1 Installation

Install the package from Bioconductor.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("RcwlPipelines")

The development version is also available to download from Github.

BiocManager::install("hubentu/RcwlPipelines")

Load the package into the R session.

library(RcwlPipelines)

2 Tools and pipelines scripts

The R scripts to build the CWL tools and pipelines based on the Rcwl package are stored in the “tools” and “pipelines” folder respectively. The function cwlTools can be used to collect the available scripts. The cachePath can be your existing cache directory or a new folder.

tools <- cwlTools(cachePath = tempdir())
tools
#> class: BiocFileCache
#> bfccache: /tmp/RtmpXjWGwT
#> bfccount: 36
#> For more information see: bfcinfo() or bfcquery()

The full paths can be pulled from the “fpath” column.

library(dplyr)
bfcinfo(tools) %>% select(rname, fpath)
#> # A tibble: 36 x 2
#>    rname        fpath                                                        
#>    <chr>        <chr>                                                        
#>  1 HTseq        /tmp/RtmpjUH337/Rinst6cd370baecf1/RcwlPipelines/tools/HTseq.R
#>  2 STAR         /tmp/RtmpjUH337/Rinst6cd370baecf1/RcwlPipelines/tools/STAR.R 
#>  3 blastn       /tmp/RtmpjUH337/Rinst6cd370baecf1/RcwlPipelines/tools/blastn…
#>  4 bowtie2_ali… /tmp/RtmpjUH337/Rinst6cd370baecf1/RcwlPipelines/tools/bowtie…
#>  5 bowtie2_bui… /tmp/RtmpjUH337/Rinst6cd370baecf1/RcwlPipelines/tools/bowtie…
#>  6 bwa          /tmp/RtmpjUH337/Rinst6cd370baecf1/RcwlPipelines/tools/bwa.R  
#>  7 bwa_index    /tmp/RtmpjUH337/Rinst6cd370baecf1/RcwlPipelines/tools/bwa_in…
#>  8 cutadapt     /tmp/RtmpjUH337/Rinst6cd370baecf1/RcwlPipelines/tools/cutada…
#>  9 fastQC       /tmp/RtmpjUH337/Rinst6cd370baecf1/RcwlPipelines/tools/fastQC…
#> 10 featureCoun… /tmp/RtmpjUH337/Rinst6cd370baecf1/RcwlPipelines/tools/featur…
#> # … with 26 more rows

3 Build a pipeline

We can develop a pipline by utilizing the available tools. For example, a simple alignment pipelines with mapping and marking duplicates can be built from the tools.

First, we load the required tools, bwa, samtools and picard markduplicates.

scripts <- bfcinfo(tools) %>%
    filter(rname %in% c("bwa",
                        "samtools_samTobam",
                        "samtools_sortBam",
                        "samtools_index",
                        "markdup")) %>%
    pull(rpath)
invisible(sapply(scripts, source))

Next, we define the input parameters.

p1 <- InputParam(id = "threads", type = "int")
p2 <- InputParam(id = "RG", type = "string")
p3 <- InputParam(id = "Ref", type = "string")
p4 <- InputParam(id = "FQ1", type = "File")
p5 <- InputParam(id = "FQ2", type = "File?")

Then we define the pipeline steps, from raw fastqs to duplicates marked alignments.

## bwa
s1 <- Step(id = "bwa", run = bwa,
           In = list(threads = "threads",
                     RG = "RG",
                     Ref = "Ref",
                     FQ1 = "FQ1",
                     FQ2 = "FQ2"))
## sam to bam
s2 <- Step(id = "sam2bam", run = sam2bam,
           In = list(sam = "bwa/sam"))
## sort bam
s3 <- Step(id = "sortBam", run = sortBam,
           In = list(bam = "sam2bam/bam"))
## mark duplicates
s4 <- Step(id = "markdup", run = markdup,
           In = list(ibam = "sortBam/sbam",
                     obam = list(
                         valueFrom="$(inputs.ibam.nameroot).mdup.bam"),
                     matrix = list(
                         valueFrom="$(inputs.ibam.nameroot).markdup.txt")))
## index bam
s5 <- Step(id = "idxBam", run = samtools_index,
           In = list(bam = "markdup/mBam"))

Last, we define the outputs and connect the steps to a new pipeline.

req1 <- list(class = "StepInputExpressionRequirement")
req2 <- list(class = "InlineJavascriptRequirement")
## outputs
o1 <- OutputParam(id = "Bam", type = "File", outputSource = "markdup/mBam")
o2 <- OutputParam(id = "Idx", type = "File", outputSource = "idxBam/idx")
## stepParam
Align <- cwlStepParam(requirements = list(req1, req2),
                      inputs = InputParamList(p1, p2, p3, p4, p5),
                      outputs = OutputParamList(o1, o2))
## build pipeline
Align <- Align + s1 + s2 + s3 + s4 + s5

The pipeline is ready for use. We can plot the pipeline with plotCWL from the Rcwl package.

plotCWL(Align)

4 Pipelines summary

There are mainly 4 pipelines are collected in this package. Here is a brief introduction to these pipelines. More pipelines and tools are expected to be included in the future.

4.1 DNASeq alignment pipeline

The pipeline can be used to preprocess DNA sequences in fastq format. It can take paired fastqs, read groups from multiple batches as input.

data(alignMerge)
inputs(alignMerge)
#> List of length 6
#> names(6): idBam RG threads Ref FQ1s FQ2s

The pipeline includes two steps and several jobs will be run in each step.

bwaAlign: bwa alignment by read groups.

runs(runs(alignMerge)[[1]])
#> List of length 4
#> names(4): bwa sam2bam sortBam idxBam

bwa: To align fastqs and read groups to reference genome with bwa.
sam2bam: To convert the alignments in “sam” format to “bam” format with samtools.
sortBam: To sort the “bam” file by coordinates with samtools.
idxBam: To index “bam” file with samtools.

mergeBamDup: Merge by samples and markduplicates.

runs(runs(alignMerge)[[2]])
#> List of length 4
#> names(4): mergeBam markdup samtools_index samtools_flagstat

mergeBam: To merge bam files from multiple batches with picard.
markdup: To mark duplicates with picard.
samtools_index: To index bam file with samtools.
samtools_flagstat: To summarize flags in bam with samtools.

The final bam files after duplicates marked, bam index, duplicates matrix, and flag statistics summary will be in the output folder.

outputs(alignMerge)
#> List of length 4
#> names(4): oBam matrix Idx stat

Here you can find an example to run the pipeline.

https://hubentu.github.io/others/Rcwl/application.html#dnaseq-alignment-pipeline

4.2 RNASeq pipeline

The pipeline was built with reads quality summary, STAR alignment, quantification by featureCounts and RSeQC quality control. Here are the inputs.

data(rnaseq_Sf)
inputs(rnaseq_Sf)
#> List of length 5
#> names(5): in_seqfiles in_prefix in_genomeDir in_GTFfile in_runThreadN

The pipeline includes 6 steps.

fastqc: To run quality summary for raw fastqs with fastqc.
STAR: To align fastqs with STAR.
samtools_index: To index aligned bam file.
samtools_flagstat: To summary alignment flags.
featureCounts: To quantify gene abundances.
RSeQC: Several steps included.
- gtfToGenePred: To convert GTF annotation to “genePred” format.
- genePredToBed: To convert “genePred” annotation to “bed” format.
- r_distribution: To run reads distribution over genome features.
- gCoverage: To summarize read coverage over gene body.

The outputs and logs from alignment, quantification and QC steps are collected together to the output folder. A final QC report could be generated by multiqc, which is also available in the data package.

An example to run the pipeline.

https://hubentu.github.io/others/Rcwl/application.html#rnaseq-pipeline

4.3 MC3 somatic variant calling pipeline

The Multi-Center Mutation Calling in Multiple Cancers project (MC3) pipeline was developed by the TCGA group, which was implemented with CWL and available at https://github.com/OpenGenomics/mc3. Two major steps, variant calling and merging VCFs to MAF, was imported to the mc3 pipeline in this package.

The steps of the pipeline was built on the CWL files from its github repository, which were also contained in the package. Thereforce, we need the load the pipleine by sourcing it from the script.

The pipeline requires a paired of tumor/normal BAM files and genomic annotation files as inputs.

bfcinfo(tools) %>% filter(rname == "mc3") %>% pull(rpath) %>% source
inputs(mc3)
#> List of length 10
#> names(10): tumorID normalID tumor normal ... cosmic dbsnp refFasta vepData

Two steps are included.

call_variants: To call variants by 7 pipelines.
covert: To merge VCFs and convert to MAF

The merged VCF and converted MAF files will be collected to the output folder.

outputs(mc3)
#> List of length 2
#> names(2): outmaf outvcf

Here is an examples to run the pipeline.
https://hubentu.github.io/others/Rcwl/application.html#mc3-somatic-variant-calling-pipeline

4.4 GATK4 germline variant calling pipeline

The GATK4 best practice pipeline for germline variant calling was implemented with Workflow Description Language (WDL). We wrapped the WDL pipeline into 3 steps with Rcwl. The details of the pipeline can be find here: https://software.broadinstitute.org/gatk/best-practices/workflow?id=11145

GAlign GATK alignment.

The fastqs, sample information and customized json files for WDL are required as inputs. Multiple steps will run in this step, including bwa alignment, mark duplicates and base quality recalibration. GATK ready BAM files will be collected to the output directory.

hapCall HaplotypeCaller.

The GATK ready BAM and customized json files are inputs in this step. The local paths of GATK bundle files are required to be modified in your json file. A “gVCF” files will be generated.

jdCall Joint variant discovery

This step will combine the “gVCF” files and then call germline variants in all samples. The paths of the local bundle files are also required to be changed in the json template file. The final VCF file of germline variants will be collected.

An example to run the pipeline.
https://hubentu.github.io/others/Rcwl/application.html#gatk4-germline-variant-calling-pipeline

5 SessionInfo

sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.2 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        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] parallel  stats4    stats     graphics  grDevices utils     datasets 
#> [8] methods   base     
#> 
#> other attached packages:
#> [1] dplyr_0.8.1         RcwlPipelines_1.0.2 BiocFileCache_1.8.0
#> [4] dbplyr_1.4.0        Rcwl_1.0.2          S4Vectors_0.22.0   
#> [7] BiocGenerics_0.30.0 yaml_2.2.0          BiocStyle_2.12.0   
#> 
#> loaded via a namespace (and not attached):
#>  [1] httr_1.4.0          viridis_0.5.1       tidyr_0.8.3        
#>  [4] bit64_0.9-7         jsonlite_1.6        viridisLite_0.3.0  
#>  [7] shiny_1.3.2         assertthat_0.2.1    BiocManager_1.30.4 
#> [10] blob_1.1.1          base64url_1.4       progress_1.2.2     
#> [13] pillar_1.4.0        RSQLite_2.1.1       backports_1.1.4    
#> [16] glue_1.3.1          downloader_0.4      digest_0.6.19      
#> [19] RColorBrewer_1.1-2  promises_1.0.1      checkmate_1.9.3    
#> [22] colorspace_1.4-1    htmltools_0.3.6     httpuv_1.5.1       
#> [25] plyr_1.8.4          XML_3.98-1.19       pkgconfig_2.0.2    
#> [28] bookdown_0.10       DiagrammeR_1.0.1    purrr_0.3.2        
#> [31] xtable_1.8-4        scales_1.0.0        brew_1.0-6         
#> [34] later_0.8.0         BiocParallel_1.18.0 tibble_2.1.1       
#> [37] ggplot2_3.1.1       influenceR_0.1.0    withr_2.1.2        
#> [40] lazyeval_0.2.2      cli_1.1.0           rgexf_0.15.3       
#> [43] magrittr_1.5        crayon_1.3.4        mime_0.6           
#> [46] memoise_1.1.0       evaluate_0.13       fansi_0.4.0        
#> [49] Rook_1.1-1          tools_3.6.0         data.table_1.12.2  
#> [52] prettyunits_1.0.2   hms_0.4.2           stringr_1.4.0      
#> [55] munsell_0.5.0       compiler_3.6.0      rlang_0.3.4        
#> [58] grid_3.6.0          rstudioapi_0.10     rappdirs_0.3.1     
#> [61] htmlwidgets_1.3     visNetwork_2.0.6    igraph_1.2.4.1     
#> [64] rmarkdown_1.12      gtable_0.3.0        curl_3.3           
#> [67] DBI_1.0.0           R6_2.4.0            gridExtra_2.3      
#> [70] knitr_1.23          zeallot_0.1.0       utf8_1.1.4         
#> [73] bit_1.1-14          readr_1.3.1         stringi_1.4.3      
#> [76] Rcpp_1.0.1          vctrs_0.1.0         batchtools_0.9.11  
#> [79] tidyselect_0.2.5    xfun_0.7

Rcwl Pipelines

2019-05-22