An introduction to the bambu package using NanoporeRNASeq data

Introduction

NanoporeRNASeq contains RNA-Seq data from the K562 and MCF7 cell lines that were generated by the SG-NEx project (https://github.com/GoekeLab/sg-nex-data). Each of these cell line has three replicates, with 1 direct RNA sequencing data and 2 cDNA sequencing data. The files contains reads aligned to the human genome (Grch38) chromosome 22 (1:25500000).

Accessing NanoporeRNASeq data

Load the NanoporeRNASeq package

library("NanoporeRNASeq")

List the samples

data("SGNexSamples")
SGNexSamples
##> DataFrame with 6 rows and 6 columns
##>                sample_id    Platform    cellLine    protocol cancer_type
##>              <character> <character> <character> <character> <character>
##> 1 K562_directcDNA_repl..      MinION        K562  directcDNA   Leukocyte
##> 2 K562_directcDNA_repl..     GridION        K562  directcDNA   Leukocyte
##> 3 K562_directRNA_repli..     GridION        K562   directRNA   Leukocyte
##> 4 MCF7_directcDNA_repl..      MinION        MCF7  directcDNA      Breast
##> 5 MCF7_directcDNA_repl..     GridION        MCF7  directcDNA      Breast
##> 6 MCF7_directRNA_repli..     GridION        MCF7   directRNA      Breast
##>                fileNames
##>              <character>
##> 1 NanoporeRNASeq/versi..
##> 2 NanoporeRNASeq/versi..
##> 3 NanoporeRNASeq/versi..
##> 4 NanoporeRNASeq/versi..
##> 5 NanoporeRNASeq/versi..
##> 6 NanoporeRNASeq/versi..

List the available BamFile

library(ExperimentHub)
NanoporeData <- query(ExperimentHub(), c("NanoporeRNA", "GRCh38", "Bam"))
bamFiles <- Rsamtools::BamFileList(NanoporeData[["EH3808"]], NanoporeData[["EH3809"]],
    NanoporeData[["EH3810"]], NanoporeData[["EH3811"]], NanoporeData[["EH3812"]],
    NanoporeData[["EH3813"]])

Get the annotation GRangesList

data("HsChr22BambuAnnotation")
HsChr22BambuAnnotation
##> GRangesList object of length 1500:
##> $ENST00000043402
##> GRanges object with 2 ranges and 2 metadata columns:
##>       seqnames            ranges strand | exon_rank exon_endRank
##>          <Rle>         <IRanges>  <Rle> | <integer>    <integer>
##>   [1]       22 20241415-20243110      - |         2            1
##>   [2]       22 20268071-20268531      - |         1            2
##>   -------
##>   seqinfo: 1 sequence from an unspecified genome; no seqlengths
##> 
##> $ENST00000086933
##> GRanges object with 3 ranges and 2 metadata columns:
##>       seqnames            ranges strand | exon_rank exon_endRank
##>          <Rle>         <IRanges>  <Rle> | <integer>    <integer>
##>   [1]       22 19148576-19149095      - |         3            1
##>   [2]       22 19149663-19149916      - |         2            2
##>   [3]       22 19150025-19150283      - |         1            3
##>   -------
##>   seqinfo: 1 sequence from an unspecified genome; no seqlengths
##> 
##> $ENST00000155674
##> GRanges object with 8 ranges and 2 metadata columns:
##>       seqnames            ranges strand | exon_rank exon_endRank
##>          <Rle>         <IRanges>  <Rle> | <integer>    <integer>
##>   [1]       22 17137511-17138357      - |         8            1
##>   [2]       22 17138550-17138738      - |         7            2
##>   [3]       22 17141059-17141233      - |         6            3
##>   [4]       22 17143098-17143131      - |         5            4
##>   [5]       22 17145024-17145117      - |         4            5
##>   [6]       22 17148448-17148560      - |         3            6
##>   [7]       22 17149542-17149745      - |         2            7
##>   [8]       22 17165209-17165287      - |         1            8
##>   -------
##>   seqinfo: 1 sequence from an unspecified genome; no seqlengths
##> 
##> ...
##> <1497 more elements>

Visualizing gene of interest from a single bam file

We can visualize the one sample for a single gene ENST00000215832 (MAPK1)

library(ggbio)
range <- HsChr22BambuAnnotation$ENST00000215832
# plot mismatch track
library(BSgenome.Hsapiens.NCBI.GRCh38)
# plot annotation track
tx <- autoplot(range, aes(col = strand), group.selfish = TRUE)
# plot coverage track
coverage <- autoplot(bamFiles[[1]], aes(col = coverage), which = range)

# merge the tracks into one plot
tracks(annotation = tx, coverage = coverage, heights = c(1, 3)) + theme_minimal()

Running Bambu with NanoporeRNASeq data

Load the bambu package

library(bambu)
genomeSequenceData <- query(ExperimentHub(), c("NanoporeRNA", "GRCh38", "FASTA"))
genomeSequence <- genomeSequenceData[["EH7260"]]

Run bambu

Applying bambu to bamFiles

se <- bambu(reads = bamFiles, annotations = HsChr22BambuAnnotation, genome = genomeSequence)

bambu returns a SummarizedExperiment object

se
##> class: RangedSummarizedExperiment 
##> dim: 1542 6 
##> metadata(2): incompatibleCounts warnings
##> assays(4): counts CPM fullLengthCounts uniqueCounts
##> rownames(1542): BambuTx1 BambuTx2 ... ENST00000641933 ENST00000641967
##> rowData names(11): TXNAME GENEID ... txid eqClassById
##> colnames(6): ce9765df3dce7_3844 ce976cd9b5c4_3846 ...
##>   ce9767f6adae5_3852 ce9767422b638_3854
##> colData names(1): name

Visualizing gene examples

We can visualize the annotated and novel isoforms identified in this gene example using plot functions from bambu

plotBambu(se, type = "annotation", gene_id = "ENSG00000099968")

##> [[1]]
##> TableGrob (3 x 1) "arrange": 3 grobs
##>   z     cells    name                grob
##> 1 1 (2-2,1-1) arrange      gtable[layout]
##> 2 2 (3-3,1-1) arrange      gtable[layout]
##> 3 3 (1-1,1-1) arrange text[GRID.text.262]
sessionInfo()
##> R Under development (unstable) (2022-10-25 r83175)
##> Platform: x86_64-pc-linux-gnu (64-bit)
##> Running under: Ubuntu 22.04.1 LTS
##> 
##> Matrix products: default
##> BLAS:   /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
##> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##> 
##> locale:
##>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##>  [3] LC_TIME=en_GB              LC_COLLATE=C              
##>  [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       
##> 
##> attached base packages:
##> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
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##>  [1] bambu_3.1.0                           
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##>  [3] Biobase_2.59.0                        
##>  [4] MatrixGenerics_1.11.0                 
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##>  [6] BSgenome.Hsapiens.NCBI.GRCh38_1.3.1000
##>  [7] BSgenome_1.67.0                       
##>  [8] rtracklayer_1.59.0                    
##>  [9] ggbio_1.47.0                          
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##> [11] Rsamtools_2.15.0                      
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##> [13] XVector_0.39.0                        
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##> [15] GenomeInfoDb_1.35.0                   
##> [16] IRanges_2.33.0                        
##> [17] S4Vectors_0.37.0                      
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