Last updated: 2020-12-01

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Rmd acb1733 davetang 2020-12-01 Read GTF files into R

The Gene Transfer Format (GTF) is a refinement of the General Feature Format (GFF). A GFF file has nine columns:

Column Description
seqname The name of the sequence; must be a chromosome or scaffold
source The program that generated this feature
feature The name of this type of feature, e.g. “CDS”, “start_codon”, “stop_codon”, and “exon”
start The starting position of the feature in the sequence; the first base is numbered 1
end The ending position of the feature (inclusive)
score A score between 0 and 1000
strand Valid entries include “+”, “-”, or “.”
frame If the feature is a coding exon, frame should be a number between 0-2 that represents the reading frame of the first base. If the feature is not a coding exon, the value should be “.”
group All lines with the same group are linked together into a single item

The first eight fields in a GTF file are the same as GFF but the group field has been expanded into a list of attributes, where each attribute consists of a type/value pair. Attributes must end in a semi-colon and be separated from any following attribute by exactly one space. The attribute list must begin with the two mandatory attributes:

  1. gene_id value – A globally unique identifier for the genomic source of the sequence.
  2. transcript_id value – A globally unique identifier for the predicted transcript.

To get started, I will use the latest GENCODE GTF file.

gencode_ver <- 36
my_url <- paste0("ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_", gencode_ver, "/gencode.v", gencode_ver, ".annotation.gtf.gz")
my_gtf <- basename(my_url)

if (!file.exists(paste0("data/", my_gtf))){
  download.file(url = my_url, destfile = paste0("data/", my_gtf))
}

We will use rtracklayer to import the GTF file into R.

library(rtracklayer)
my_obj <- import(paste0("data/", my_gtf))
class(my_obj)
[1] "GRanges"
attr(,"package")
[1] "GenomicRanges"

The GenomicRanges package serves as the foundation for representing genomic locations within the Bioconductor project. The GRanges class represents a collection of genomic features that each have a single start and end location on the genome. This includes features such as contiguous binding sites, transcripts, and exons.

my_obj
GRanges object with 3014855 ranges and 21 metadata columns:
            seqnames      ranges strand |   source       type     score
               <Rle>   <IRanges>  <Rle> | <factor>   <factor> <numeric>
        [1]     chr1 11869-14409      + |   HAVANA gene              NA
        [2]     chr1 11869-14409      + |   HAVANA transcript        NA
        [3]     chr1 11869-12227      + |   HAVANA exon              NA
        [4]     chr1 12613-12721      + |   HAVANA exon              NA
        [5]     chr1 13221-14409      + |   HAVANA exon              NA
        ...      ...         ...    ... .      ...        ...       ...
  [3014851]     chrM 15888-15953      + |  ENSEMBL transcript        NA
  [3014852]     chrM 15888-15953      + |  ENSEMBL exon              NA
  [3014853]     chrM 15956-16023      - |  ENSEMBL gene              NA
  [3014854]     chrM 15956-16023      - |  ENSEMBL transcript        NA
  [3014855]     chrM 15956-16023      - |  ENSEMBL exon              NA
                phase           gene_id                          gene_type
            <integer>       <character>                        <character>
        [1]      <NA> ENSG00000223972.5 transcribed_unprocessed_pseudogene
        [2]      <NA> ENSG00000223972.5 transcribed_unprocessed_pseudogene
        [3]      <NA> ENSG00000223972.5 transcribed_unprocessed_pseudogene
        [4]      <NA> ENSG00000223972.5 transcribed_unprocessed_pseudogene
        [5]      <NA> ENSG00000223972.5 transcribed_unprocessed_pseudogene
        ...       ...               ...                                ...
  [3014851]      <NA> ENSG00000210195.2                            Mt_tRNA
  [3014852]      <NA> ENSG00000210195.2                            Mt_tRNA
  [3014853]      <NA> ENSG00000210196.2                            Mt_tRNA
  [3014854]      <NA> ENSG00000210196.2                            Mt_tRNA
  [3014855]      <NA> ENSG00000210196.2                            Mt_tRNA
              gene_name       level     hgnc_id          havana_gene
            <character> <character> <character>          <character>
        [1]     DDX11L1           2  HGNC:37102 OTTHUMG00000000961.2
        [2]     DDX11L1           2  HGNC:37102 OTTHUMG00000000961.2
        [3]     DDX11L1           2  HGNC:37102 OTTHUMG00000000961.2
        [4]     DDX11L1           2  HGNC:37102 OTTHUMG00000000961.2
        [5]     DDX11L1           2  HGNC:37102 OTTHUMG00000000961.2
        ...         ...         ...         ...                  ...
  [3014851]       MT-TT           3   HGNC:7499                 <NA>
  [3014852]       MT-TT           3   HGNC:7499                 <NA>
  [3014853]       MT-TP           3   HGNC:7494                 <NA>
  [3014854]       MT-TP           3   HGNC:7494                 <NA>
  [3014855]       MT-TP           3   HGNC:7494                 <NA>
                transcript_id      transcript_type transcript_name
                  <character>          <character>     <character>
        [1]              <NA>                 <NA>            <NA>
        [2] ENST00000456328.2 processed_transcript     DDX11L1-202
        [3] ENST00000456328.2 processed_transcript     DDX11L1-202
        [4] ENST00000456328.2 processed_transcript     DDX11L1-202
        [5] ENST00000456328.2 processed_transcript     DDX11L1-202
        ...               ...                  ...             ...
  [3014851] ENST00000387460.2              Mt_tRNA       MT-TT-201
  [3014852] ENST00000387460.2              Mt_tRNA       MT-TT-201
  [3014853]              <NA>                 <NA>            <NA>
  [3014854] ENST00000387461.2              Mt_tRNA       MT-TP-201
  [3014855] ENST00000387461.2              Mt_tRNA       MT-TP-201
            transcript_support_level         tag    havana_transcript
                         <character> <character>          <character>
        [1]                     <NA>        <NA>                 <NA>
        [2]                        1       basic OTTHUMT00000362751.1
        [3]                        1       basic OTTHUMT00000362751.1
        [4]                        1       basic OTTHUMT00000362751.1
        [5]                        1       basic OTTHUMT00000362751.1
        ...                      ...         ...                  ...
  [3014851]                       NA       basic                 <NA>
  [3014852]                       NA       basic                 <NA>
  [3014853]                     <NA>        <NA>                 <NA>
  [3014854]                       NA       basic                 <NA>
  [3014855]                       NA       basic                 <NA>
            exon_number           exon_id         ont  protein_id      ccdsid
            <character>       <character> <character> <character> <character>
        [1]        <NA>              <NA>        <NA>        <NA>        <NA>
        [2]        <NA>              <NA>        <NA>        <NA>        <NA>
        [3]           1 ENSE00002234944.1        <NA>        <NA>        <NA>
        [4]           2 ENSE00003582793.1        <NA>        <NA>        <NA>
        [5]           3 ENSE00002312635.1        <NA>        <NA>        <NA>
        ...         ...               ...         ...         ...         ...
  [3014851]        <NA>              <NA>        <NA>        <NA>        <NA>
  [3014852]           1 ENSE00001544475.2        <NA>        <NA>        <NA>
  [3014853]        <NA>              <NA>        <NA>        <NA>        <NA>
  [3014854]        <NA>              <NA>        <NA>        <NA>        <NA>
  [3014855]           1 ENSE00001544473.2        <NA>        <NA>        <NA>
  -------
  seqinfo: 25 sequences from an unspecified genome; no seqlengths

We can use the awesome plyranges package by Stuart Lee to find out the number of transcripts on each chromosome.

library(plyranges)
my_obj %>%
  group_by(seqnames) %>%
  summarise(total = n()) %>%
  as.data.frame()
   seqnames  total
1      chr1 276557
2      chr2 225529
3      chr3 191057
4      chr4 126025
5      chr5 134857
6      chr6 137896
7      chr7 143222
8      chr8 114067
9      chr9 107691
10    chr10 116513
11    chr11 176975
12    chr12 172270
13    chr13  48692
14    chr14 107092
15    chr15 111227
16    chr16 139150
17    chr17 181102
18    chr18  53969
19    chr19 176133
20    chr20  68882
21    chr21  33899
22    chr22  64343
23     chrX  97807
24     chrY   9757
25     chrM    143

Fetch mitochondrial transcripts.

my_obj %>%
  filter(seqnames == "chrM") %>%
  head()
GRanges object with 6 ranges and 21 metadata columns:
      seqnames    ranges strand |   source       type     score     phase
         <Rle> <IRanges>  <Rle> | <factor>   <factor> <numeric> <integer>
  [1]     chrM   577-647      + |  ENSEMBL gene              NA      <NA>
  [2]     chrM   577-647      + |  ENSEMBL transcript        NA      <NA>
  [3]     chrM   577-647      + |  ENSEMBL exon              NA      <NA>
  [4]     chrM  648-1601      + |  ENSEMBL gene              NA      <NA>
  [5]     chrM  648-1601      + |  ENSEMBL transcript        NA      <NA>
  [6]     chrM  648-1601      + |  ENSEMBL exon              NA      <NA>
                gene_id   gene_type   gene_name       level     hgnc_id
            <character> <character> <character> <character> <character>
  [1] ENSG00000210049.1     Mt_tRNA       MT-TF           3   HGNC:7481
  [2] ENSG00000210049.1     Mt_tRNA       MT-TF           3   HGNC:7481
  [3] ENSG00000210049.1     Mt_tRNA       MT-TF           3   HGNC:7481
  [4] ENSG00000211459.2     Mt_rRNA     MT-RNR1           3   HGNC:7470
  [5] ENSG00000211459.2     Mt_rRNA     MT-RNR1           3   HGNC:7470
  [6] ENSG00000211459.2     Mt_rRNA     MT-RNR1           3   HGNC:7470
      havana_gene     transcript_id transcript_type transcript_name
      <character>       <character>     <character>     <character>
  [1]        <NA>              <NA>            <NA>            <NA>
  [2]        <NA> ENST00000387314.1         Mt_tRNA       MT-TF-201
  [3]        <NA> ENST00000387314.1         Mt_tRNA       MT-TF-201
  [4]        <NA>              <NA>            <NA>            <NA>
  [5]        <NA> ENST00000389680.2         Mt_rRNA     MT-RNR1-201
  [6]        <NA> ENST00000389680.2         Mt_rRNA     MT-RNR1-201
      transcript_support_level         tag havana_transcript exon_number
                   <character> <character>       <character> <character>
  [1]                     <NA>        <NA>              <NA>        <NA>
  [2]                       NA       basic              <NA>        <NA>
  [3]                       NA       basic              <NA>           1
  [4]                     <NA>        <NA>              <NA>        <NA>
  [5]                       NA       basic              <NA>        <NA>
  [6]                       NA       basic              <NA>           1
                exon_id         ont  protein_id      ccdsid
            <character> <character> <character> <character>
  [1]              <NA>        <NA>        <NA>        <NA>
  [2]              <NA>        <NA>        <NA>        <NA>
  [3] ENSE00001544501.1        <NA>        <NA>        <NA>
  [4]              <NA>        <NA>        <NA>        <NA>
  [5]              <NA>        <NA>        <NA>        <NA>
  [6] ENSE00001544499.2        <NA>        <NA>        <NA>
  -------
  seqinfo: 25 sequences from an unspecified genome; no seqlengths

Summarise biotypes and plot.

my_obj %>%
  group_by(transcript_type) %>%
  summarise(number = n()) %>%
  as.data.frame() -> my_biotypes

my_biotypes %>%
  dplyr::filter(!is.na(transcript_type)) -> my_biotypes

my_biotypes %>%
  arrange(desc(number)) %>%
  dplyr::pull(transcript_type) -> my_order

my_biotypes$transcript_type <- factor(my_biotypes$transcript_type, levels = my_order)

library(ggplot2)

ggplot(my_biotypes, aes(transcript_type, number)) +
  geom_col() +
  theme_bw() +
  scale_y_log10() +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))

Version Author Date
6ef7a62 davetang 2020-12-01

Visualise transcripts near chr17:7661779-7687538.

library(Gviz)
library(GenomicFeatures)
my_txdb <- makeTxDbFromGFF(paste0("data/", my_gtf))

my_start <- 7661779 - 5000
my_end <- 7687538 + 5000
geneTrack <- GeneRegionTrack(my_txdb, chromosome="chr17", from=my_start, to=my_end)

plotTracks(geneTrack, chromosome="chr17", from=my_start, to=my_end, showId=TRUE)

Version Author Date
6ef7a62 davetang 2020-12-01

sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8

attached base packages:
 [1] grid      parallel  stats4    stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] GenomicFeatures_1.40.1 AnnotationDbi_1.50.3   Biobase_2.48.0        
 [4] Gviz_1.32.0            ggplot2_3.3.2          plyranges_1.8.0       
 [7] rtracklayer_1.48.0     GenomicRanges_1.40.0   GenomeInfoDb_1.24.2   
[10] IRanges_2.22.2         S4Vectors_0.26.1       BiocGenerics_0.34.0   
[13] workflowr_1.6.2       

loaded via a namespace (and not attached):
  [1] colorspace_2.0-0            ellipsis_0.3.1             
  [3] rprojroot_2.0.2             biovizBase_1.36.0          
  [5] htmlTable_2.1.0             XVector_0.28.0             
  [7] base64enc_0.1-3             fs_1.5.0                   
  [9] dichromat_2.0-0             rstudioapi_0.13            
 [11] farver_2.0.3                bit64_4.0.5                
 [13] xml2_1.3.2                  splines_4.0.2              
 [15] knitr_1.30                  Formula_1.2-4              
 [17] Rsamtools_2.4.0             cluster_2.1.0              
 [19] dbplyr_2.0.0                png_0.1-7                  
 [21] compiler_4.0.2              httr_1.4.2                 
 [23] backports_1.2.0             assertthat_0.2.1           
 [25] Matrix_1.2-18               lazyeval_0.2.2             
 [27] later_1.1.0.1               htmltools_0.5.0            
 [29] prettyunits_1.1.1           tools_4.0.2                
 [31] gtable_0.3.0                glue_1.4.2                 
 [33] GenomeInfoDbData_1.2.3      dplyr_1.0.2                
 [35] rappdirs_0.3.1              Rcpp_1.0.5                 
 [37] vctrs_0.3.5                 Biostrings_2.56.0          
 [39] xfun_0.19                   stringr_1.4.0              
 [41] lifecycle_0.2.0             ensembldb_2.12.1           
 [43] XML_3.99-0.5                zlibbioc_1.34.0            
 [45] scales_1.1.1                BSgenome_1.56.0            
 [47] VariantAnnotation_1.34.0    ProtGenerics_1.20.0        
 [49] hms_0.5.3                   promises_1.1.1             
 [51] SummarizedExperiment_1.18.2 AnnotationFilter_1.12.0    
 [53] RColorBrewer_1.1-2          yaml_2.2.1                 
 [55] curl_4.3                    memoise_1.1.0              
 [57] gridExtra_2.3               biomaRt_2.44.4             
 [59] rpart_4.1-15                latticeExtra_0.6-29        
 [61] stringi_1.5.3               RSQLite_2.2.1              
 [63] checkmate_2.0.0             BiocParallel_1.22.0        
 [65] rlang_0.4.9                 pkgconfig_2.0.3            
 [67] matrixStats_0.57.0          bitops_1.0-6               
 [69] evaluate_0.14               lattice_0.20-41            
 [71] purrr_0.3.4                 GenomicAlignments_1.24.0   
 [73] htmlwidgets_1.5.2           bit_4.0.4                  
 [75] tidyselect_1.1.0            magrittr_2.0.1             
 [77] R6_2.5.0                    generics_0.1.0             
 [79] Hmisc_4.4-2                 DelayedArray_0.14.1        
 [81] DBI_1.1.0                   pillar_1.4.7               
 [83] whisker_0.4                 foreign_0.8-80             
 [85] withr_2.3.0                 survival_3.2-7             
 [87] RCurl_1.98-1.2              nnet_7.3-14                
 [89] tibble_3.0.4                crayon_1.3.4               
 [91] BiocFileCache_1.12.1        rmarkdown_2.5              
 [93] jpeg_0.1-8.1                progress_1.2.2             
 [95] data.table_1.13.2           blob_1.2.1                 
 [97] git2r_0.27.1                digest_0.6.27              
 [99] httpuv_1.5.4                openssl_1.4.3              
[101] munsell_0.5.0               askpass_1.1