Contents

1 Introduction

The incorporation of personalised or population-level variants into a reference genome has been shown to have a significant impact on subsequent alignments (Kaminow et al. 2022). Whilst implemented for splice-aware alignment or RNA-Seq data using STARconsensus, the package transmogR enables the creation of a variant-modified reference transcriptome for use with pseudo aligners such as salmon (Srivastava et al. 2020). In addition, multiple visualisation and summarisation methods are included for a cursory analysis of any custom variant sets being used.

Whilst the subsequent code is demonstrated on a small genomic region, the complete process for creating a modified a reference can run in under 20 minutes if using 4 or more cores.

Importantly, it is expected that the user will have carefully prepared their set of variants such that only the following exact variant types are included.

Any variants which do not conform to these criteria will likely cause a series of frustrating errors when attempting to use this package.

2 Setup

2.1 Installation

In order to perform the operations in this vignette, the following packages require installation.

if (!"BiocManager" %in% rownames(installed.packages()))
  install.packages("BiocManager")
BiocManager::install("transmogR")

Once these packages are installed, we can load them easily

library(VariantAnnotation)
library(rtracklayer)
library(extraChIPs)
library(transmogR)
library(BSgenome.Hsapiens.UCSC.hg38)

2.2 Required Data

In order to create a modified reference, three primary data objects are required: 1) a reference genome; 2) a set of genomic variants; and 3) a set of exon-level co-ordinates defining transcript structure.

For this vignette, we’ll use GRCh38 as our primary reference genome, but restricting the sequences to chr1 only. The package can take either a DNAStringSet or BSgenome object as the reference genome.

chr1 <- getSeq(BSgenome.Hsapiens.UCSC.hg38, "chr1")
chr1 <- as(chr1, "DNAStringSet")
names(chr1) <- "chr1"
chr1
## DNAStringSet object of length 1:
##         width seq                                           names               
## [1] 248956422 NNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNN chr1

A small set of variants has been provided with the package.

sq <- seqinfo(chr1)
genome(sq) <- "GRCh38"
vcf <- system.file("extdata/1000GP_subset.vcf.gz", package = "transmogR")
vcf_param <- ScanVcfParam(fixed = "ALT", info = NA, which = GRanges(sq))
var <- rowRanges(readVcf(vcf, param = vcf_param))
seqinfo(var) <- sq
var
## GRanges object with 100 ranges and 3 metadata columns:
##                         seqnames        ranges strand | paramRangeID
##                            <Rle>     <IRanges>  <Rle> |     <factor>
##            1:113969:C:T     chr1        113969      * |         chr1
##            1:121009:C:T     chr1        121009      * |         chr1
##            1:123511:G:A     chr1        123511      * |         chr1
##            1:126113:C:A     chr1        126113      * |         chr1
##            1:128798:C:T     chr1        128798      * |         chr1
##                     ...      ...           ...    ... .          ...
##            1:839405:G:A     chr1        839405      * |         chr1
##   1:839480:CACACACCTG:C     chr1 839480-839494      * |         chr1
##   1:839515:CTAGACACAC:C     chr1 839515-839543      * |         chr1
##            1:840046:G:A     chr1        840046      * |         chr1
##            1:840279:A:G     chr1        840279      * |         chr1
##                                             REF                ALT
##                                  <DNAStringSet> <DNAStringSetList>
##            1:113969:C:T                       C                  T
##            1:121009:C:T                       C                  T
##            1:123511:G:A                       G                  A
##            1:126113:C:A                       C                  A
##            1:128798:C:T                       C                  T
##                     ...                     ...                ...
##            1:839405:G:A                       G                  A
##   1:839480:CACACACCTG:C         CACACACCTGGACAA                  C
##   1:839515:CTAGACACAC:C CTAGACACAC...CACACACACG                  C
##            1:840046:G:A                       G                  A
##            1:840279:A:G                       A                  G
##   -------
##   seqinfo: 1 sequence from GRCh38 genome

An additional set of transcripts derived from Gencode v441 https://www.gencodegenes.org/human/ has also been provided.

f <- system.file("extdata/gencode.v44.subset.gtf.gz", package = "transmogR")
gtf <- import.gff(f, which = GRanges(sq))
seqinfo(gtf) <- sq
gtf
## GRanges object with 603 ranges and 11 metadata columns:
##         seqnames        ranges strand |      source       type     score
##            <Rle>     <IRanges>  <Rle> |    <factor>   <factor> <numeric>
##     [1]     chr1  89295-133723      - | rtracklayer gene              NA
##     [2]     chr1  89295-120932      - | rtracklayer transcript        NA
##     [3]     chr1 120775-120932      - | rtracklayer exon              NA
##     [4]     chr1 112700-112804      - | rtracklayer exon              NA
##     [5]     chr1   92091-92240      - | rtracklayer exon              NA
##     ...      ...           ...    ... .         ...        ...       ...
##   [599]     chr1 852671-852766      + | rtracklayer exon              NA
##   [600]     chr1 853391-854096      + | rtracklayer exon              NA
##   [601]     chr1 851348-852752      + | rtracklayer transcript        NA
##   [602]     chr1 851348-852110      + | rtracklayer exon              NA
##   [603]     chr1 852671-852752      + | rtracklayer exon              NA
##             phase            gene_id   gene_type       gene_name
##         <integer>        <character> <character>     <character>
##     [1]      <NA>  ENSG00000238009.6      lncRNA ENSG00000238009
##     [2]      <NA>  ENSG00000238009.6      lncRNA ENSG00000238009
##     [3]      <NA>  ENSG00000238009.6      lncRNA ENSG00000238009
##     [4]      <NA>  ENSG00000238009.6      lncRNA ENSG00000238009
##     [5]      <NA>  ENSG00000238009.6      lncRNA ENSG00000238009
##     ...       ...                ...         ...             ...
##   [599]      <NA> ENSG00000228794.12      lncRNA       LINC01128
##   [600]      <NA> ENSG00000228794.12      lncRNA       LINC01128
##   [601]      <NA> ENSG00000228794.12      lncRNA       LINC01128
##   [602]      <NA> ENSG00000228794.12      lncRNA       LINC01128
##   [603]      <NA> ENSG00000228794.12      lncRNA       LINC01128
##             transcript_id transcript_type transcript_name exon_number
##               <character>     <character>     <character> <character>
##     [1]              <NA>            <NA>            <NA>        <NA>
##     [2] ENST00000466430.5          lncRNA ENST00000466430        <NA>
##     [3] ENST00000466430.5          lncRNA ENST00000466430           1
##     [4] ENST00000466430.5          lncRNA ENST00000466430           2
##     [5] ENST00000466430.5          lncRNA ENST00000466430           3
##     ...               ...             ...             ...         ...
##   [599] ENST00000688420.1          lncRNA   LINC01128-235           4
##   [600] ENST00000688420.1          lncRNA   LINC01128-235           5
##   [601] ENST00000425657.1 retained_intron   LINC01128-203        <NA>
##   [602] ENST00000425657.1 retained_intron   LINC01128-203           1
##   [603] ENST00000425657.1 retained_intron   LINC01128-203           2
##   -------
##   seqinfo: 1 sequence from GRCh38 genome

Splitting this gtf into feature types can also be very handy for downstream processes.

gtf <- splitAsList(gtf, gtf$type)

3 Inspecting Variants

Knowing where our variants lie, and how they relate to each other can be informative, and as such, some simple visualisation and summarisation functions have been included. In the following, we can check to see how many exons directly overlap a variant, showing how many unique genes this summarises to. Any ids, which don’t overlap a variants are also described in the plot title.

upsetVarByCol(gtf$exon, var, mcol = "gene_id")
Included variants which overlap exonic sequences, summarised by unique gene ids

Figure 1: Included variants which overlap exonic sequences, summarised by unique gene ids

In addition, we can obtain a simple breakdown of overlapping regions using a GRangesList. We can use the function defineRegions() from extraChIPs to define regions based on gene & transcript locations. This function assigns each position in the genome uniquely to a given feature hierarchically, using all provided transcripts, meaning some exons will be considered as promoters. To ensure that all exons are included as exons, we can just substitute in the values from our gtf for this feature type.

regions <- defineRegions(gtf$gene, gtf$transcript, gtf$exon, proximal = 0)
regions$exon <- granges(gtf$exon)
overlapsByVar(regions, var)
##                   Deletion Insertion SNV Total
## promoter                 1         1  46    48
## upstream_promoter        0         0  20    20
## exon                     0         1  37    38
## intron                   5         0  24    29
## intergenic               0         0   0     0

4 Creating Modified Reference Sequences

4.1 Modifying a Reference Genome

The simplest method for modifying a reference genome is to simply call genomogrify() with either a DNAStringSet or BSgenome object. A tag can be optionally added to all sequence names to avoid confusion.

chr1_mod <- genomogrify(chr1, var, tag = "mod")
chr1_mod
## DNAStringSet object of length 1:
##         width seq                                           names               
## [1] 248956362 NNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNN chr1_mod

The new reference genome can be exported to fasta format using writeXStringSet().

4.2 Modifying a Reference Transcriptome

The process for creating a variant-modified reference transcriptome is essentially the same as above, with the addition of exon-level co-ordinates for the set of transcripts. An optional tag can be added to all transcripts to indicate which have been modified, with an additional tag able to be included which indicates which type of variant has been incorporated into the new transcript sequence. Variant tags will be one of s, i or d to indicate SNV, Insertions or Deletions respectively. In our example dataset, only one transcript contains both SNVs and an insertion.

trans_mod <- transmogrify(
    chr1, var, gtf$exon, tag = "mod", var_tags = TRUE
)
trans_mod
## DNAStringSet object of length 112:
##       width seq                                             names               
##   [1]  1947 ACCCTCCTTGAGACAGCCCTCC...TAAACAATACACACGTGTTAAA ENST00000326734.2...
##   [2]  1702 CACACCGTGAGCTGCTGAGACG...GTGCAGGGCACAGGTGGGCGCC ENST00000357876.6
##   [3]  1358 AATCAGAACTCGCGGTGGGGGC...ATAAAATTAATGAGAATGATCT ENST00000412115.2...
##   [4]   421 GACAGGGTCTCCCTCTGTTGTC...AAAGCATCCAGGATTCAATGAA ENST00000414688.6
##   [5]   894 CTGCAATACCTCACTCAATCTC...GTATTTGATGGCCTCTGTTGTT ENST00000415295.1
##   ...   ... ...
## [108]  3097 GCCAGTGTCTCCGCCGGTTGAA...GATAAAACTTAAAAATATCCCC ENST00000701768.1
## [109]  1601 GGAGTGCGTTCGGTGCGCCGTG...AGCTATTAAAAGAGACAGAGGC ENST00000702098.1
## [110]  1432 GTCTGCGTCGGGTTCTGTTGGA...ATCAATAAAAATTTAAATGCTC ENST00000702273.1
## [111]  1460 CTGTTAGGTAAGAGGCGCGTGA...ATCAATAAAAATTTAAATGCTC ENST00000702557.1
## [112]  1651 GTTAGGTAAGAGGCGCGTGAGG...GCTATTAAAAGAGACAGAGGCA ENST00000702847.1
names(trans_mod)[grepl("_si", names(trans_mod))]
## [1] "ENST00000473798.2_mod_si"

This can be simply exported again using writeXStringSet(). If using decoy transcripts for salmon, the gentrome can also be simply exported by combining the two modified references.

Both of the above processes rely on the lower-level functions owl() and indelcator() which overwrite letters or substitute indels respectively. These are also able to be called individually as shown in the help pages.

5 Additional Capabilities

5.1 Pseudo-Autosomal Regions (PAR-Y)

Beyond these lower-level functions, PAR-Y regions for hg38, hg19 and CHM13 are able to obtained using parY() and passing the appropriate seqinfo object. This seqinfo object checks the length of the Y-chromosome and guesses which reference genome has been used.

sq_hg38 <- seqinfo(BSgenome.Hsapiens.UCSC.hg38)
parY(sq_hg38)
## GRanges object with 2 ranges and 1 metadata column:
##       seqnames            ranges strand |         PAR
##          <Rle>         <IRanges>  <Rle> | <character>
##   [1]     chrY     10001-2781479      * |        PAR1
##   [2]     chrY 56887903-57217415      * |        PAR2
##   -------
##   seqinfo: 711 sequences (1 circular) from hg38 genome

If the user wishes to exclude transcripts in the PAR-Y region, these ranges can be passed to transmogrify() and any transcripts which overlap the PAR-Y region will be excluded. Alternatively, passing the entire Y-chromosome to transmogrify() will exclude all transcripts in the Y-chromosome, as may be preferred for female-specific references.

These regions can also be passed to genomogrify() as a mask, with all bases within the masked region being re-assigned as an N.

5.2 Splice Junctions

In addition, the set of splice junctions associated with any transcript can be obtained using our known exons.

ec <- c("transcript_id", "transcript_name", "gene_id", "gene_name")
sj <- sjFromExons(gtf$exon, extra_cols = ec)
sj
## GRanges object with 730 ranges and 6 metadata columns:
##         seqnames        ranges strand |        site     transcript_id
##            <Rle>     <IRanges>  <Rle> | <character>       <character>
##     [1]     chr1 182745-182752      + |       donor ENST00000624431.2
##     [2]     chr1 183128-183132      + |    acceptor ENST00000624431.2
##     [3]     chr1 183215-183222      + |       donor ENST00000624431.2
##     [4]     chr1 183490-183494      + |    acceptor ENST00000624431.2
##     [5]     chr1 183570-183577      + |       donor ENST00000624431.2
##     ...      ...           ...    ... .         ...               ...
##   [726]     chr1 810061-810068      - |       donor ENST00000590817.3
##   [727]     chr1 810170-810174      - |    acceptor ENST00000447500.4
##   [728]     chr1 817367-817374      - |       donor ENST00000447500.4
##   [729]     chr1 827664-827671      - |       donor ENST00000635509.2
##   [730]     chr1 827664-827671      - |       donor ENST00000634337.2
##         transcript_name            gene_id       gene_name exon_number
##             <character>        <character>     <character>   <integer>
##     [1]    DDX11L17-201  ENSG00000279928.2        DDX11L17           1
##     [2]    DDX11L17-201  ENSG00000279928.2        DDX11L17           2
##     [3]    DDX11L17-201  ENSG00000279928.2        DDX11L17           2
##     [4]    DDX11L17-201  ENSG00000279928.2        DDX11L17           3
##     [5]    DDX11L17-201  ENSG00000279928.2        DDX11L17           3
##     ...             ...                ...             ...         ...
##   [726] ENST00000590817  ENSG00000230092.8 ENSG00000230092           1
##   [727] ENST00000447500  ENSG00000290784.1 ENSG00000290784           2
##   [728] ENST00000447500  ENSG00000290784.1 ENSG00000290784           1
##   [729] ENST00000635509 ENSG00000230021.10 ENSG00000230021           1
##   [730] ENST00000634337 ENSG00000230021.10 ENSG00000230021           1
##   -------
##   seqinfo: 1 sequence from GRCh38 genome

Many splice junctions will be shared across multiple transcripts, so the returned set of junctions can also be simplified using chopMC() from extraChIPs.

chopMC(sj)
## GRanges object with 196 ranges and 6 metadata columns:
##         seqnames        ranges strand |        site
##            <Rle>     <IRanges>  <Rle> | <character>
##     [1]     chr1 182745-182752      + |       donor
##     [2]     chr1 183128-183132      + |    acceptor
##     [3]     chr1 183215-183222      + |       donor
##     [4]     chr1 183490-183494      + |    acceptor
##     [5]     chr1 183570-183577      + |       donor
##     ...      ...           ...    ... .         ...
##   [192]     chr1 808623-808627      - |    acceptor
##   [193]     chr1 810061-810068      - |       donor
##   [194]     chr1 810170-810174      - |    acceptor
##   [195]     chr1 817367-817374      - |       donor
##   [196]     chr1 827664-827671      - |       donor
##                               transcript_id                 transcript_name
##                             <CharacterList>                 <CharacterList>
##     [1]                   ENST00000624431.2                    DDX11L17-201
##     [2]                   ENST00000624431.2                    DDX11L17-201
##     [3]                   ENST00000624431.2                    DDX11L17-201
##     [4]                   ENST00000624431.2                    DDX11L17-201
##     [5]                   ENST00000624431.2                    DDX11L17-201
##     ...                                 ...                             ...
##   [192] ENST00000447500.4,ENST00000590817.3 ENST00000447500,ENST00000590817
##   [193] ENST00000447500.4,ENST00000590817.3 ENST00000447500,ENST00000590817
##   [194]                   ENST00000447500.4                 ENST00000447500
##   [195]                   ENST00000447500.4                 ENST00000447500
##   [196] ENST00000635509.2,ENST00000634337.2 ENST00000635509,ENST00000634337
##                                       gene_id                       gene_name
##                               <CharacterList>                 <CharacterList>
##     [1]                     ENSG00000279928.2                        DDX11L17
##     [2]                     ENSG00000279928.2                        DDX11L17
##     [3]                     ENSG00000279928.2                        DDX11L17
##     [4]                     ENSG00000279928.2                        DDX11L17
##     [5]                     ENSG00000279928.2                        DDX11L17
##     ...                                   ...                             ...
##   [192]   ENSG00000290784.1,ENSG00000230092.8 ENSG00000290784,ENSG00000230092
##   [193]   ENSG00000290784.1,ENSG00000230092.8 ENSG00000290784,ENSG00000230092
##   [194]                     ENSG00000290784.1                 ENSG00000290784
##   [195]                     ENSG00000290784.1                 ENSG00000290784
##   [196] ENSG00000230021.10,ENSG00000230021.10 ENSG00000230021,ENSG00000230021
##           exon_number
##         <IntegerList>
##     [1]             1
##     [2]             2
##     [3]             2
##     [4]             3
##     [5]             3
##     ...           ...
##   [192]           3,2
##   [193]           2,1
##   [194]             2
##   [195]             1
##   [196]           1,1
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

As a further alternative, splice junctions can be returned as a set of interactions, with donor sites being assigned to the anchorOne element, and acceptor sites being placed in the anchorTwo element This enables the identification of all splice junctions for specific transcripts.

sj <- sjFromExons(gtf$exon, extra_cols = ec, as = "GInteractions")
subset(sj, transcript_name == "DDX11L17-201")
## GInteractions object with 4 interactions and 5 metadata columns:
##       seqnames1       ranges1     seqnames2       ranges2 |     transcript_id
##           <Rle>     <IRanges>         <Rle>     <IRanges> |       <character>
##   [1]      chr1 182745-182752 ---      chr1 183128-183132 | ENST00000624431.2
##   [2]      chr1 183215-183222 ---      chr1 183490-183494 | ENST00000624431.2
##   [3]      chr1 183570-183577 ---      chr1 183736-183740 | ENST00000624431.2
##   [4]      chr1 183900-183907 ---      chr1 183977-183981 | ENST00000624431.2
##       transcript_name           gene_id   gene_name        sj
##           <character>       <character> <character> <integer>
##   [1]    DDX11L17-201 ENSG00000279928.2    DDX11L17         1
##   [2]    DDX11L17-201 ENSG00000279928.2    DDX11L17         2
##   [3]    DDX11L17-201 ENSG00000279928.2    DDX11L17         3
##   [4]    DDX11L17-201 ENSG00000279928.2    DDX11L17         4
##   -------
##   regions: 196 ranges and 0 metadata columns
##   seqinfo: 1 sequence from GRCh38 genome

6 Session info

## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-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       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] BSgenome.Hsapiens.UCSC.hg38_1.4.5 BSgenome_1.72.0                  
##  [3] BiocIO_1.14.0                     transmogR_1.0.0                  
##  [5] extraChIPs_1.8.0                  tibble_3.2.1                     
##  [7] ggside_0.3.1                      ggplot2_3.5.1                    
##  [9] BiocParallel_1.38.0               rtracklayer_1.64.0               
## [11] VariantAnnotation_1.50.0          Rsamtools_2.20.0                 
## [13] Biostrings_2.72.0                 XVector_0.44.0                   
## [15] SummarizedExperiment_1.34.0       Biobase_2.64.0                   
## [17] GenomicRanges_1.56.0              GenomeInfoDb_1.40.0              
## [19] IRanges_2.38.0                    S4Vectors_0.42.0                 
## [21] MatrixGenerics_1.16.0             matrixStats_1.3.0                
## [23] BiocGenerics_0.50.0               BiocStyle_2.32.0                 
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-7               filelock_1.0.3            
##   [3] polyclip_1.10-6            XML_3.99-0.16.1           
##   [5] rpart_4.1.23               lifecycle_1.0.4           
##   [7] httr2_1.0.1                edgeR_4.2.0               
##   [9] lattice_0.22-6             ensembldb_2.28.0          
##  [11] MASS_7.3-60.2              backports_1.4.1           
##  [13] magrittr_2.0.3             limma_3.60.0              
##  [15] Hmisc_5.1-2                sass_0.4.9                
##  [17] rmarkdown_2.26             jquerylib_0.1.4           
##  [19] yaml_2.3.8                 metapod_1.12.0            
##  [21] Gviz_1.48.0                DBI_1.2.2                 
##  [23] RColorBrewer_1.1-3         abind_1.4-5               
##  [25] zlibbioc_1.50.0            purrr_1.0.2               
##  [27] AnnotationFilter_1.28.0    biovizBase_1.52.0         
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##  [31] tweenr_2.0.3               rappdirs_0.3.3            
##  [33] GenomeInfoDbData_1.2.12    ggrepel_0.9.5             
##  [35] codetools_0.2-20           DelayedArray_0.30.0       
##  [37] xml2_1.3.6                 ggforce_0.4.2             
##  [39] tidyselect_1.2.1           futile.logger_1.4.3       
##  [41] farver_2.1.1               UCSC.utils_1.0.0          
##  [43] ComplexUpset_1.3.3         BiocFileCache_2.12.0      
##  [45] base64enc_0.1-3            GenomicAlignments_1.40.0  
##  [47] jsonlite_1.8.8             Formula_1.2-5             
##  [49] tools_4.4.0                progress_1.2.3            
##  [51] Rcpp_1.0.12                glue_1.7.0                
##  [53] gridExtra_2.3              SparseArray_1.4.0         
##  [55] xfun_0.43                  dplyr_1.1.4               
##  [57] withr_3.0.0                formatR_1.14              
##  [59] BiocManager_1.30.22        fastmap_1.1.1             
##  [61] latticeExtra_0.6-30        fansi_1.0.6               
##  [63] digest_0.6.35              R6_2.5.1                  
##  [65] colorspace_2.1-0           jpeg_0.1-10               
##  [67] dichromat_2.0-0.1          biomaRt_2.60.0            
##  [69] RSQLite_2.3.6              utf8_1.2.4                
##  [71] tidyr_1.3.1                generics_0.1.3            
##  [73] data.table_1.15.4          prettyunits_1.2.0         
##  [75] InteractionSet_1.32.0      httr_1.4.7                
##  [77] htmlwidgets_1.6.4          S4Arrays_1.4.0            
##  [79] pkgconfig_2.0.3            gtable_0.3.5              
##  [81] blob_1.2.4                 htmltools_0.5.8.1         
##  [83] bookdown_0.39              ProtGenerics_1.36.0       
##  [85] scales_1.3.0               png_0.1-8                 
##  [87] knitr_1.46                 lambda.r_1.2.4            
##  [89] rstudioapi_0.16.0          rjson_0.2.21              
##  [91] checkmate_2.3.1            curl_5.2.1                
##  [93] cachem_1.0.8               stringr_1.5.1             
##  [95] parallel_4.4.0             foreign_0.8-86            
##  [97] AnnotationDbi_1.66.0       restfulr_0.0.15           
##  [99] pillar_1.9.0               grid_4.4.0                
## [101] vctrs_0.6.5                dbplyr_2.5.0              
## [103] cluster_2.1.6              htmlTable_2.4.2           
## [105] evaluate_0.23              VennDiagram_1.7.3         
## [107] GenomicFeatures_1.56.0     cli_3.6.2                 
## [109] locfit_1.5-9.9             compiler_4.4.0            
## [111] futile.options_1.0.1       rlang_1.1.3               
## [113] crayon_1.5.2               labeling_0.4.3            
## [115] interp_1.1-6               forcats_1.0.0             
## [117] stringi_1.8.3              deldir_2.0-4              
## [119] munsell_0.5.1              lazyeval_0.2.2            
## [121] csaw_1.38.0                Matrix_1.7-0              
## [123] hms_1.1.3                  patchwork_1.2.0           
## [125] bit64_4.0.5                KEGGREST_1.44.0           
## [127] statmod_1.5.0              highr_0.10                
## [129] igraph_2.0.3               broom_1.0.5               
## [131] memoise_2.0.1              bslib_0.7.0               
## [133] bit_4.0.5                  GenomicInteractions_1.38.0

References

Kaminow, Benjamin, Sara Ballouz, Jesse Gillis, and Alexander Dobin. 2022. “Pan-Human Consensus Genome Significantly Improves the Accuracy of RNA-seq Analyses.” Genome Res. 32 (4): 738–49.

Srivastava, Avi, Laraib Malik, Hirak Sarkar, Mohsen Zakeri, Fatemeh Almodaresi, Charlotte Soneson, Michael I Love, Carl Kingsford, and Rob Patro. 2020. “Alignment and Mapping Methodology Influence Transcript Abundance Estimation.” Genome Biol. 21 (1): 239.