Reference genomic data for your projects are available from Ensembl. This is usually the latest build of the genome, transcriptome etc as well as the annotations in GTF or GFF format. Most common organisms are available from ensembl.org. You can select the organism and then click on Download FASTA/Download GTF/GFF which takes you to the FTP site.
You can also go directly to their FTP site ftp://ftp.ensembl.org/pub/release-96 where you can select the type of data you need, and then select the organism. For eg; homo_sapiens, under which you find cdna, cds, dna, dna_index, ncrna and pep. Under dna, the FASTA files are available as full genome or as separate chromosomes. Each of them are again available as regular (repeat content as normal bases), soft-masked (sm, repeat content in lowercase) or repeat-masked (rm, repeat content as Ns). Full genomes are also available as primary assembly or top-level. Primary assembly is what most people would need. The top-level is much larger in size and contains non-chromosomal contigs, patches, haplotypes etc. This is significantly larger in size compared to the primary assembly.
Clades such as metazoa, protists, bacteria, fungi and plants are available through separate ensembl websites. These are listed on http://ensemblgenomes.org/.
In this section, we will download annotation data using R package biomaRt. Annotations refer to known features (verified experimentally or predicted) in the genome. Usually, our features of interest in RNA-Seq are genes, their IDs, position in the genome, gene biotype (protein coding, non-coding etc) etc. We will also use the dplyr package to pipe data through functions.
library(biomaRt)
library(dplyr)
listMarts()## biomart version
## 1 ENSEMBL_MART_ENSEMBL Ensembl Genes 96
## 2 ENSEMBL_MART_MOUSE Mouse strains 96
## 3 ENSEMBL_MART_SNP Ensembl Variation 96
## 4 ENSEMBL_MART_FUNCGEN Ensembl Regulation 96We will use the code below to find the name of the Human ensembl genes dataset under ensembl mart.
mart <- useMart("ENSEMBL_MART_ENSEMBL")
ds <- as.data.frame(listDatasets(mart=mart))
# find all rows in dataset 'ds' where column 'description' contains the string 'human'
ds[grepl("human",tolower(ds$description)),]## dataset description version
## 73 hsapiens_gene_ensembl Human genes (GRCh38.p12) GRCh38.p12Now that we know the name of the dataset, we can list all the columns (filters) in this dataset.
mart <- useMart("ENSEMBL_MART_ENSEMBL")
mart <- useDataset(mart=mart,dataset="hsapiens_gene_ensembl")
la <- listAttributes(mart=mart)
head(la)## name description page
## 1 ensembl_gene_id Gene stable ID feature_page
## 2 ensembl_gene_id_version Gene stable ID version feature_page
## 3 ensembl_transcript_id Transcript stable ID feature_page
## 4 ensembl_transcript_id_version Transcript stable ID version feature_page
## 5 ensembl_peptide_id Protein stable ID feature_page
## 6 ensembl_peptide_id_version Protein stable ID version feature_pageOne can also search for attributes of interest.
searchAttributes(mart=mart,pattern="entrez")## name description page
## 55 entrezgene_trans_name EntrezGene transcript name ID feature_page
## 74 entrezgene NCBI gene ID feature_pageWe create a vector of our columns of interest.
myattributes <- c("ensembl_gene_id",
"entrezgene",
"external_gene_name",
"chromosome_name",
"start_position",
"end_position",
"strand",
"gene_biotype",
"description")We then use this to download our data. Note that this can be a slow step.
mart <- useMart("ENSEMBL_MART_ENSEMBL")
mart <- useDataset(mart=mart,dataset="hsapiens_gene_ensembl")
bdata <- getBM(mart=mart,attributes=myattributes,uniqueRows=T)
head(bdata) ensembl_gene_id entrezgene external_gene_name chromosome_name start_position
1 ENSG00000210049 NA MT-TF MT 577
2 ENSG00000211459 NA MT-RNR1 MT 648
3 ENSG00000210077 NA MT-TV MT 1602
4 ENSG00000210082 NA MT-RNR2 MT 1671
5 ENSG00000209082 NA MT-TL1 MT 3230
6 ENSG00000198888 4535 MT-ND1 MT 3307
end_position strand gene_biotype
1 647 1 Mt_tRNA
2 1601 1 Mt_rRNA
3 1670 1 Mt_tRNA
4 3229 1 Mt_rRNA
5 3304 1 Mt_tRNA
6 4262 1 protein_coding
description
1 mitochondrially encoded tRNA-Phe (UUU/C) [Source:HGNC Symbol;Acc:HGNC:7481]
2 mitochondrially encoded 12S RNA [Source:HGNC Symbol;Acc:HGNC:7470]
3 mitochondrially encoded tRNA-Val (GUN) [Source:HGNC Symbol;Acc:HGNC:7500]
4 mitochondrially encoded 16S RNA [Source:HGNC Symbol;Acc:HGNC:7471]
5 mitochondrially encoded tRNA-Leu (UUA/G) 1 [Source:HGNC Symbol;Acc:HGNC:7490]
6 mitochondrially encoded NADH:ubiquinone oxidoreductase core subunit 1 [Source:HGNC Symbol;Acc:HGNC:7455]We find that there are several duplicates for all the IDs. This needs to be fixed when this information is to be used downstream.
sum(duplicated(bdata$ensembl_gene_id))
sum(duplicated(bdata$entrezgene))
sum(duplicated(bdata$external_gene_name))252
45751
6417# arrange table by chr name and start position
bdata <- dplyr::arrange(bdata,chromosome_name,start_position)
write.table(bdata,"./data/human_genes.txt",row.names=F,quote=F,col.names=T,sep="\t")Here we download transcript to gene mappings. Notice that we can specify the mart and dataset in the useMart() function.
mart <- useMart(biomart="ensembl",dataset="hsapiens_gene_ensembl")
t2g <- getBM(attributes=c("ensembl_transcript_id","ensembl_gene_id","external_gene_name"),mart=mart)
write.table(t2g,"./data/human_transcripts.txt",row.names=F,quote=F,col.names=T,sep="\t")The transcipt information file is saved to a file and will be used in the lab on Kallisto.
Similarly, we can get entrez gene ID to GO ID relationships. List all the GO related filters:
mart <- biomaRt::useMart(biomart="ensembl",dataset="hsapiens_gene_ensembl")
lf <- listFilters(mart=mart)
# find all rows in dataset 'lf' where column 'name' contains the string 'go'
lf[grepl("go",tolower(lf$name)),] name description
1 with_go With GO ID(s)
2 with_goslim_goa With GOSlim GOA ID(s)
3 go GO ID(s) [e.g. GO:0000002]
4 goslim_goa GOSlim GOA ID(s) [e.g. GO:0000003]
5 go_parent_term Parent term accession
6 go_parent_name Parent term name
7 go_evidence_code GO Evidence code
8 with_cdingo_homolog Orthologous Dingo Genes
9 with_ggorilla_homolog Orthologous Gorilla Genesmart <- biomaRt::useMart(biomart="ensembl",dataset="hsapiens_gene_ensembl")
bdata <- getBM(mart=mart,attributes=c("entrezgene","go","go_evidence_code"),uniqueRows=T)
write.table(bdata,"./data/go.txt",row.names=F,quote=F,col.names=T,sep="\t")We can also take a quick look at converting IDs. It is often desirable to convert a certain gene identifier to another (ensembl gene ID, entrez gene ID, gene ID). Sometimes, it may be necessary to convert gene IDs of one organism to another. biomaRt has a convenient function for this called getLDS().
Here is an example where we convert a few mouse ensembl IDs to Human Hugo gene IDs.
mouse_genes <- c("ENSMUSG00000035847","ENSMUSG00000000214")
mouse <- useMart("ensembl",dataset="mmusculus_gene_ensembl")
human <- useMart("ensembl",dataset="hsapiens_gene_ensembl")
human_genes <- getLDS(attributes=c("ensembl_gene_id"),filters="ensembl_gene_id",values=mouse_genes,mart=mouse, attributesL=c("hgnc_symbol"),martL=human,valuesL="hgnc_symbol",uniqueRows=F)[,1] Gene.stable.ID HGNC.symbol
1 ENSMUSG00000000214 TH
2 ENSMUSG00000035847 IDS## R version 3.5.2 (2018-12-20)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats datasets grDevices utils graphics
## [8] methods base
##
## other attached packages:
## [1] pheatmap_1.0.12 edgeR_3.24.3
## [3] limma_3.38.3 biomaRt_2.39.3
## [5] ggplot2_3.1.1 dplyr_0.8.0.1
## [7] leaflet_2.0.2 captioner_2.2.3
## [9] bookdown_0.9 knitr_1.22
## [11] DESeq2_1.22.2 SummarizedExperiment_1.12.0
## [13] DelayedArray_0.8.0 BiocParallel_1.16.6
## [15] matrixStats_0.54.0 Biobase_2.42.0
## [17] GenomicRanges_1.34.0 GenomeInfoDb_1.18.2
## [19] IRanges_2.16.0 S4Vectors_0.20.1
## [21] BiocGenerics_0.28.0
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-137 bitops_1.0-6 bit64_0.9-7
## [4] RColorBrewer_1.1-2 progress_1.2.0 httr_1.4.0
## [7] tools_3.5.2 backports_1.1.4 R6_2.4.0
## [10] rpart_4.1-13 mgcv_1.8-26 Hmisc_4.2-0
## [13] DBI_1.0.0 lazyeval_0.2.2 colorspace_1.4-1
## [16] nnet_7.3-12 withr_2.1.2 tidyselect_0.2.5
## [19] gridExtra_2.3 prettyunits_1.0.2 curl_3.3
## [22] bit_1.1-14 compiler_3.5.2 htmlTable_1.13.1
## [25] labeling_0.3 scales_1.0.0 checkmate_1.9.1
## [28] genefilter_1.64.0 stringr_1.4.0 digest_0.6.18
## [31] foreign_0.8-71 rmarkdown_1.12 XVector_0.22.0
## [34] base64enc_0.1-3 pkgconfig_2.0.2 htmltools_0.3.6
## [37] htmlwidgets_1.3 rlang_0.3.4 rstudioapi_0.10
## [40] RSQLite_2.1.1 shiny_1.3.2 jsonlite_1.6
## [43] crosstalk_1.0.0 acepack_1.4.1 RCurl_1.95-4.12
## [46] magrittr_1.5 GenomeInfoDbData_1.2.0 Formula_1.2-3
## [49] Matrix_1.2-15 Rcpp_1.0.1 munsell_0.5.0
## [52] stringi_1.4.3 yaml_2.2.0 zlibbioc_1.28.0
## [55] plyr_1.8.4 grid_3.5.2 blob_1.1.1
## [58] promises_1.0.1 crayon_1.3.4 lattice_0.20-38
## [61] cowplot_0.9.4 splines_3.5.2 annotate_1.60.1
## [64] hms_0.4.2 locfit_1.5-9.1 pillar_1.3.1
## [67] ggpubr_0.2 geneplotter_1.60.0 XML_3.98-1.19
## [70] glue_1.3.1 evaluate_0.13 latticeExtra_0.6-28
## [73] data.table_1.12.2 httpuv_1.5.1 tidyr_0.8.3
## [76] gtable_0.3.0 purrr_0.3.2 assertthat_0.2.1
## [79] xfun_0.6 mime_0.6 xtable_1.8-3
## [82] later_0.8.0 survival_2.43-3 tibble_2.1.1
## [85] AnnotationDbi_1.44.0 memoise_1.1.0 cluster_2.0.7-1