,TUBB8
#> [2] chr10 92828-95178 - | rtracklayer mRNA NA <NA> 873 TUBB8 <NA>
#> [3] chr10 92828-95504 - | rtracklayer mRNA NA <NA> 872 TUBB8 <NA>
#> [4] chr10 92828-94054 - | rtracklayer exon NA <NA> <NA> <NA> <NA>
#> [5] chr10 92997-94054 - | rtracklayer CDS NA <NA> <NA> <NA> <NA>
#> genome Parent
#> <character> <CharacterList>
#> [1] hg19
#> [2] <NA> GeneID:347688
#> [3] <NA> GeneID:347688
#> [4] <NA> 872,873
#> [5] <NA> 872,873
#> -------
#> seqinfo: 298 sequences (2 circular) from hg19 genomeNote that you can indicate the genome build using the genome argument,
which is useful for ensuring that the imported data is compatible with
other genomic data you may be working with.
As an alternative, you can use the readGFF function directly, which is
more explicit about the GFF version:
readGFF(gff_file, version = "3")
#> DataFrame with 31 rows and 14 columns
#> seqid source type start end score strand phase ID Name geneName
#> <factor> <factor> <factor> <integer> <integer> <numeric> <character> <integer> <character> <character> <character>
#> 1 chr10 rtracklayer gene 92828 95504 5 - NA GeneID:347688 TUBB8 tubulin, beta 8
#> 2 chr10 rtracklayer mRNA 92828 95178 NA - NA 873 TUBB8 NA
#> 3 chr10 rtracklayer mRNA 92828 95504 NA - NA 872 TUBB8 NA
#> 4 chr10 rtracklayer exon 92828 94054 NA - NA NA NA NA
#> 5 chr10 rtracklayer CDS 92997 94054 NA - NA NA NA NA
#> ... ... ... ... ... ... ... ... ... ... ... ...
#> Alias genome Parent
#> <CharacterList> <character> <CharacterList>
#> 1 FLJ40100,TUBB8 hg19
#> 2 NA GeneID:347688
#> 3 NA GeneID:347688
#> 4 NA 872,873
#> 5 NA 872,873
#> ... ... ... ...
#> [ reached 'max' / getOption("max.print") -- omitted 5 rows ]This function returns a DataFrame object, which contains the same
information as the GRanges object but in a tabular format. Note that
one can use the makeGRangesFromDataFrame function to convert the
DataFrame returned by readGFF into a GRanges object, which is
often more convenient for downstream analysis:
readGFF(gff_file, version = "3") |>
makeGRangesFromDataFrame(
keep.extra.columns = TRUE
)
#> GRanges object with 31 ranges and 10 metadata columns:
#> seqnames ranges strand | source type score phase ID Name geneName Alias
#> <Rle> <IRanges> <Rle> | <factor> <factor> <numeric> <integer> <character> <character> <character> <CharacterList>
#> [1] chr10 92828-95504 - | rtracklayer gene 5 <NA> GeneID:347688 TUBB8 tubulin, beta 8 FLJ40100,TUBB8
#> [2] chr10 92828-95178 - | rtracklayer mRNA NA <NA> 873 TUBB8 <NA>
#> [3] chr10 92828-95504 - | rtracklayer mRNA NA <NA> 872 TUBB8 <NA>
#> [4] chr10 92828-94054 - | rtracklayer exon NA <NA> <NA> <NA> <NA>
#> [5] chr10 92997-94054 - | rtracklayer CDS NA <NA> <NA> <NA> <NA>
#> ... ... ... ... . ... ... ... ... ... ... ... ...
#> [27] chr12 89675-89827 + | rtracklayer CDS NA <NA> <NA> <NA> <NA>
#> [28] chr12 90587-90655 + | rtracklayer exon NA <NA> <NA> <NA> <NA>
#> [29] chr12 90587-90655 + | rtracklayer CDS NA <NA> <NA> <NA> <NA>
#> [30] chr12 90796-91263 + | rtracklayer exon NA <NA> <NA> <NA> <NA>
#> [31] chr12 90796-91263 * | rtracklayer CDS NA <NA> <NA> <NA> <NA>
#> genome Parent
#> <character> <CharacterList>
#> [1] hg19
#> [2] <NA> GeneID:347688
#> [3] <NA> GeneID:347688
#> [4] <NA> 872,873
#> [5] <NA> 872,873
#> ... ... ...
#> [27] <NA> 4644
#> [28] <NA> 4644
#> [29] <NA> 4644
#> [30] <NA> 4644
#> [31] <NA> 4644
#> -------
#> seqinfo: 2 sequences from an unspecified genome; no seqlengthsYou can also read GFF files from remote URLs. The import function can
handle URLs directly, allowing you to read GFF files hosted on remote
servers. On the Bioconductor Build System (BBS), we use the
BiocFileCache package to cache remote files, which avoids downloading
the file every time the code gets run.
library(BiocFileCache)
bfc <- BiocFileCache::BiocFileCache()
url <- "https://www.mirbase.org/download/hsa.gff3"
bquery <- bfcquery(bfc, url, "rname", exact = TRUE)
if (!nrow(bquery))
bfcadd(x = bfc, rname = url, rtype = "web", download = TRUE)
gff_remote <-
bfcrpath(bfc, rnames = url, exact = TRUE, download = FALSE, rtype = "web")
import(gff_remote)
#> GRanges object with 4801 ranges and 8 metadata columns:
#> seqnames ranges strand | source type score phase ID Alias
#> <Rle> <IRanges> <Rle> | <factor> <factor> <numeric> <integer> <character> <CharacterList>
#> [1] chr1 17369-17436 - | NA miRNA_primary_transcript NA <NA> MI0022705 MI0022705
#> [2] chr1 17409-17431 - | NA miRNA NA <NA> MIMAT0027618 MIMAT0027618
#> [3] chr1 17369-17391 - | NA miRNA NA <NA> MIMAT0027619 MIMAT0027619
#> [4] chr1 30366-30503 + | NA miRNA_primary_transcript NA <NA> MI0006363 MI0006363
#> [5] chr1 30438-30458 + | NA miRNA NA <NA> MIMAT0005890 MIMAT0005890
#> ... ... ... ... . ... ... ... ... ... ...
#> [4797] chrY 2609229-2609252 + | NA miRNA NA <NA> MIMAT0023714_1 MIMAT0023714
#> [4798] chrY 4606120-4606228 + | NA miRNA_primary_transcript NA <NA> MI0032313 MI0032313
#> [4799] chrY 4606140-4606158 + | NA miRNA NA <NA> MIMAT0039763 MIMAT0039763
#> [4800] chrY 13479177-13479266 + | NA miRNA_primary_transcript NA <NA> MI0039722 MI0039722
#> [4801] chrY 13479189-13479214 + | NA miRNA NA <NA> MIMAT0049014 MIMAT0049014
#> Name Derives_from
#> <character> <character>
#> [1] hsa-mir-6859-1 <NA>
#> [2] hsa-miR-6859-5p MI0022705
#> [3] hsa-miR-6859-3p MI0022705
#> [4] hsa-mir-1302-2 <NA>
#> [5] hsa-miR-1302 MI0006363
#> ... ... ...
#> [4797] hsa-miR-6089 MI0023563
#> [4798] hsa-mir-9985 <NA>
#> [4799] hsa-miR-9985 MI0032313
#> [4800] hsa-mir-12120 <NA>
#> [4801] hsa-miR-12120 MI0039722
#> -------
#> seqinfo: 24 sequences from an unspecified genome; no seqlengthsOtherwise, you can read the remote GFF file directly using the readGFF
function:
import(url, version = "3")While TxDb objects are highly efficient for querying transcript
annotations within R, it is often necessary to export this data to a
standard, portable format for use with external tools or for sharing
with collaborators. The GFF3 format is a widely accepted standard for
this purpose. Converting a TxDb or a derivative object (like a
GRangesList of exons) into a GFF3-compatible GRanges object allows
for easy export. This is particularly useful for visualizing annotations
in genome browsers like IGV or UCSC, or for input into downstream
analysis pipelines that expect GFF3 files.
library(GenomicFeatures)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
exonsBy(txdb, by = "tx") |>
asGFF()
#> GRanges object with 825453 ranges and 7 metadata columns:
#> seqnames ranges strand | type ID Name exon_id exon_name exon_rank Parent
#> <Rle> <IRanges> <Rle> | <character> <character> <character> <integer> <character> <integer> <character>
#> [1] chr1 11874-14409 + | mRNA mRNA1 1 <NA> <NA> <NA> <NA>
#> [2] chr1 11874-14409 + | mRNA mRNA2 2 <NA> <NA> <NA> <NA>
#> [3] chr1 11874-14409 + | mRNA mRNA3 3 <NA> <NA> <NA> <NA>
#> [4] chr1 69091-70008 + | mRNA mRNA4 4 <NA> <NA> <NA> <NA>
#> [5] chr1 321084-321115 + | mRNA mRNA5 5 <NA> <NA> <NA> <NA>
#> ... ... ... ... . ... ... ... ... ... ... ...
#> [825449] chrUn_gl000241 22732-22846 - | exon exon742489 <NA> 289961 <NA> 6 mRNA82957
#> [825450] chrUn_gl000241 20433-20481 - | exon exon742490 <NA> 289960 <NA> 7 mRNA82957
#> [825451] chrUn_gl000243 11501-11530 + | exon exon742491 <NA> 289967 <NA> 1 mRNA82958
#> [825452] chrUn_gl000243 13608-13637 + | exon exon742492 <NA> 289968 <NA> 1 mRNA82959
#> [825453] chrUn_gl000247 5787-5816 - | exon exon742493 <NA> 289969 <NA> 1 mRNA82960
#> -------
#> seqinfo: 93 sequences (1 circular) from hg19 genomeYou can convert a GFF object to a TxDb object using the
makeTxDbFromGFF in the
txdbmaker package.
This is useful for creating a transcript database from GFF annotations,
which can then be used for various genomic analyses.
library(txdbmaker)
txdb <- makeTxDbFromGFF(
file = gff_remote,
format = "gff3",
dataSource = "https://www.mirbase.org/download/hsa.gff3",
organism = "Homo sapiens",
taxonomyId = 9606
)
#> Import genomic features from the file as a GRanges object ... OK
#> Prepare the 'metadata' data frame ... OK
#> Make the TxDb object ...
#> Warning in .extract_transcripts_from_GRanges(tx_IDX, gr, mcols0$type, mcols0$ID, : the transcript names ("tx_name" column in the TxDb
#> object) imported from the "Name" attribute are not unique
#> Warning in .makeTxDb_normarg_chrominfo(chrominfo): genome version information is not available for this TxDb object
#> OK
genome <- grepv("genome-build-id", readLines(gff_remote)) |>
strsplit("# genome-build-id:\\s+") |>
unlist() |>
tail(1L)
genome(txdb) <- genome
txdb
#> TxDb object:
#> # Db type: TxDb
#> # Supporting package: GenomicFeatures
#> # Data source: https://www.mirbase.org/download/hsa.gff3
#> # Organism: Homo sapiens
#> # Taxonomy ID: 9606
#> # Genome: NA
#> # Nb of transcripts: 4801
#> # Db created by: txdbmaker package from Bioconductor
#> # Creation time: 2025-09-11 17:01:07 -0400 (Thu, 11 Sep 2025)
#> # txdbmaker version at creation time: 1.5.6
#> # RSQLite version at creation time: 2.4.3
#> # DBSCHEMAVERSION: 1.2sessionInfo()
#> R version 4.5.1 Patched (2025-06-14 r88325)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#>
#> Matrix products: default
#> BLAS/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 LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
#> [6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] BSgenome.Hsapiens.UCSC.hg19_1.4.3 BSgenome_1.77.1 rtracklayer_1.69.1
#> [4] txdbmaker_1.5.6 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 GenomicFeatures_1.61.6
#> [7] AnnotationDbi_1.71.1 Biobase_2.69.0 BiocStyle_2.37.1
#> [10] BiocFileCache_2.99.6 dbplyr_2.5.1 Rsamtools_2.25.3
#> [13] Biostrings_2.77.2 XVector_0.49.0 BiocIO_1.19.0
#> [16] GenomicRanges_1.61.2 Seqinfo_0.99.2 IRanges_2.43.0
#> [19] S4Vectors_0.47.0 BiocGenerics_0.55.1 generics_0.1.4
#> [22] Bioc.gff_0.99.13 colorout_1.3-2
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.1 dplyr_1.1.4 blob_1.2.4 filelock_1.0.3
#> [5] bitops_1.0-9 fastmap_1.2.0 lazyeval_0.2.2 RCurl_1.98-1.17
#> [9] GenomicAlignments_1.45.2 promises_1.3.3 rex_1.2.1 XML_3.99-0.19
#> [13] digest_0.6.37 lifecycle_1.0.4 processx_3.8.6 KEGGREST_1.49.1
#> [17] RSQLite_2.4.3 magrittr_2.0.3 compiler_4.5.1 progress_1.2.3
#> [21] rlang_1.1.6 tools_4.5.1 yaml_2.3.10 knitr_1.50
#> [25] prettyunits_1.2.0 S4Arrays_1.9.1 bit_4.6.0 curl_7.0.0
#> [29] DelayedArray_0.35.2 xml2_1.4.0 abind_1.4-8 BiocParallel_1.43.4
#> [33] rsconnect_1.5.1 websocket_1.4.4 covr_3.6.4 withr_3.0.2
#> [37] purrr_1.1.0 grid_4.5.1 biomaRt_2.65.7 SummarizedExperiment_1.39.1
#> [41] cli_3.6.5 rmarkdown_2.29 crayon_1.5.3 tinytest_1.4.1
#> [45] rstudioapi_0.17.1 httr_1.4.7 rjson_0.2.23 BiocBaseUtils_1.11.2
#> [49] DBI_1.2.3 cachem_1.1.0 chromote_0.5.1 stringr_1.5.2
#> [53] rvest_1.0.5 parallel_4.5.1 selectr_0.4-2 BiocManager_1.30.26
#> [57] restfulr_0.0.16 matrixStats_1.5.0 vctrs_0.6.5 Matrix_1.7-4
#> [61] jsonlite_2.0.0 hms_1.1.3 bit64_4.6.0-1 glue_1.8.0
#> [65] codetools_0.2-20 ps_1.9.1 stringi_1.8.7 later_1.4.4
#> [69] GenomeInfoDb_1.45.10 UCSC.utils_1.5.0 tibble_3.3.0 pillar_1.11.0
#> [73] rappdirs_0.3.3 htmltools_0.5.8.1 GenomeInfoDbData_1.2.14 R6_2.6.1
#> [77] httr2_1.2.1 lattice_0.22-7 evaluate_1.0.5 png_0.1-8
#> [81] memoise_2.0.1 Rcpp_1.1.0 SparseArray_1.9.1 xfun_0.53
#> [85] MatrixGenerics_1.21.0 pkgconfig_2.0.3