Loading SpliceWiz

For instructions on installing and configuring SpliceWiz, please see the Quick-Start vignette.

library(SpliceWiz)
#> Loading required package: NxtIRFdata
#> SpliceWiz package loaded with 2 threads
#> Use setSWthreads() to set the number of SpliceWiz threads


Reference Generation

First, define the path to the directory in which the reference should be stored. This directory will be made by SpliceWiz, but its parent directory must exist, otherwise an error will be returned.

ref_path <- "./Reference"


Create a SpliceWiz reference from user-defined FASTA and GTF files locally

Note that setting genome_path = "hg38" will prompt SpliceWiz to use the default files for nonPolyA and Mappability exclusion references in the generation of its reference. Valid options for genome_path are “hg38”, “hg19”, “mm10” and “mm9”.

buildRef(
    reference_path = ref_path,
    fasta = "genome.fa", gtf = "transcripts.gtf",
    genome_type = "hg38"
)


Prepare genome resources and building the reference as separate steps

buildRef() first fetches the genome and gene annotations and makes a compressed local copy in the resources subdirectory of the given reference path. This can sometimes be a long process (especially when downloading the genome from the internet). Also, one may choose to generate the mappability exclusion regions manually (see Reference Generation - Mappability exclusion generation using Rsubread section). This needs to occur prior to generation of the SpliceWiz reference.

To separately prepare the annotation data and build the SpliceWiz reference, use the getResources() function to specify the FASTA and GTF files. Once getResources() has completed successfully, call buildRef() and leave the fasta and gtf arguments blank, as in the example below:

getResources(
    reference_path = ref_path,
    fasta = "genome.fa",
    gtf = "transcripts.gtf"
)

buildRef(
    reference_path = ref_path,
    genome_type = "hg38"
)


Overwriting an existing reference, but using the same annotations

To re-build and overwrite an existing reference, using the same resource annotations:

# Assuming hg38 genome:

buildRef(
    reference_path = ref_path,
    genome_type = "hg38",
    overwrite = TRUE
)


Create a SpliceWiz reference using web resources from Ensembl’s FTP

The following will first download the genome and gene annotation files from the online resource and store a local copy of it in a file cache, facilitated by BiocFileCache. Then, it uses the downloaded resource to create the SpliceWiz reference.

FTP <- "ftp://ftp.ensembl.org/pub/release-94/"

buildRef(
    reference_path = ref_path,
    fasta = paste0(FTP, "fasta/homo_sapiens/dna/",
        "Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz"), 
    gtf = paste0(FTP, "gtf/homo_sapiens/",
        "Homo_sapiens.GRCh38.94.chr.gtf.gz"),
    genome_type = "hg38"
)


Create a SpliceWiz reference using AnnotationHub resources

AnnotationHub contains Ensembl references for many genomes. To browse what is available:

require(AnnotationHub)
#> Loading required package: AnnotationHub
#> Loading required package: BiocGenerics
#> 
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#>     pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
#>     tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: BiocFileCache
#> Loading required package: dbplyr

ah <- AnnotationHub()
#> snapshotDate(): 2022-10-26
query(ah, "Ensembl")
#> AnnotationHub with 33426 records
#> # snapshotDate(): 2022-10-26
#> # $dataprovider: Ensembl, FANTOM5,DLRP,IUPHAR,HPRD,STRING,SWISSPROT,TREMBL,E...
#> # $species: Homo sapiens, Mus musculus, Danio rerio, Rattus norvegicus, Pan ...
#> # $rdataclass: TwoBitFile, GRanges, EnsDb, SQLiteFile, data.frame, OrgDb, list
#> # additional mcols(): taxonomyid, genome, description,
#> #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> #   rdatapath, sourceurl, sourcetype 
#> # retrieve records with, e.g., 'object[["AH5046"]]' 
#> 
#>              title                                                       
#>   AH5046   | Ensembl Genes                                               
#>   AH5160   | Ensembl Genes                                               
#>   AH5311   | Ensembl Genes                                               
#>   AH5434   | Ensembl Genes                                               
#>   AH5435   | Ensembl EST Genes                                           
#>   ...        ...                                                         
#>   AH107263 | LRBaseDb for Taeniopygia guttata (Zebra finch, v004)        
#>   AH107264 | LRBaseDb for Takifugu rubripes (Fugu, v004)                 
#>   AH107265 | LRBaseDb for Ursus maritimus (Polar bear, v004)             
#>   AH107266 | LRBaseDb for Vulpes vulpes (Red fox, v004)                  
#>   AH107267 | LRBaseDb for Xenopus tropicalis (Tropical clawed frog, v004)

For a more specific query:

query(ah, c("Homo Sapiens", "release-94"))
#> AnnotationHub with 9 records
#> # snapshotDate(): 2022-10-26
#> # $dataprovider: Ensembl
#> # $species: Homo sapiens
#> # $rdataclass: TwoBitFile, GRanges
#> # additional mcols(): taxonomyid, genome, description,
#> #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> #   rdatapath, sourceurl, sourcetype 
#> # retrieve records with, e.g., 'object[["AH64628"]]' 
#> 
#>             title                                           
#>   AH64628 | Homo_sapiens.GRCh38.94.abinitio.gtf             
#>   AH64629 | Homo_sapiens.GRCh38.94.chr.gtf                  
#>   AH64630 | Homo_sapiens.GRCh38.94.chr_patch_hapl_scaff.gtf 
#>   AH64631 | Homo_sapiens.GRCh38.94.gtf                      
#>   AH65744 | Homo_sapiens.GRCh38.cdna.all.2bit               
#>   AH65745 | Homo_sapiens.GRCh38.dna.primary_assembly.2bit   
#>   AH65746 | Homo_sapiens.GRCh38.dna_rm.primary_assembly.2bit
#>   AH65747 | Homo_sapiens.GRCh38.dna_sm.primary_assembly.2bit
#>   AH65748 | Homo_sapiens.GRCh38.ncrna.2bit

We wish to fetch “AH65745” and “AH64631” which contains the desired FASTA and GTF files, respectively. To build a reference using these resources:

buildRef(
    reference_path = ref_path,
    fasta = "AH65745",
    gtf = "AH64631",
    genome_type = "hg38"
)

Build-Reference-methods will recognise the inputs of fasta and gtf as AnnotationHub resources as they begin with “AH”.

Create a SpliceWiz reference from species other than human or mouse

For human and mouse genomes, we highly recommend specifying genome_type as the default mappability file is used to exclude intronic regions with repeat sequences from intron retention analysis. For other species, one could generate a SpliceWiz reference without this reference:

buildRef(
    reference_path = ref_path,
    fasta = "genome.fa", gtf = "transcripts.gtf",
    genome_type = ""
)


STAR reference generation

Checking if STAR is installed

To use STAR to align FASTQ files, one must be using a system with STAR installed. This software is not available in Windows. To check if STAR is available:

STAR_version()
#> Nov 09 18:08:35 STAR is not installed


Building a STAR reference

ref_path = "./Reference"

# Ensure genome resources are prepared from genome FASTA and GTF file:

if(!dir.exists(file.path(ref_path, "resource"))) {
    getResources(
        reference_path = ref_path,
        fasta = "genome.fa",
        gtf = "transcripts.gtf"
    )
}

# Generate a STAR genome reference:
STAR_BuildRef(
    reference_path = ref_path,
    n_threads = 8
)


Building a STAR reference alongside the SpliceWiz reference

If STAR is available on the same computer or server where R/RStudio is being run, we can use the one-line function buildFullRef. This function will: * Prepare the resources from the given FASTA and GTF files * Generate a STAR genome * Use the STAR genome and the FASTA file to de-novo calculate and define low mappability regions * Build the SpliceWiz reference using the genome resources and mappability file

buildFullRef(
    reference_path = ref_path,
    fasta = "genome.fa", gtf = "transcripts.gtf",
    genome_type = "",
    use_STAR_mappability = TRUE,
    n_threads = 4
)

n_threads specify how many threads should be used to build the STAR reference and to calculate the low mappability regions

Mappability exclusion generation using Rsubread

Finally, if STAR is not available, Rsubread is available on Bioconductor to perform mappability calculations. The example code in the manual is displayed here for convenience, to demonstrate how this would be done:

# (1a) Creates genome resource files 

ref_path <- file.path(tempdir(), "Reference")

getResources(
    reference_path = ref_path,
    fasta = chrZ_genome(),
    gtf = chrZ_gtf()
)

# (1b) Systematically generate reads based on the SpliceWiz example genome:

generateSyntheticReads(
    reference_path = ref_path
)

# (2) Align the generated reads using Rsubread:

# (2a) Build the Rsubread genome index:

setwd(ref_path)
Rsubread::buildindex(basename = "./reference_index", 
    reference = chrZ_genome())

# (2b) Align the synthetic reads using Rsubread::subjunc()

Rsubread::subjunc(
    index = "./reference_index", 
    readfile1 = file.path(ref_path, "Mappability", "Reads.fa"), 
    output_file = file.path(ref_path, "Mappability", "AlignedReads.bam"), 
    useAnnotation = TRUE, 
    annot.ext = chrZ_gtf(), 
    isGTF = TRUE
)

# (3) Analyse the aligned reads in the BAM file for low-mappability regions:

calculateMappability(
    reference_path = ref_path,
    aligned_bam = file.path(ref_path, "Mappability", "AlignedReads.bam")
)

# (4) Build the SpliceWiz reference using the calculated Mappability Exclusions

buildRef(ref_path)      


Aligning Raw RNA-seq data using SpliceWiz’s STAR wrappers


First, remember to check that STAR is available via command line:

STAR_version()
#> Nov 09 18:08:35 STAR is not installed

Aligning a single sample using STAR

STAR_alignReads(
    STAR_ref_path = file.path(ref_path, "STAR"),
    BAM_output_path = "./bams/sample1",
    fastq_1 = "sample1_1.fastq", fastq_2 = "sample1_2.fastq",
    n_threads = 8
)


Aligning multiple samples using STAR

Experiment <- data.frame(
    sample = c("sample_A", "sample_B"),
    forward = file.path("raw_data", c("sample_A", "sample_B"),
        c("sample_A_1.fastq", "sample_B_1.fastq")),
    reverse = file.path("raw_data", c("sample_A", "sample_B"),
        c("sample_A_2.fastq", "sample_B_2.fastq"))
)

STAR_alignExperiment(
    Experiment = Experiment,
    STAR_ref_path = file.path("Reference_FTP", "STAR"),
    BAM_output_path = "./bams",
    n_threads = 8,
    two_pass = FALSE
)

To use two-pass mapping, set two_pass = TRUE. We recommend disabling this feature, as one-pass mapping is adequate in typical-use cases.

Finding FASTQ files recursively from a given directory

SpliceWiz can identify sequencing FASTQ files recursively from a given directory. It assumes that forward and reverse reads are suffixed as _1 and _2, respectively. Users can choose to identify such files using a specified file extension. For example, to recursively identify FASTQ files of the format {sample}_1.fq.gz and {sample}_2.fq.gz, use the following:

# Assuming sequencing files are named by their respective sample names
fastq_files <- findFASTQ("./sequencing_files", paired = TRUE,
    fastq_suffix = ".fq.gz", level = 0)

Then, these can be aligned as follows:

STAR_alignExperiment(
    Experiment = fastq_files,
    STAR_ref_path = file.path("Reference_FTP", "STAR"),
    BAM_output_path = "./bams",
    n_threads = 8,
    two_pass = FALSE
)

Running processBAM on BAM files

To conveniently find all BAM files recursively in a given path:

bams <- findBAMS("./bams", level = 1)

This convenience function returns the putative sample names, either from BAM file names themselves (level = 0), or from the names of their parent directories (level = 1).

To run processBAM() using 4 OpenMP threads:

# assume SpliceWiz reference has been generated in `ref_path`

processBAM(
    bamfiles = bams$path,
    sample_names = bams$sample,
    reference_path = ref_path,
    output_path = "./pb_output",
    n_threads = 4,
    useOpenMP = TRUE
)


Creating COV files from BAM files without running processBAM

Sometimes one may wish to create a COV file from a BAM file without running processBAM(). One reason might be because a SpliceWiz reference is not available.

To convert a list of BAM files, run BAM2COV(). This is a function structurally similar to processBAM() but without the need to give the path to the SpliceWiz reference:

BAM2COV(
    bamfiles = bams$path,
    sample_names = bams$sample,
    output_path = "./pb_output",
    n_threads = 4,
    useOpenMP = TRUE
)


Collating the experiment

Assuming the SpliceWiz reference is in ref_path, after running processBAM() as shown in the previous section, use the convenience function findSpliceWizOutput() to tabulate a list of samples and their corresponding processBAM() outputs:

expr <- findSpliceWizOutput("./pb_output")

This data.frame can be directly used to run collateData:

collateData(
    Experiment = expr,
    reference_path = ref_path,
    output_path = "./NxtSE_output"
)
  • NB: Novel splicing detection can be enabled by setting novelSplicing = TRUE. See the SpliceWiz Quick-Start vignette for more details.

Then, the collated data can be imported as a NxtSE object, which is an object that inherits SummarizedExperiment and has specialized containers to hold additional data required by SpliceWiz.

se <- makeSE("./NxtSE_output")


Downstream analysis using SpliceWiz

Please refer to SpliceWiz: Quick-Start vignette for worked examples using the example dataset.

SessionInfo

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] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] AnnotationHub_3.7.0 BiocFileCache_2.7.0 dbplyr_2.2.1       
#> [4] BiocGenerics_0.45.0 SpliceWiz_1.1.2     NxtIRFdata_1.5.0   
#> 
#> loaded via a namespace (and not attached):
#>   [1] RColorBrewer_1.1-3            jsonlite_1.8.3               
#>   [3] magrittr_2.0.3                rmarkdown_2.18               
#>   [5] fs_1.5.2                      BiocIO_1.9.0                 
#>   [7] zlibbioc_1.45.0               vctrs_0.5.0                  
#>   [9] memoise_2.0.1                 Rsamtools_2.15.0             
#>  [11] DelayedMatrixStats_1.21.0     RCurl_1.98-1.9               
#>  [13] webshot_0.5.4                 progress_1.2.2               
#>  [15] htmltools_0.5.3               curl_4.3.3                   
#>  [17] Rhdf5lib_1.21.0               rhdf5_2.43.0                 
#>  [19] shinyFiles_0.9.3              sass_0.4.2                   
#>  [21] bslib_0.4.1                   htmlwidgets_1.5.4            
#>  [23] plotly_4.10.1                 cachem_1.0.6                 
#>  [25] GenomicAlignments_1.35.0      iterators_1.0.14             
#>  [27] mime_0.12                     lifecycle_1.0.3              
#>  [29] pkgconfig_2.0.3               Matrix_1.5-1                 
#>  [31] R6_2.5.1                      fastmap_1.1.0                
#>  [33] GenomeInfoDbData_1.2.9        MatrixGenerics_1.11.0        
#>  [35] shiny_1.7.3                   digest_0.6.30                
#>  [37] colorspace_2.0-3              AnnotationDbi_1.61.0         
#>  [39] S4Vectors_0.37.0              GenomicRanges_1.51.1         
#>  [41] RSQLite_2.2.18                seriation_1.4.0              
#>  [43] filelock_1.0.2                fansi_1.0.3                  
#>  [45] httr_1.4.4                    compiler_4.3.0               
#>  [47] withr_2.5.0                   bit64_4.0.5                  
#>  [49] BiocParallel_1.33.4           viridis_0.6.2                
#>  [51] DBI_1.1.3                     heatmaply_1.4.0              
#>  [53] dendextend_1.16.0             HDF5Array_1.27.0             
#>  [55] R.utils_2.12.1                rappdirs_0.3.3               
#>  [57] DelayedArray_0.25.0           rjson_0.2.21                 
#>  [59] tools_4.3.0                   interactiveDisplayBase_1.37.0
#>  [61] httpuv_1.6.6                  fst_0.9.8                    
#>  [63] R.oo_1.25.0                   glue_1.6.2                   
#>  [65] restfulr_0.0.15               rhdf5filters_1.11.0          
#>  [67] promises_1.2.0.1              grid_4.3.0                   
#>  [69] generics_0.1.3                gtable_0.3.1                 
#>  [71] BSgenome_1.67.1               ca_0.71.1                    
#>  [73] R.methodsS3_1.8.2             tidyr_1.2.1                  
#>  [75] hms_1.1.2                     data.table_1.14.4            
#>  [77] utf8_1.2.2                    XVector_0.39.0               
#>  [79] foreach_1.5.2                 BiocVersion_3.17.1           
#>  [81] pillar_1.8.1                  stringr_1.4.1                
#>  [83] genefilter_1.81.0             later_1.3.0                  
#>  [85] splines_4.3.0                 dplyr_1.0.10                 
#>  [87] lattice_0.20-45               ompBAM_1.3.0                 
#>  [89] survival_3.4-0                rtracklayer_1.59.0           
#>  [91] bit_4.0.4                     annotate_1.77.0              
#>  [93] tidyselect_1.2.0              registry_0.5-1               
#>  [95] Biostrings_2.67.0             knitr_1.40                   
#>  [97] rhandsontable_0.3.8           gridExtra_2.3                
#>  [99] IRanges_2.33.0                SummarizedExperiment_1.29.1  
#> [101] stats4_4.3.0                  xfun_0.34                    
#> [103] shinydashboard_0.7.2          Biobase_2.59.0               
#> [105] matrixStats_0.62.0            pheatmap_1.0.12              
#> [107] DT_0.26                       stringi_1.7.8                
#> [109] lazyeval_0.2.2                yaml_2.3.6                   
#> [111] shinyWidgets_0.7.4            evaluate_0.18                
#> [113] codetools_0.2-18              tibble_3.1.8                 
#> [115] BiocManager_1.30.19           cli_3.4.1                    
#> [117] xtable_1.8-4                  munsell_0.5.0                
#> [119] jquerylib_0.1.4               Rcpp_1.0.9                   
#> [121] GenomeInfoDb_1.35.2           png_0.1-7                    
#> [123] XML_3.99-0.12                 parallel_4.3.0               
#> [125] fstcore_0.9.12                ellipsis_0.3.2               
#> [127] ggplot2_3.4.0                 assertthat_0.2.1             
#> [129] blob_1.2.3                    prettyunits_1.1.1            
#> [131] sparseMatrixStats_1.11.0      bitops_1.0-7                 
#> [133] viridisLite_0.4.1             scales_1.2.1                 
#> [135] purrr_0.3.5                   crayon_1.5.2                 
#> [137] rlang_1.0.6                   TSP_1.2-1                    
#> [139] KEGGREST_1.39.0