BgeeDB is a collection of functions to import data from
the Bgee database (https://bgee.org/) directly into R, and to facilitate
downstream analyses, such as gene set enrichment test based on
expression of genes in anatomical structures. Bgee provides annotated
and processed expression data and expression calls from curated
wild-type healthy samples, from human and many other animal species.
The package retrieves the annotation of RNA-seq, single cell full
length RNA-seq and Affymetrix experiments integrated into the Bgee
database, and downloads into R the quantitative data and expression
calls produced by the Bgee pipeline. The package also allows to run
GO-like enrichment analyses based on anatomical terms, where genes are
mapped to anatomical terms by expression patterns, based on the
topGO package. This is the same as the TopAnat web-service
available at (https://www.bgee.org/analysis/top-anat/), but with more
flexibility in the choice of parameters and developmental stages.
In summary, the BgeeDB package allows to: * 1. List annotation of bulk RNA-seq, single cell RNA-seq and microarray data available the Bgee database * 2. Download the processed gene expression data available in the Bgee database * 3. Download the gene expression calls and use them to perform TopAnat analyses
The pipeline used to generate Bgee expression data is documented and publicly available at (https://github.com/BgeeDB/bgee_pipeline)
If you find a bug or have any issues to use BgeeDB
please write a bug report in our own GitHub issues manager available at
(https://github.com/BgeeDB/BgeeDB_R/issues)
In R:
#if (!requireNamespace("BiocManager", quietly=TRUE))
#install.packages("BiocManager")
#BiocManager::install("BgeeDB")In case BgeeDB is run on Windows please be sure your OS has curl installed. It is installed by default on Windows 10, version 1803 or later. If Git for Windows is installed on the OS then curl is already installed. If not installed please install it before running BgeeDB in order to avoid potential timeout errors when downloading files.
The listBgeeSpecies() function allows to retrieve
available species in the Bgee database, and which data types are
available for each species.
##
## Querying Bgee to get release information...
##
## Building URL to query species in Bgee release 15_2...
##
## downloading Bgee species info... (https://www.bgee.org/ftp/bgee_v15_2/rPackageSpeciesInfo.tsv)
##
## Download of species information successful!## ID GENUS SPECIES_NAME COMMON_NAME AFFYMETRIX
## 1 6239 Caenorhabditis elegans nematode TRUE
## 2 7227 Drosophila melanogaster fruit fly TRUE
## 3 7237 Drosophila pseudoobscura FALSE
## 4 7240 Drosophila simulans FALSE
## 5 7740 Branchiostoma lanceolatum Common lancelet FALSE
## 6 7897 Latimeria chalumnae Coelacanth FALSE
## 7 7918 Lepisosteus oculatus Spotted gar FALSE
## 8 7936 Anguilla anguilla European freshwater eel FALSE
## 9 7955 Danio rerio zebrafish TRUE
## 10 7994 Astyanax mexicanus Blind cave fish FALSE
## 11 8010 Esox lucius Northern pike FALSE
## 12 8030 Salmo salar Atlantic salmon FALSE
## 13 8049 Gadus morhua Atlantic cod FALSE
## 14 8081 Poecilia reticulata Guppy FALSE
## 15 8090 Oryzias latipes Japanese rice fish FALSE
## 16 8154 Astatotilapia calliptera Eastern happy FALSE
## 17 8355 Xenopus laevis African clawed frog FALSE
## 18 8364 Xenopus tropicalis western clawed frog FALSE
## 19 9031 Gallus gallus chicken FALSE
## 20 9103 Meleagris gallopavo Wild turkey FALSE
## 21 9258 Ornithorhynchus anatinus platypus FALSE
## 22 9483 Callithrix jacchus White-tufted-ear marmoset FALSE
## 23 9531 Cercocebus atys Sooty mangabey FALSE
## 24 9541 Macaca fascicularis Crab-eating macaque FALSE
## 25 9544 Macaca mulatta macaque TRUE
## 26 9545 Macaca nemestrina Pig-tailed macaque FALSE
## 27 9555 Papio anubis Olive baboon FALSE
## 28 9593 Gorilla gorilla gorilla FALSE
## 29 9597 Pan paniscus bonobo FALSE
## 30 9598 Pan troglodytes chimpanzee FALSE
## 31 9606 Homo sapiens human TRUE
## 32 9615 Canis lupus familiaris dog FALSE
## 33 9685 Felis catus cat FALSE
## 34 9796 Equus caballus horse FALSE
## 35 9823 Sus scrofa pig FALSE
## 36 9913 Bos taurus cattle FALSE
## 37 9925 Capra hircus Goat FALSE
## 38 9940 Ovis aries Sheep FALSE
## 39 9974 Manis javanica Malayan pangolin FALSE
## 40 9986 Oryctolagus cuniculus rabbit FALSE
## 41 10090 Mus musculus mouse TRUE
## 42 10116 Rattus norvegicus rat TRUE
## 43 10141 Cavia porcellus guinea pig FALSE
## 44 10181 Heterocephalus glaber Naked mole rat FALSE
## 45 13616 Monodelphis domestica opossum FALSE
## 46 28377 Anolis carolinensis green anole FALSE
## 47 30608 Microcebus murinus Gray mouse lemur FALSE
## 48 32507 Neolamprologus brichardi Fairy cichlid FALSE
## 49 52904 Scophthalmus maximus Turbot FALSE
## 50 60711 Chlorocebus sabaeus Green monkey FALSE
## 51 69293 Gasterosteus aculeatus Three-spined stickleback FALSE
## 52 105023 Nothobranchius furzeri Turquoise killifish FALSE
## EST IN_SITU RNA_SEQ SC_FULL_LENGTH SC_DROPLET_BASED
## 1 FALSE TRUE TRUE FALSE FALSE
## 2 TRUE TRUE TRUE FALSE TRUE
## 3 FALSE FALSE TRUE FALSE FALSE
## 4 FALSE FALSE TRUE FALSE FALSE
## 5 FALSE FALSE TRUE FALSE FALSE
## 6 FALSE FALSE TRUE FALSE FALSE
## 7 FALSE FALSE TRUE FALSE FALSE
## 8 FALSE FALSE TRUE FALSE FALSE
## 9 TRUE TRUE TRUE FALSE FALSE
## 10 FALSE FALSE TRUE FALSE FALSE
## 11 FALSE FALSE TRUE FALSE FALSE
## 12 FALSE FALSE TRUE FALSE FALSE
## 13 FALSE FALSE TRUE FALSE FALSE
## 14 FALSE FALSE TRUE FALSE FALSE
## 15 FALSE FALSE TRUE FALSE FALSE
## 16 FALSE FALSE TRUE FALSE FALSE
## 17 FALSE FALSE TRUE FALSE FALSE
## 18 TRUE TRUE TRUE FALSE FALSE
## 19 FALSE FALSE TRUE FALSE TRUE
## 20 FALSE FALSE TRUE FALSE FALSE
## 21 FALSE FALSE TRUE FALSE TRUE
## 22 FALSE FALSE TRUE FALSE TRUE
## 23 FALSE FALSE TRUE FALSE FALSE
## 24 FALSE FALSE TRUE FALSE FALSE
## 25 FALSE FALSE TRUE FALSE TRUE
## 26 FALSE FALSE TRUE FALSE FALSE
## 27 FALSE FALSE TRUE FALSE FALSE
## 28 FALSE FALSE TRUE FALSE TRUE
## 29 FALSE FALSE TRUE FALSE FALSE
## 30 FALSE FALSE TRUE FALSE TRUE
## 31 TRUE FALSE TRUE TRUE TRUE
## 32 FALSE FALSE TRUE FALSE FALSE
## 33 FALSE FALSE TRUE FALSE FALSE
## 34 FALSE FALSE TRUE FALSE FALSE
## 35 FALSE FALSE TRUE FALSE TRUE
## 36 FALSE FALSE TRUE FALSE FALSE
## 37 FALSE FALSE TRUE FALSE FALSE
## 38 FALSE FALSE TRUE FALSE FALSE
## 39 FALSE FALSE TRUE FALSE FALSE
## 40 FALSE FALSE TRUE FALSE TRUE
## 41 TRUE TRUE TRUE TRUE TRUE
## 42 FALSE FALSE TRUE FALSE FALSE
## 43 FALSE FALSE TRUE FALSE FALSE
## 44 FALSE FALSE TRUE FALSE TRUE
## 45 FALSE FALSE TRUE FALSE TRUE
## 46 FALSE FALSE TRUE FALSE FALSE
## 47 FALSE FALSE TRUE FALSE FALSE
## 48 FALSE FALSE TRUE FALSE FALSE
## 49 FALSE FALSE TRUE FALSE FALSE
## 50 FALSE FALSE TRUE FALSE FALSE
## 51 FALSE FALSE TRUE FALSE FALSE
## 52 FALSE FALSE TRUE FALSE FALSEIt is possible to list all species from a specific release of Bgee
with the release argument (see
listBgeeRelease() function), and order the species
according to a specific columns with the ordering argument.
For example:
##
## Querying Bgee to get release information...
##
## Building URL to query species in Bgee release 13_2...
##
## downloading Bgee species info... (https://r.bgee.org/bgee13/?page=species&display_type=tsv&source=BgeeDB_R_package&source_version=2.33.0)
##
## Download of species information successful!## ID GENUS SPECIES_NAME COMMON_NAME AFFYMETRIX EST IN_SITU
## 17 28377 Anolis carolinensis anolis FALSE FALSE FALSE
## 13 9913 Bos taurus cow FALSE FALSE FALSE
## 1 6239 Caenorhabditis elegans c.elegans TRUE FALSE TRUE
## 3 7955 Danio rerio zebrafish TRUE TRUE TRUE
## 2 7227 Drosophila melanogaster fruitfly TRUE TRUE TRUE
## 5 9031 Gallus gallus chicken FALSE FALSE FALSE
## 8 9593 Gorilla gorilla gorilla FALSE FALSE FALSE
## 11 9606 Homo sapiens human TRUE TRUE FALSE
## 7 9544 Macaca mulatta macaque FALSE FALSE FALSE
## 16 13616 Monodelphis domestica opossum FALSE FALSE FALSE
## 14 10090 Mus musculus mouse TRUE TRUE TRUE
## 6 9258 Ornithorhynchus anatinus platypus FALSE FALSE FALSE
## 9 9597 Pan paniscus bonobo FALSE FALSE FALSE
## 10 9598 Pan troglodytes chimpanzee FALSE FALSE FALSE
## 15 10116 Rattus norvegicus rat FALSE FALSE FALSE
## 12 9823 Sus scrofa pig FALSE FALSE FALSE
## 4 8364 Xenopus tropicalis xenopus FALSE TRUE TRUE
## RNA_SEQ
## 17 TRUE
## 13 TRUE
## 1 TRUE
## 3 FALSE
## 2 FALSE
## 5 TRUE
## 8 TRUE
## 11 TRUE
## 7 TRUE
## 16 TRUE
## 14 TRUE
## 6 TRUE
## 9 TRUE
## 10 TRUE
## 15 TRUE
## 12 TRUE
## 4 TRUEIn the following example we will choose to focus on zebrafish
(“Danio_rerio”) RNA-seq. Species can be specified using their name or
their NCBI taxonomic IDs. To specify that RNA-seq data want to be
downloaded, the dataType argument is set to “rna_seq”. To
download Affymetrix microarray data, set this argument to “affymetrix”.
To download single cell full length RNA-seq data, set this argument to
“sc_full_length”. To download droplet based single cell RNA-seq data,
set this argument to “sc_droplet_based”.
##
## Querying Bgee to get release information...
##
## Building URL to query species in Bgee release 15_2...
##
## downloading Bgee species info... (https://www.bgee.org/ftp/bgee_v15_2/rPackageSpeciesInfo.tsv)
##
## Download of species information successful!
##
## API key built: 34afaa4f431d10bc6a2823b4cebf8484330a74ee1a7bbff0598d6e041904d1b867ed167c8173017091a093dd102dff89b9f79f3f964ca12a93d8c1edf40d8afdNote 1: It is possible to work with data from a specific
release of Bgee by specifying the release argument, see
listBgeeRelease() function for available releases.
Note 2: The functions of the package will store the
downloaded files in a versioned folder created by default in the working
directory. These cache files allow faster re-access to the data. The
directory where data are stored can be changed with the
pathToData argument.
The getAnnotation() function will output the list of
bulk RNA-seq experiments and libraries available in Bgee for
zebrafish.
##
## Saved annotation files in /tmp/Rtmp8oyk2b/Rbuild41f321bfe38b0/BgeeDB/vignettes/Danio_rerio_Bgee_15_2 folder.## $sample.annotation
## Experiment.ID Library.ID Anatomical.entity.ID Anatomical.entity.name
## 1 SRP055573 SRX893428 UBERON:0000014 zone of skin
## 2 SRP055573 SRX893429 UBERON:0000014 zone of skin
## 3 SRP055573 SRX893430 UBERON:0000014 zone of skin
## 4 SRP055573 SRX893431 UBERON:0000014 zone of skin
## 5 SRP055573 SRX893432 UBERON:0000014 zone of skin
## 6 SRP055573 SRX893433 UBERON:0000014 zone of skin
## Anatomical.entity.author.annotation Stage.ID Stage.name
## 1 skin UBERON:0000113 post-juvenile
## 2 skin UBERON:0000113 post-juvenile
## 3 skin UBERON:0000113 post-juvenile
## 4 skin UBERON:0000113 post-juvenile
## 5 skin UBERON:0000113 post-juvenile
## 6 skin UBERON:0000113 post-juvenile
## Stage.author.annotation Sex Strain Expression.mapped.anatomical.entity.ID
## 1 12 months male NA UBERON:0000014
## 2 12 months male NA UBERON:0000014
## 3 12 months male NA UBERON:0000014
## 4 12 months male NA UBERON:0000014
## 5 12 months male NA UBERON:0000014
## 6 12 months male NA UBERON:0000014
## Expression.mapped.anatomical.entity.name Expression.mapped.stage.ID
## 1 zone of skin UBERON:0000113
## 2 zone of skin UBERON:0000113
## 3 zone of skin UBERON:0000113
## 4 zone of skin UBERON:0000113
## 5 zone of skin UBERON:0000113
## 6 zone of skin UBERON:0000113
## Expression.mapped.stage.name Expression.mapped.sex Expression.mapped.strain
## 1 post-juvenile male wild-type
## 2 post-juvenile male wild-type
## 3 post-juvenile male wild-type
## 4 post-juvenile male wild-type
## 5 post-juvenile male wild-type
## 6 post-juvenile male wild-type
## Platform.ID Protocol Library.type Library.orientation
## 1 Illumina HiSeq 2000 polyA single NA
## 2 Illumina HiSeq 2000 polyA single NA
## 3 Illumina HiSeq 2000 polyA single NA
## 4 Illumina HiSeq 2000 polyA single NA
## 5 Illumina HiSeq 2000 polyA single NA
## 6 Illumina HiSeq 2000 polyA single NA
## TMM.normalization.factor Expression.threshold Expression.unit
## 1 1.151250 0.851205 tpm
## 2 1.476505 0.975114 tpm
## 3 1.209218 0.898562 tpm
## 4 1.137837 0.912914 tpm
## 5 1.205815 0.947171 tpm
## 6 1.268032 0.897872 tpm
## Cell.compartment Sequenced.transcript.part Genotype Read.count
## 1 cell full length NA 37106422
## 2 cell full length NA 36371186
## 3 cell full length NA 37757076
## 4 cell full length NA 39481452
## 5 cell full length NA 40239380
## 6 cell full length NA 37777312
## Mapped.read.count Min.read.length Max.read.length All.genes.percent.present
## 1 29074870 50 50 57.20
## 2 28397674 50 50 62.95
## 3 28889562 50 50 54.99
## 4 30247166 50 50 55.31
## 5 30876407 50 50 55.22
## 6 29147850 50 50 56.64
## Protein.coding.genes.percent.present Intergenic.regions.percent.present
## 1 68.74 4.36
## 2 74.69 4.46
## 3 66.02 4.40
## 4 66.46 4.39
## 5 66.37 4.53
## 6 67.94 4.26
## Distinct.rank.count Max.rank.in.the.expression.mapped.condition Run.IDs
## 1 24170 NA SRR1821827
## 2 25495 NA SRR1821828
## 3 23830 NA SRR1821829
## 4 23987 NA SRR1821830
## 5 23943 NA SRR1821831
## 6 24320 NA SRR1821832
## Data.source
## 1 SRA
## 2 SRA
## 3 SRA
## 4 SRA
## 5 SRA
## 6 SRA
## Data.source.URL
## 1 https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=SRX893428
## 2 https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=SRX893429
## 3 https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=SRX893430
## 4 https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=SRX893431
## 5 https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=SRX893432
## 6 https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=SRX893433
## Bgee.normalized.data.URL
## 1 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_SRP055573.tsv.gz
## 2 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_SRP055573.tsv.gz
## 3 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_SRP055573.tsv.gz
## 4 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_SRP055573.tsv.gz
## 5 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_SRP055573.tsv.gz
## 6 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_SRP055573.tsv.gz
## Raw.file.URL
## 1 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRX893428
## 2 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRX893429
## 3 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRX893430
## 4 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRX893431
## 5 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRX893432
## 6 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRX893433
##
## $experiment.annotation
## Experiment.ID
## 1 SRP055573
## 2 DRP003810
## 3 SRP022612
## 4 SRP470937
## 5 SRP009426
## 6 GSE39703
## Experiment.name
## 1 RNA-seq of zebrafish brain, liver and skin during perturbation with rotenone at young and old age
## 2 EXPANDE project
## 3 RNA-seq of Danio rerio and Mus musculus skin for three different age groups
## 4 Danio rerio Raw sequence reads
## 5 Comprehensive identification of long non-coding RNAs expressed during zebrafish embryogenesis [RNA_seq]
## 6 Transcriptomic analysis of zebrafish during development and homeostasis
## Library.count Condition.count Organ.stage.count Organ.count Celltype.Count
## 1 36 3 3 3 0
## 2 35 16 16 5 0
## 3 20 1 1 1 0
## 4 18 2 2 2 0
## 5 17 8 8 5 0
## 6 16 5 5 4 0
## Stage.count Sex.count Strain.count Data.source
## 1 1 1 1 SRA
## 2 16 1 1 SRA
## 3 1 1 1 GEO
## 4 1 2 1 SRA
## 5 8 1 1 SRA
## 6 4 1 1 GEO
## Data.source.URL
## 1 https://www.ncbi.nlm.nih.gov/sra/SRP055573
## 2 https://www.ncbi.nlm.nih.gov/sra/DRP003810
## 3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=SRP022612
## 4 https://www.ncbi.nlm.nih.gov/sra/SRP470937
## 5 https://www.ncbi.nlm.nih.gov/sra/SRP009426
## 6 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39703
## Bgee.normalized.data.URL
## 1 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_SRP055573.tsv.gz
## 2 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_DRP003810.tsv.gz
## 3 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_SRP022612.tsv.gz
## 4 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_SRP470937.tsv.gz
## 5 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_SRP009426.tsv.gz
## 6 https://www.bgee.org/ftp/bgee_v15_2/download/processed_expr_values/rna_seq/Danio_rerio/Danio_rerio_RNA-Seq_read_counts_TPM_GSE39703.tsv.gz
## Experiment.description
## 1 Zebrafish of two different age groups (12 and 36 months) were treated with low amounts of rotenone (mild stress) and compared to untreated zebrafish. Two different durations were used (3 and 8 weeks). Illumina sequencing (HiSeq2000) was applied to generate 50bp single-end reads. Jena Centre for Systems Biology of Ageing - JenAge (www.jenage.de) Overall design: 68 sample: 3 tissues (brain, liver, skin); 2 age groups (12 and 36 months); controls and rotenone treated samples; 2-6 biological replicates for each group
## 2 EXPression AloNg Development and Evolution (EXPANDE) project aims to identify gene expression profiles expanded during embryogenesis and evolution. In brief, taking advantages of Illumina sequencing, RNAseq profiles of early to late embryos of 8 chordate species were identified with biological replicates (two or more biological replicates).
## 3 Comparison of temporal gene expression profiles (www.jenage.de) The RNA-seq data comprises 3 age groups: 2, 15 and 30 months for mouse skin; 5, 24 and 42 months for zebrafish skin. Illumina 50bp single-stranded single-read RNA sequencing Overall design: 15 samples for mouse: 5 biological replicates for 2 months, 6 biological replicates for 15 months and 4 biological replicates for 30 months; 20 samples for zebrafish: 9 biological replicates for 5 months, 6 biological replicates for 24 months and 5 biological replicates for 42 months
## 4 normal RNAseq of zebrafish
## 5 Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins. Recent genome-wide studies in human and mouse have annotated lncRNAs expressed in cell lines and adult tissues, but a systematic analysis of lncRNAs expressed during vertebrate embryogenesis has been elusive. To identify lncRNAs with potential functions in vertebrate embryogenesis, we performed a time series of RNA-Seq experiments at eight stages during early zebrafish development. We reconstructed 56,535 high-confidence transcripts in 28,912 loci, recovering the vast majority of expressed RefSeq transcripts, while identifying thousands of novel isoforms and expressed loci. We defined a stringent set of 1,133 non-coding multi-exonic transcripts expressed during embryogenesis. These include long intergenic ncRNAs (lincRNAs), intronic overlapping lncRNAs, exonic antisense overlapping lncRNAs, and precursors for small RNAs (sRNAs). Zebrafish lncRNAs share many of the characteristics of their mammalian counterparts: relatively short length, low exon number, low expression, and conservation levels comparable to introns. Subsets of lncRNAs carry chromatin signatures characteristic of genes with developmental functions. The temporal expression profile of lncRNAs revealed two novel properties: lncRNAs are expressed in narrower time windows than protein-coding genes and are specifically enriched in early-stage embryos. In addition, several lncRNAs show tissue-specific expression and distinct subcellular localization patterns. Integrative computational analyses associated individual lncRNAs with specific pathways and functions, ranging from cell cycle regulation to morphogenesis. Our study provides the first comprehensive identification of lncRNAs in a vertebrate embryo and forms the foundation for future genetic, genomic and evolutionary studies. Overall design: RNA-Seq for 8 zebrafish developmental stages, 2 lanes for each stage (3 for shield).
## 6 Sequencing libraries were generated from total RNA samples following the mRNAseq protocol for the generation of single end (16-36 hpf, 5 day larvae, adult head and adult tail) or paired end (24 hpf) libraries (Illumina). Single end reads of 36 nucleotides and paired end reads (2 x 76 nucleotides) were obtained with a GAIIx (Illumina). Gene expression at the different stages/tissu was assessed by cufflinks and HTseq.The getSampleProcessedData() function will download
processed bulk RNA-seq data from all zebrafish experiments in Bgee as a
data frame.
# download all RNA-seq experiments from zebrafish
data_bgee_zebrafish <- getSampleProcessedData(bgee)## downloading data from Bgee FTP...## You tried to download more than 15 experiments, because of that all the Bgee data for this species will be downloaded.## Downloading all expression data for species Danio_rerio## Saved expression data file in /tmp/Rtmp8oyk2b/Rbuild41f321bfe38b0/BgeeDB/vignettes/Danio_rerio_Bgee_15_2 folder. Now untar file...## Finished uncompress tar files## Save data in local sqlite database## Load queried data. The query is : SELECT * from rna_seq## [1] 16## $Experiment.ID
## [1] "ERP000447" "ERP000447" "ERP000447" "ERP000447" "ERP000447" "ERP000447"
##
## $Library.ID
## [1] "ERX009445" "ERX009445" "ERX009445" "ERX009445" "ERX009445" "ERX009445"
##
## $Library.type
## [1] "paired" "paired" "paired" "paired" "paired" "paired"
##
## $Gene.ID
## [1] "ENSDARG00000000001" "ENSDARG00000000002" "ENSDARG00000000018"
## [4] "ENSDARG00000000019" "ENSDARG00000000068" "ENSDARG00000000069"
##
## $Anatomical.entity.ID
## [1] "UBERON:0000948" "UBERON:0000948" "UBERON:0000948" "UBERON:0000948"
## [5] "UBERON:0000948" "UBERON:0000948"
##
## $Anatomical.entity.name
## [1] "\"heart\"" "\"heart\"" "\"heart\"" "\"heart\"" "\"heart\"" "\"heart\""
##
## $Stage.ID
## [1] "UBERON:0000113" "UBERON:0000113" "UBERON:0000113" "UBERON:0000113"
## [5] "UBERON:0000113" "UBERON:0000113"
##
## $Stage.name
## [1] "\"post-juvenile\"" "\"post-juvenile\"" "\"post-juvenile\""
## [4] "\"post-juvenile\"" "\"post-juvenile\"" "\"post-juvenile\""
##
## $Sex
## [1] "mixed" "mixed" "mixed" "mixed" "mixed" "mixed"
##
## $Strain
## [1] "\"Singapore\"" "\"Singapore\"" "\"Singapore\"" "\"Singapore\""
## [5] "\"Singapore\"" "\"Singapore\""
##
## $Read.count
## [1] 14.0000 15.0000 104.0000 101.0000 12.0000 98.9097
##
## $TPM
## [1] 3.205255 2.980668 19.807954 19.893809 3.304039 30.809485
##
## $Rank
## [1] 7324 7536 2599 2594 7225 1835
##
## $Detection.flag
## [1] "present" "absent" "present" "present" "present" "present"
##
## $pValue
## [1] 0.0471476817 0.0538957373 0.0004800936 0.0004735145 0.0445365056
## [6] 0.0001090867
##
## $State.in.Bgee
## [1] "Part of a call" "Part of a call" "Part of a call" "Part of a call"
## [5] "Part of a call" "Part of a call"The result of the getSampleProcessedData() function is a
data frame. Each row is a gene and the expression levels are displayed
as raw read counts, RPKMs (up to Bgee 13.2), TPMs (from Bgee 14.0), or
FPKMs (from Bgee 14.0 to Bgee 14.2). A detection flag indicates if the
gene is significantly expressed above background level of expression.
From Bgee 15.0 a pValue allows to have a precise metric indicating how
much the gene is significantly expressed above background level of
expression (the detection flag is still available and a gene is
considered as present if pValue < 0.05).
Note: If microarray data are downloaded, rows corresponding to probesets and log2 of expression intensities are available instead of read counts/RPKMs/TPMs/FPKMs.
Alternatively, you can choose to download data from one RNA-seq
experiment from Bgee with the experimentId parameter:
# download data for GSE68666
data_bgee_zebrafish_gse68666 <- getSampleProcessedData(bgee, experimentId = "GSE68666")## Load queried data. The query is : SELECT * from rna_seq WHERE [Experiment.ID] = "GSE68666"It is possible to download data by combining filters : * experimentId : one or more experimentId, * sampleId : one or more sampleId (i.e libraryId for RNA-Seq and ChipId for Affymetrix), * anatEntityId : one or more anatomical entity ID from the UBERON ontology (https://uberon.github.io/), * withDescendantAnatEntities : filter on the selected anatEntityId and all its descendants (from Bgee 15.0), * stageId : one or more developmental stage ID from the developmental stage ontologies (https://github.com/obophenotype/developmental-stage-ontologies), * withDescendantStages : filter on the selected stageId and all its descendants (from Bgee 15.0) * cellTypeId : one or more cell type, only for single cell datatype (from Bgee 15.0), * withDescendantCellTypes : filter on the selected cellTypeId and all its descendants (from Bgee 15.0) * sex : one or more sex (from Bgee 15.0), * strain : one or more strain (from Bgee 15.0),
# Examples of data downloading using different filtering combination
# retrieve zebrafish RNA-Seq data for heart (UBERON:0000955) or brain (UBERON:0000948)
data_bgee_zebrafish_filters <- getSampleProcessedData(bgee, anatEntityId = c("UBERON:0000955","UBERON:0000948"))
# retrieve zebrafish RNA-Seq data for embryo (UBERON:0000922) part of the experiment GSE68666
data_bgee_zebrafish_filters <- getSampleProcessedData(bgee, experimentId = "GSE68666", anatEntityId = "UBERON:0000922")
# retrieve zebrafish RNA-Seq data for head kidney (UBERON:0007132) or swim bladder (UBERON:0006860) from post-juvenile adult stage (UBERON:0000113)
data_bgee_zebrafish_filters <- getSampleProcessedData(bgee, stageId = "UBERON:0000113", anatEntityId = c("UBERON:0007132","UBERON:0006860"))
# retrieve zebrafish RNA-Seq data for brain (UBERON:0000948) and all substructures of brain from post-juvenile adult stage (UBERON:0000113)
data_bgee_zebrafish_filters <- getSampleProcessedData(bgee, stageId = "UBERON:0000113", anatEntityId = "UBERON:0000948", withDescendantAnatEntities = TRUE)It is sometimes easier to work with data organized as a matrix, where
rows represent genes or probesets and columns represent different
samples. The formatData() function reformats the data into
an ExpressionSet object including: * An expression data matrix, with
genes or probesets as rows, and samples as columns
(assayData slot). The stats argument allows to
choose if the matrix should be filled with read counts, RPKMs (up to
Bgee 13.2), FPKMs (from Bgee 14.0 to Bgee 14.2), or TPMs (from Bgee
14.0) for RNA-seq data. For microarray data the matrix is filled with
log2 expression intensities. * A data frame listing the samples and
their anatomical structure and developmental stage annotation
(phenoData slot) * For microarray data, the mapping from
probesets to Ensembl genes (featureData slot)
The callType argument allows to retain only actively
expressed genes or probesets, if set to “present” or “present high
quality”. Genes or probesets that are absent in a given sample are given
NA values.
# use only present calls and fill expression matrix with TPM values
gene.expression.zebrafish.tpm <- formatData(bgee, data_bgee_zebrafish_gse68666, callType = "present", stats = "tpm")##
## Extracting expression data matrix...
## Keeping only present genes.## Warning: `spread_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `spread()` instead.
## ℹ The deprecated feature was likely used in the BgeeDB package.
## Please report the issue at <https://github.com/BgeeDB/BgeeDB_R/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.##
## Extracting features information...
##
## Extracting samples information...## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 27806 features, 3 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: SRX1021683 SRX1021684 SRX1021685
## varLabels: Library.ID Anatomical.entity.ID ... Stage.name (5 total)
## varMetadata: labelDescription
## featureData
## featureNames: ENSDARG00000000001 ENSDARG00000000002 ...
## ENSDARG00000117824 (27806 total)
## fvarLabels: Gene.ID
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:The getCellProcessedData() function will download
processed single-cell RNA-seq data at the cell level. In the following
example we will focus on downloading UMI counts at cell level for the
Gallus gallus experiment ERP132576. The data are stored as h5ad files
and can be opened using either the package "zellkonverter" (default) or
the package "anndata" with the attribute
package = "anndata. Both "zellkonverter" and "anndata"
packages require a configuration step when used the first time.
#create a bgee object to download droplet based data from Gallus gallus
bgee <- Bgee$new(species = "Gallus_gallus", dataType = "sc_droplet_based")
# download cell data for one RNA-seq experiment
cell_data_bgee_gallus_gallus <- getCellProcessedData(bgee, experimentId = "ERP132576")The result of the getCellProcessedData() depends on the
package used to load the data. With "zellkonverter" it is an object of
the class SingleCellExperiment. With "anndata" it is an object of the
class AnnData. The expression levels are displayed as raw UMI
counts.
For some documentation on the TopAnat analysis, please refer to our publications, or to the web-tool page (https://www.bgee.org/analysis/top-anat/).
Similarly to the quantitative data download example above, the first step of a topAnat analysis is to built an object from the Bgee class. For this example, we will focus on zebrafish:
##
## NOTE: You did not specify any data type. The argument dataType will be set to c("rna_seq","affymetrix","est","in_situ","sc_full_length", "sc_droplet_based") for the next steps.
##
## Querying Bgee to get release information...
##
## NOTE: the file describing Bgee species information for release 15_2 was found in the download directory /tmp/Rtmp8oyk2b/Rbuild41f321bfe38b0/BgeeDB/vignettes. Data will not be redownloaded.
##
## API key built: 34afaa4f431d10bc6a2823b4cebf8484330a74ee1a7bbff0598d6e041904d1b867ed167c8173017091a093dd102dff89b9f79f3f964ca12a93d8c1edf40d8afdNote : We are free to specify any data type of interest
using the dataType argument among rna_seq,
sc_full_length, sc_droplet_based,
affymetrix, est or in_situ, or
even a combination of data types. If nothing is specified, as in the
above example, all data types available for the targeted species are
used. This equivalent to specifying
dataType=c("rna_seq","sc_full_length","sc_droplet_based","affymetrix","est","in_situ").
The loadTopAnatData() function loads a mapping from
genes to anatomical structures based on calls of expression in
anatomical structures. It also loads the structure of the anatomical
ontology and the names of anatomical structures.
##
## Building URLs to retrieve organ relationships from Bgee.........
## URL successfully built (https://www.bgee.org/bgee15_2/api/?page=r_package&action=get_anat_entity_relations&display_type=tsv&species_list=7955&attr_list=SOURCE_ID&attr_list=TARGET_ID&api_key=34afaa4f431d10bc6a2823b4cebf8484330a74ee1a7bbff0598d6e041904d1b867ed167c8173017091a093dd102dff89b9f79f3f964ca12a93d8c1edf40d8afd&source=BgeeDB_R_package&source_version=2.33.0)
## Submitting URL to Bgee webservice (can be long)
## Got results from Bgee webservice. Files are written in "/tmp/Rtmp8oyk2b/Rbuild41f321bfe38b0/BgeeDB/vignettes/Danio_rerio_Bgee_15_2"
##
## Building URLs to retrieve organ names from Bgee.................
## URL successfully built (https://www.bgee.org/bgee15_2/api/?page=r_package&action=get_anat_entities&display_type=tsv&species_list=7955&attr_list=ID&attr_list=NAME&api_key=34afaa4f431d10bc6a2823b4cebf8484330a74ee1a7bbff0598d6e041904d1b867ed167c8173017091a093dd102dff89b9f79f3f964ca12a93d8c1edf40d8afd&source=BgeeDB_R_package&source_version=2.33.0)
## Submitting URL to Bgee webservice (can be long)
## Got results from Bgee webservice. Files are written in "/tmp/Rtmp8oyk2b/Rbuild41f321bfe38b0/BgeeDB/vignettes/Danio_rerio_Bgee_15_2"
##
## Building URLs to retrieve mapping of gene to organs from Bgee...
## URL successfully built (https://www.bgee.org/bgee15_2/api/?page=r_package&action=get_expression_calls&display_type=tsv&species_list=7955&attr_list=GENE_ID&attr_list=ANAT_ENTITY_ID&api_key=34afaa4f431d10bc6a2823b4cebf8484330a74ee1a7bbff0598d6e041904d1b867ed167c8173017091a093dd102dff89b9f79f3f964ca12a93d8c1edf40d8afd&source=BgeeDB_R_package&source_version=2.33.0&data_qual=SILVER)
## Submitting URL to Bgee webservice (can be long)
## Got results from Bgee webservice. Files are written in "/tmp/Rtmp8oyk2b/Rbuild41f321bfe38b0/BgeeDB/vignettes/Danio_rerio_Bgee_15_2"
##
## Parsing the results.............................................
##
## Adding BGEE:0 as unique root of all terms of the ontology.......
##
## Done.The stringency on the quality of expression calls can be changed with
the confidence argument. Finally, if you are interested in
expression data coming from a particular developmental stage or a group
of stages, please specify the a Uberon stage Id in the
stage argument.
## Loading silver and gold expression calls from affymetrix data made on embryonic samples only
## This is just given as an example, but is not run in this vignette because only few data are returned
bgee <- Bgee$new(species = "Danio_rerio", dataType="affymetrix", release = "15.2")
myTopAnatData <- loadTopAnatData(bgee, stage="UBERON:0000068", confidence="silver")Note 1: As mentioned above, the downloaded data files are
stored in a versioned folder that can be set with the
pathToData argument when creating the Bgee class object
(default is the working directory). If you query again Bgee with the
exact same parameters, these cached files will be read instead of
querying the web-service again. It is thus important, if you
plan to reuse the same data for multiple parallel topAnat analyses, to
plan to make use of these cached files instead of redownloading them for
each analysis. The cached files also give the possibility to
repeat analyses offline.
Note 2: In releases up to Bgee 13.2 allowed
confidence`` values were `low_quality` or or `high_quality`. Starting from Bgee 14.0confidence``values aregoldorsilver`.
First we need to prepare a list of genes in the foreground and in the
background. The input format is the same as the gene list required to
build a topGOdata object in the topGO package:
a vector with background genes as names, and 0 or 1 values depending if
a gene is in the foreground or not. In this example we will look at
genes with an annotated phenotype related to “pectoral fin” . We use the
biomaRt package to retrieve this list of genes. We expect
them to be enriched for expression in male tissues, notably the testes.
The background list of genes is set to all genes annotated to at least
one Gene Ontology term, allowing to account for biases in which types of
genes are more likely to receive Gene Ontology annotation.
# if (!requireNamespace("BiocManager", quietly=TRUE))
# install.packages("BiocManager")
# BiocManager::install("biomaRt")
library(biomaRt)
ensembl <- useMart("ENSEMBL_MART_ENSEMBL", dataset="drerio_gene_ensembl", host="mar2016.archive.ensembl.org")
# get the mapping of Ensembl genes to phenotypes. It will corresponds to the background genes
universe <- getBM(filters=c("phenotype_source"), value=c("ZFIN"), attributes=c("ensembl_gene_id","phenotype_description"), mart=ensembl)
# select phenotypes related to pectoral fin
phenotypes <- grep("pectoral fin", unique(universe$phenotype_description), value=T)
# Foreground genes are those with an annotated phenotype related to "pectoral fin"
myGenes <- unique(universe$ensembl_gene_id[universe$phenotype_description %in% phenotypes])
# Prepare the gene list vector
geneList <- factor(as.integer(unique(universe$ensembl_gene_id) %in% myGenes))
names(geneList) <- unique(universe$ensembl_gene_id)
summary(geneList)
# Prepare the topGO object
myTopAnatObject <- topAnat(myTopAnatData, geneList)The above code using the biomaRt package is not executed
in this vignette to prevent building issues of our package in case of
biomaRt downtime. Instead we use a geneList object saved in
the data/ folder that we built using pre-downloaded
data.
##
## Checking topAnatData object.............
##
## Checking gene list......................
##
## WARNING: Some genes in your gene list have no expression data in Bgee, and will not be included in the analysis. 2959 genes in background will be kept.
##
## Building most specific Ontology terms... ( 1316 Ontology terms found. )
##
## Building DAG topology................... ( 2262 Ontology terms and 4630 relations. )
##
## Annotating nodes (Can be long).......... ( 2959 genes annotated to the Ontology terms. )Warning: This can be long, especially if the gene list is large, since the Uberon anatomical ontology is large and expression calls will be propagated through the whole ontology (e.g., expression in the forebrain will also be counted as expression in parent structures such as the brain, nervous system, etc). Consider running a script in batch mode if you have multiple analyses to do.
For this step, see the vignette of the topGO package for
more details, as you have to directly use the tests implemented in the
topGO package, as shown in this example:
##
## -- Weight Algorithm --
##
## The algorithm is scoring 1019 nontrivial nodes
## parameters:
## test statistic: fisher : ratio##
## Level 29: 1 nodes to be scored.##
## Level 28: 1 nodes to be scored.##
## Level 27: 1 nodes to be scored.##
## Level 26: 3 nodes to be scored.##
## Level 25: 5 nodes to be scored.##
## Level 24: 6 nodes to be scored.##
## Level 23: 8 nodes to be scored.##
## Level 22: 22 nodes to be scored.##
## Level 21: 22 nodes to be scored.##
## Level 20: 29 nodes to be scored.##
## Level 19: 40 nodes to be scored.##
## Level 18: 71 nodes to be scored.##
## Level 17: 69 nodes to be scored.##
## Level 16: 88 nodes to be scored.##
## Level 15: 103 nodes to be scored.##
## Level 14: 115 nodes to be scored.##
## Level 13: 106 nodes to be scored.##
## Level 12: 91 nodes to be scored.##
## Level 11: 76 nodes to be scored.##
## Level 10: 55 nodes to be scored.##
## Level 9: 31 nodes to be scored.##
## Level 8: 26 nodes to be scored.##
## Level 7: 20 nodes to be scored.##
## Level 6: 17 nodes to be scored.##
## Level 5: 6 nodes to be scored.##
## Level 4: 3 nodes to be scored.##
## Level 3: 2 nodes to be scored.##
## Level 2: 1 nodes to be scored.##
## Level 1: 1 nodes to be scored.Warning: This can be long because of the size of the ontology. Consider running scripts in batch mode if you have multiple analyses to do.
The makeTable function allows to filter and format the
test results, and calculate FDR values.
# Display results sigificant at a 1% FDR threshold
tableOver <- makeTable(myTopAnatData, myTopAnatObject, results, cutoff = 0.01)##
## Building the results table for the 9 significant terms at FDR threshold of 0.01...
## Ordering results by pValue column in increasing order...
## Done## organId organName annotated significant
## UBERON:0000151 UBERON:0000151 pectoral fin 410 60
## UBERON:0005419 UBERON:0005419 pectoral appendage bud 133 31
## UBERON:2000040 UBERON:2000040 median fin fold 53 15
## UBERON:0005729 UBERON:0005729 pectoral appendage field 14 8
## UBERON:0006598 UBERON:0006598 presumptive structure 1803 122
## UBERON:0004376 UBERON:0004376 fin bone 27 9
## expected foldEnrichment pValue FDR
## UBERON:0000151 20.23 2.965892 3.105066e-15 3.533565e-12
## UBERON:0005419 6.56 4.725610 3.953367e-14 2.249466e-11
## UBERON:2000040 2.62 5.725191 1.517744e-08 5.757308e-06
## UBERON:0005729 0.69 11.594203 6.792283e-08 1.932405e-05
## UBERON:0006598 88.96 1.371403 1.928360e-06 4.388947e-04
## UBERON:0004376 1.33 6.766917 2.968197e-06 5.629680e-04At the time of building this vignette (Sept. 2024), there was 9 significant anatomical structures. The first term is “pectoral fin”, and the second “pectoral appendage bud”. Other terms in the list, especially those with high enrichment folds, are clearly related to pectoral fins or substructures of fins. This analysis shows that genes with phenotypic effects on pectoral fins are specifically expressed in or next to these structures
By default results are sorted by p-value, but this can be changed
with the ordering parameter by specifying which column
should be used to order the results (preceded by a “-” sign to indicate
that ordering should be made in decreasing order). For example, it is
often convenient to sort significant structures by decreasing enrichment
fold, using ordering = -6. The full table of results can be
obtained using cutoff = 1.
Of note, it is possible to retrieve for a particular tissue the significant genes that were mapped to it.
# In order to retrieve significant genes mapped to the term " paired limb/fin bud"
term <- "UBERON:0004357"
termStat(myTopAnatObject, term) ## Annotated Significant Expected
## UBERON:0004357 172 37 8.49## $`UBERON:0004357`
## [1] "ENSDARG00000001057" "ENSDARG00000001785" "ENSDARG00000002445"
## [4] "ENSDARG00000002487" "ENSDARG00000002795" "ENSDARG00000002952"
## [7] "ENSDARG00000003293" "ENSDARG00000003399" "ENSDARG00000004954"
## [10] "ENSDARG00000005479" "ENSDARG00000005645" "ENSDARG00000005762"
## [13] "ENSDARG00000006921" "ENSDARG00000007407" "ENSDARG00000007438"
## [16] "ENSDARG00000007918" "ENSDARG00000008305" "ENSDARG00000008886"
## [19] "ENSDARG00000009438" "ENSDARG00000009534" "ENSDARG00000010192"
## [22] "ENSDARG00000011027" "ENSDARG00000011247" "ENSDARG00000011407"
## [25] "ENSDARG00000011618" "ENSDARG00000012078" "ENSDARG00000012422"
## [28] "ENSDARG00000012824" "ENSDARG00000013144" "ENSDARG00000013409"
## [31] "ENSDARG00000013881" "ENSDARG00000014091" "ENSDARG00000014626"
## [34] "ENSDARG00000014796" "ENSDARG00000015554" "ENSDARG00000015674"
## [37] "ENSDARG00000016022" "ENSDARG00000016454" "ENSDARG00000016858"
## [40] "ENSDARG00000017219" "ENSDARG00000017369" "ENSDARG00000018426"
## [43] "ENSDARG00000018460" "ENSDARG00000018492" "ENSDARG00000018902"
## [46] "ENSDARG00000019260" "ENSDARG00000019353" "ENSDARG00000019579"
## [49] "ENSDARG00000019995" "ENSDARG00000020143" "ENSDARG00000021442"
## [52] "ENSDARG00000021938" "ENSDARG00000022280" "ENSDARG00000024561"
## [55] "ENSDARG00000024894" "ENSDARG00000025081" "ENSDARG00000025641"
## [58] "ENSDARG00000025891" "ENSDARG00000028071" "ENSDARG00000029764"
## [61] "ENSDARG00000030110" "ENSDARG00000030756" "ENSDARG00000031222"
## [64] "ENSDARG00000031894" "ENSDARG00000031952" "ENSDARG00000033327"
## [67] "ENSDARG00000034375" "ENSDARG00000035648" "ENSDARG00000036254"
## [70] "ENSDARG00000036558" "ENSDARG00000037109" "ENSDARG00000037675"
## [73] "ENSDARG00000037677" "ENSDARG00000038006" "ENSDARG00000038428"
## [76] "ENSDARG00000038672" "ENSDARG00000038879" "ENSDARG00000040764"
## [79] "ENSDARG00000041609" "ENSDARG00000041706" "ENSDARG00000041799"
## [82] "ENSDARG00000042296" "ENSDARG00000043130" "ENSDARG00000043559"
## [85] "ENSDARG00000043923" "ENSDARG00000044574" "ENSDARG00000052131"
## [88] "ENSDARG00000052139" "ENSDARG00000052344" "ENSDARG00000052652"
## [91] "ENSDARG00000053479" "ENSDARG00000054026" "ENSDARG00000055026"
## [94] "ENSDARG00000055027" "ENSDARG00000055381" "ENSDARG00000055398"
## [97] "ENSDARG00000056427" "ENSDARG00000056995" "ENSDARG00000058115"
## [100] "ENSDARG00000058543" "ENSDARG00000058822" "ENSDARG00000058996"
## [103] "ENSDARG00000059233" "ENSDARG00000059276" "ENSDARG00000059279"
## [106] "ENSDARG00000060808" "ENSDARG00000061328" "ENSDARG00000061345"
## [109] "ENSDARG00000061600" "ENSDARG00000068365" "ENSDARG00000068732"
## [112] "ENSDARG00000069105" "ENSDARG00000069473" "ENSDARG00000070069"
## [115] "ENSDARG00000070670" "ENSDARG00000070903" "ENSDARG00000071336"
## [118] "ENSDARG00000071560" "ENSDARG00000071699" "ENSDARG00000073814"
## [121] "ENSDARG00000074378" "ENSDARG00000075559" "ENSDARG00000075713"
## [124] "ENSDARG00000076010" "ENSDARG00000076554" "ENSDARG00000076566"
## [127] "ENSDARG00000076856" "ENSDARG00000077121" "ENSDARG00000077353"
## [130] "ENSDARG00000077473" "ENSDARG00000078696" "ENSDARG00000078784"
## [133] "ENSDARG00000079027" "ENSDARG00000079201" "ENSDARG00000079922"
## [136] "ENSDARG00000079964" "ENSDARG00000089805" "ENSDARG00000090820"
## [139] "ENSDARG00000091161" "ENSDARG00000092136" "ENSDARG00000092809"
## [142] "ENSDARG00000095743" "ENSDARG00000096546" "ENSDARG00000098359"
## [145] "ENSDARG00000099088" "ENSDARG00000099458" "ENSDARG00000099996"
## [148] "ENSDARG00000100236" "ENSDARG00000100252" "ENSDARG00000100312"
## [151] "ENSDARG00000100558" "ENSDARG00000100725" "ENSDARG00000101076"
## [154] "ENSDARG00000101199" "ENSDARG00000101209" "ENSDARG00000101244"
## [157] "ENSDARG00000101701" "ENSDARG00000101766" "ENSDARG00000101831"
## [160] "ENSDARG00000102153" "ENSDARG00000102470" "ENSDARG00000102750"
## [163] "ENSDARG00000102824" "ENSDARG00000102995" "ENSDARG00000103432"
## [166] "ENSDARG00000103515" "ENSDARG00000103754" "ENSDARG00000103799"
## [169] "ENSDARG00000104353" "ENSDARG00000104808" "ENSDARG00000104815"
## [172] "ENSDARG00000105230"# 37 significant genes mapped to this term for Bgee 15.2
annotated <- genesInTerm(myTopAnatObject, term)[["UBERON:0004357"]]
annotated[annotated %in% sigGenes(myTopAnatObject)]## [1] "ENSDARG00000002445" "ENSDARG00000002952" "ENSDARG00000003293"
## [4] "ENSDARG00000008305" "ENSDARG00000011407" "ENSDARG00000012824"
## [7] "ENSDARG00000013881" "ENSDARG00000014091" "ENSDARG00000018426"
## [10] "ENSDARG00000018902" "ENSDARG00000019260" "ENSDARG00000019353"
## [13] "ENSDARG00000024894" "ENSDARG00000028071" "ENSDARG00000031894"
## [16] "ENSDARG00000036254" "ENSDARG00000037677" "ENSDARG00000038006"
## [19] "ENSDARG00000038672" "ENSDARG00000041799" "ENSDARG00000042296"
## [22] "ENSDARG00000043559" "ENSDARG00000043923" "ENSDARG00000056427"
## [25] "ENSDARG00000058543" "ENSDARG00000069473" "ENSDARG00000070903"
## [28] "ENSDARG00000071336" "ENSDARG00000073814" "ENSDARG00000076856"
## [31] "ENSDARG00000077121" "ENSDARG00000077353" "ENSDARG00000079027"
## [34] "ENSDARG00000099088" "ENSDARG00000100252" "ENSDARG00000100312"
## [37] "ENSDARG00000101831"Warning: it is debated whether FDR correction is appropriate
on enrichment test results, since tests on different terms of the
ontologies are not independent. A nice discussion can be found in the
vignette of the topGO package.
Since version 2.14.0 (Bioconductor 3.11) BgeeDB store downloaded expression data in a local RSQLite database. The advantages of this approach compared to the one used in the previous BgeeDB versions are: * do not anymore need a server with lot of memory to access to subset of huge dataset (e.g GTeX dataset) * more granular filtering using arguments in the getSampleProcessedData() function * do not download twice the same data * fast access to data once integrated in the local database
This approach comes with some drawbacks : * the SQLite local database use more disk space than the previously conpressed .rds approach * first access to a dataset takes more time (integration to SQLite local database is time consuming)
It is possible to remove .rds files generated in previous versions of BgeeDB and not used anymore since version 2.14.0. The function below delete all .rds files for the selected species and for all datatype.
bgee <- Bgee$new(species="Mus_musculus", release = "14.1")
# delete all old .rds files of species Mus musculus
deleteOldData(bgee)As the new SQLite approach use more disk space it is now possible to delete all local data of one species from one release of Bgee.