1 About this template

This is the BLAST workflow template of the systemPipeRdata package, a companion package to systemPipeR (H Backman and Girke 2016). Like other workflow templates, it can be loaded with a single command. Users have the flexibility to utilize the template as is or modify it as needed. More in-depth information can be found in the main vignette of systemPipeRdata. The BLAST workflow template serves as a starting point for conducting sequence similarity search routines. It employs NCBI’s BLAST software as an illustrative example, enabling users to search a sequence database for entries that share sequence similarity to one or multiple query sequences. The search results can be presented in a concise tabular summary format, or the corresponding pairwise alignments can be included. To utilize this workflow, users must download and install the BLAST software from NCBI’s website here and ensure it is added to their system’s PATH environment variable.

The following data analysis steps are included in this workflow template:

  1. Validation of the BLAST installation
  2. Creation of an indexed sequence database that can be searched with BLAST
  3. BLAST search of indexed database with query sequence

The topology graph of the BLAST workflow is shown in Figure 1.

Topology graph of BLAST workflow.

Figure 1: Topology graph of BLAST workflow

2 Workflow environment

The environment of the chosen workflow is generated with the genWorenvir function. After this, the user’s R session needs to be directed into the resulting directory (here SPblast).

systemPipeRdata::genWorkenvir(workflow = "SPblast", mydirname = "SPblast")
setwd("SPblast")

The SPRproject function initializes a new workflow project instance. This function call creates an empty SAL workflow container and at the same time a linked project log directory (default name .SPRproject) that acts as a flat-file database of a workflow. For additional details, please visit this section in systemPipeR's main vignette.

library(systemPipeR)
sal <- SPRproject()
sal

The importWF function allows to import all the workflow steps outlined in the source Rmd file of this vignette into a SAL (SYSargsList) workflow container. Once imported, the entire workflow can be executed from start to finish using the runWF function. More details regarding this process are provided in the following section here.

sal <- importWF(sal, "SPblast.Rmd")
sal <- runWF(sal)

2.1 Step 1: Load packages

Next, the systemPipeR package needs to be loaded in a workflow.

appendStep(sal) <- LineWise(code = {
    library(systemPipeR)
}, step_name = "load_packages")

2.2 Step 2: Test BLAST install

The following step is optional. It tests the availability of the BLAST software on the user’s system.

appendStep(sal) <- LineWise(code = {
    # If you have a modular system, then enable the
    # following line moduleload('ncbi-blast')
    blast_check <- tryCMD("blastn", silent = TRUE)
    if (blast_check == "error")
        stop("Check your BLAST installation path.")
}, step_name = "test_blast", dependency = "load_packages")

2.3 Step 3: BLASTable database

This step creates an indexed sequence database that can be searched with BLAST. The sample sequences used for creating the databases are stored in a file named tair10.fasta under the data directory of the workflow environment. The exact command-line (CL) call used for creating the indexed database can be returned with cmdlist(sal, step=3).

appendStep(sal) <- SYSargsList(step_name = "build_genome_db",
    dir = FALSE, targets = NULL, wf_file = "blast/makeblastdb.cwl",
    input_file = "blast/makeblastdb.yml", dir_path = "param/cwl",
    dependency = "test_blast")

2.5 Step 5: View top hits

This step displays the top hits identified by the BLAST search in the previous step. The e_value and bit_score columns allow to rank the BLAST results by sequence similarity.

appendStep(sal) <- LineWise(code = {
    # get the output file path from a Sysargs step using
    # `getColumn`
    tbl_tair10 <- read.delim(getColumn(sal, step = "blast_genome")[1],
        header = FALSE, stringsAsFactors = FALSE)
    names(tbl_tair10) <- c("query", "subject", "identity", "alignment_length",
        "mismatches", "gap_openings", "q_start", "q_end", "s_start",
        "s_end", "e_value", "bit_score")
    print(head(tbl_tair10, n = 20))
}, step_name = "display_hits", dependency = "blast_genome")

2.6 Version Information

appendStep(sal) <- LineWise(code = {
    sessionInfo()
}, step_name = "wf_session", dependency = "display_hits")

3 Automated routine

Once the above workflow steps have been loaded into sal from the source Rmd file of this vignette, the workflow can be executed from start to finish (or partially) with the runWF command. Subsequently, scientific and technical workflow reports can be generated with the renderReport and renderLogs functions, respectively.

Note: To demonstrate ‘systemPipeR’s’ automation routines without regenerating a new workflow environment from scratch, the first line below uses the overwrite=TRUE option of the SPRproject function. This option is generally discouraged as it erases the existing workflow project and sal container. For information on resuming and restarting workflow runs, users want to consult the relevant section of the main vignette (see here.)

sal <- SPRproject(overwrite = TRUE)  # Avoid 'overwrite=TRUE' in real runs.
sal <- importWF(sal, file_path = "SPblast.Rmd")  # Imports above steps from new.Rmd.
sal <- runWF(sal)  # Runs ggworkflow.
plotWF(sal)  # Plot toplogy graph of workflow
sal <- renderReport(sal)  # Renders scientific report.
sal <- renderLogs(sal)  # Renders technical report from log files.

3.1 CL tools used

The listCmdTools (and listCmdModules) return the CL tools that are used by a workflow. To include a CL tool list in a workflow report, one can use the following code. Additional details on this topic can be found in the main vignette here.

if (file.exists(file.path(".SPRproject", "SYSargsList.yml"))) {
    local({
        sal <- systemPipeR::SPRproject(resume = TRUE)
        systemPipeR::listCmdTools(sal)
        systemPipeR::listCmdModules(sal)
    })
} else {
    cat(crayon::blue$bold("Tools and modules required by this workflow are:\n"))
    cat(c("BLAST 2.16.0+"), sep = "\n")
}
## Tools and modules required by this workflow are:
## BLAST 2.16.0+

3.2 Session Info

This is the session information for rendering this R Markdown report. To access the session information for the workflow run, generate the technical HTML report with renderLogs.

sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
## 
## 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] stats     graphics  grDevices utils     datasets 
## [6] methods   base     
## 
## other attached packages:
## [1] BiocStyle_2.33.1
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.37       R6_2.5.1           
##  [3] codetools_0.2-20    bookdown_0.40      
##  [5] fastmap_1.2.0       xfun_0.47          
##  [7] cachem_1.1.0        knitr_1.48         
##  [9] htmltools_0.5.8.1   rmarkdown_2.28     
## [11] lifecycle_1.0.4     cli_3.6.3          
## [13] sass_0.4.9          jquerylib_0.1.4    
## [15] compiler_4.4.1      highr_0.11         
## [17] tools_4.4.1         evaluate_1.0.0     
## [19] bslib_0.8.0         yaml_2.3.10        
## [21] formatR_1.14        BiocManager_1.30.25
## [23] crayon_1.5.3        jsonlite_1.8.9     
## [25] rlang_1.1.4

H Backman, Tyler W, and Thomas Girke. 2016. “systemPipeR: NGS workflow and report generation environment.” BMC Bioinformatics 17 (1): 388. https://doi.org/10.1186/s12859-016-1241-0.