systemPipeR: Workflow design and reporting generation environment

In systemPipeR: systemPipeR: NGS workflow and report generation environment

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```r
BiocStyle::markdown()
options(width=80, max.print=1000)
knitr::opts_chunk$set(
    eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")),
    cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE")), 
    tidy.opts=list(width.cutoff=80), tidy=TRUE)

suppressPackageStartupMessages({
    library(systemPipeR)
    library(BiocParallel)
    library(Biostrings)
    library(Rsamtools)
    library(GenomicRanges)
    library(ggplot2)
    library(GenomicAlignments)
    library(ShortRead)
    library(ape)
    library(batchtools)
    library(magrittr)
})

Note: if you use systemPipeR in published research, please cite: Backman, T.W.H and Girke, T. (2016). systemPipeR: NGS Workflow and Report Generation Environment. BMC Bioinformatics, 17: 388. 10.1186/s12859-016-1241-0.

Introduction

systemPipeR provides flexible utilities for building and running automated end-to-end analysis workflows for a wide range of research applications, including next-generation sequencing (NGS) experiments, such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq [@H_Backman2016-bt]. Important features include a uniform workflow interface across different data analysis applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters (Figure 1). The latter supports interactive job submissions and batch submissions to queuing systems of clusters. For instance, systemPipeR can be used with most command-line aligners such as BWA [@Li2013-oy; @Li2009-oc], HISAT2 [@Kim2015-ve], TopHat2 [@Kim2013-vg] and Bowtie2 [@Langmead2012-bs], as well as the R-based NGS aligners Rsubread [@Liao2013-bn] and gsnap (gmapR) [@Wu2010-iq]. Efficient handling of complex sample sets (e.g. FASTQ/BAM files) and experimental designs are facilitated by a well-defined sample annotation infrastructure which improves reproducibility and user-friendliness of many typical analysis workflows in the NGS area [@Lawrence2013-kt].

The main motivation and advantages of using systemPipeR for complex data analysis tasks are:

1. Facilitates the design of complex NGS workflows involving multiple R/Bioconductor packages
2. Common workflow interface for different NGS applications
3. Makes NGS analysis with Bioconductor utilities more accessible to new users
4. Simplifies usage of command-line software from within R
5. Reduces the complexity of using compute clusters for R and command-line software
6. Accelerates runtime of workflows via parallelization on computer systems with multiple CPU cores and/or multiple compute nodes
7. Improves reproducibility by automating analyses and generation of analysis reports

Figure 1: Relevant features in systemPipeR. Workflow design concepts are illustrated under (A & B). Examples of systemPipeR’s visualization functionalities are given under (C).

A central concept for designing workflows within the systemPipeR environment is the use of workflow management containers. In previous versions, systemPipeR used a custom command-line interface called SYSargs (see Figure 3) and for this purpose will continue to be supported for some time. With the latest Bioconductor Release 3.9, we are adopting for this functionality the widely used community standard Common Workflow Language (CWL) for describing analysis workflows in a generic and reproducible manner, introducing SYSargs2 workflow control class (see Figure 2). Using this community standard in systemPipeR has many advantages. For instance, the integration of CWL allows running systemPipeR workflows from a single specification instance either entirely from within R, from various command-line wrappers (e.g., cwl-runner) or from other languages (, e.g., Bash or Python). systemPipeR includes support for both command-line and R/Bioconductor software as well as resources for containerization, parallel evaluations on computer clusters along with the automated generation of interactive analysis reports.

An important feature of systemPipeR's CWL interface is that it provides two options to run command-line tools and workflows based on CWL. First, one can run CWL in its native way via an R-based wrapper utility for cwl-runner or cwl-tools (CWL-based approach). Second, one can run workflows using CWL's command-line and workflow instructions from within R (R-based approach). In the latter case the same CWL workflow definition files (e.g. *.cwl and *.yml) are used but rendered and executed entirely with R functions defined by systemPipeR, and thus use CWL mainly as a command-line and workflow definition format rather than software to run workflows. In this regard systemPipeR also provides several convenience functions that are useful for designing and debugging workflows, such as a command-line rendering function to retrieve the exact command-line strings for each data set and processing step prior to running a command-line.

This overview introduces the design of a new CWL S4 class in systemPipeR, as well as the custom command-line interface, combined with the overview of all the common analysis steps of NGS experiments.

Workflow design structure using SYSargs2

The flexibility of systemPipeR's new interface workflow control class is the driving factor behind the use of as many steps necessary for the analysis, as well as the connection between command-line- or R-based software. The connectivity among all workflow steps is achieved by the SYSargs2 workflow control class (see Figure 3). This S4 class is a list-like container where each instance stores all the input/output paths and parameter components required for a particular data analysis step. SYSargs2 instances are generated by two constructor functions, loadWorkflow and renderWF, using as data input targets or yaml files as well as two cwl parameter files (for details see below). When running preconfigured workflows, the only input the user needs to provide is the initial targets file containing the paths to the input files (e.g. FASTQ) along with unique sample labels. Subsequent targets instances are created automatically. The parameters required for running command-line software is provided by the parameter (.cwl) files described below.

We also introduce the SYSargs2Pipe class that organizes one or many SYSargs2 containers in a single compound object capturing all information required to run, control and monitor complex workflows from start to finish. This design enhances the systemPipeR workflow framework with a generalized, flexible, and robust design.

Figure 2: Workflow steps with input/output file operations are controlled by SYSargs2 objects. Each SYSargs2 instance is constructed from one targets and two param files. The only input provided by the user is the initial targets file. Subsequent targets instances are created automatically, from the previous output files. Any number of predefined or custom workflow steps are supported. One or many SYSargs2 objects are organized in an SYSargs2Pipe container.

Workflow Management using SYSargsList

systemPipeR allows creation (multi-step analyses) and execution of workflow entirely for R, with control, flexibility, and scalability of the all process. The execution of the workflow can be sent to a HPC, can be parallelizes, accelerating results acquisition. A workflow management system provides an infrastructure for the set-up, performance and monitoring of a defined sequence of tasks, arranged as a workflow application.

Figure 3: Workflow Management using SYSargsList.

Workflow design structure using SYSargs: Previous version

Instances of this S4 object class are constructed by the systemArgs function from two simple tabular files: a targets file and a param file. The latter is optional for workflow steps lacking command-line software. Typically, a SYSargs instance stores all sample-level inputs as well as the paths to the corresponding outputs generated by command-line- or R-based software generating sample-level output files, such as read preprocessors (trimmed/filtered FASTQ files), aligners (SAM/BAM files), variant callers (VCF/BCF files) or peak callers (BED/WIG files). Each sample level input/output operation uses its own SYSargs instance. The outpaths of SYSargs usually define the sample inputs for the next SYSargs instance. This connectivity is established by writing the outpaths with the writeTargetsout function to a new targets file that serves as input to the next systemArgs call. Typically, the user has to provide only the initial targets file. All downstream targets files are generated automatically. By chaining several SYSargs steps together one can construct complex workflows involving many sample-level input/output file operations with any combination of command-line or R-based software.

Figure 4: Workflow design structure of systemPipeR using SYSargs.

Getting Started

Installation

The R software for running systemPipeR can be downloaded from CRAN. The systemPipeR environment can be installed from the R console using the BiocManager::install command. The associated data package systemPipeRdata can be installed the same way. The latter is a helper package for generating systemPipeR workflow environments with a single command containing all parameter files and sample data required to quickly test and run workflows.

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

Please note that if you desire to use a third-party command line tool, the particular tool and dependencies need to be installed and exported in your PATH. See details.

Loading package and documentation

library("systemPipeR") # Loads the package
library(help="systemPipeR") # Lists package info
vignette("systemPipeR") # Opens vignette

Load sample data and workflow templates

The mini sample FASTQ files used by this overview vignette as well as the associated workflow reporting vignettes can be loaded via the systemPipeRdata package as shown below. The chosen data set SRP010938 obtains 18 paired-end (PE) read sets from Arabidposis thaliana [@Howard2013-fq]. To minimize processing time during testing, each FASTQ file has been subsetted to 90,000-100,000 randomly sampled PE reads that map to the first 100,000 nucleotides of each chromosome of the A. thalina genome. The corresponding reference genome sequence (FASTA) and its GFF annotation files (provided in the same download) have been truncated accordingly. This way the entire test sample data set requires less than 200MB disk storage space. A PE read set has been chosen for this test data set for flexibility, because it can be used for testing both types of analysis routines requiring either SE (single-end) reads or PE reads.

The following generates a fully populated systemPipeR workflow environment (here for RNA-Seq) in the current working directory of an R session. At this time the package includes workflow templates for RNA-Seq, ChIP-Seq, VAR-Seq, and Ribo-Seq. Templates for additional NGS applications will be provided in the future.

library(systemPipeRdata)
genWorkenvir(workflow="rnaseq")
setwd("rnaseq")

If you desire run this tutorial with your data set, please follow the instruction here:

library(systemPipeRdata)
genWorkenvir(workflow="new", mydirname = "FEB_project")

Workflow template from an individual's package

The package provides pre-configured workflows and reporting templates for a wide range of NGS applications that are listed here. Additional workflow templates will be provided in the future. If you desire to use an individual package and version, follow the instruction below:

library(systemPipeRdata)
genWorkenvir(workflow=NULL, package_repo = "systemPipeR/systemPipeRIBOseq", ref = "master", subdir = NULL)

library(systemPipeRdata)
genWorkenvir(workflow=NULL, package_repo = "systemPipeR/systemPipeRNAseq", ref = "singleMachine", subdir = NULL)

Directory Structure

The working environment of the sample data loaded in the previous step contains the following pre-configured directory structure (Figure 4). Directory names are indicated in green. Users can change this structure as needed, but need to adjust the code in their workflows accordingly.

• workflow/ (e.g. rnaseq/)
  • This is the root directory of the R session running the workflow.
  • Run script ( *.Rmd) and sample annotation (targets.txt) files are located here.
  • Note, this directory can have any name (e.g. rnaseq, varseq). Changing its name does not require any modifications in the run script(s).
• Important subdirectories:
  • param/
    • Stores non-CWL parameter files such as: *.param, *.tmpl and *.run.sh. These files are only required for backwards compatibility to run old workflows using the previous custom command-line interface.
    • param/cwl/: This subdirectory stores all the CWL parameter files. To organize workflows, each can have its own subdirectory, where all CWL param and input.yml files need to be in the same subdirectory.
  • data/
    • FASTQ files
    • FASTA file of reference (e.g. reference genome)
    • Annotation files
    • etc.
  • results/
    • Analysis results are usually written to this directory, including: alignment, variant and peak files (BAM, VCF, BED); tabular result files; and image/plot files
    • Note, the user has the option to organize results files for a given sample and analysis step in a separate subdirectory.

Figure 5: systemPipeR's preconfigured directory structure.

The following parameter files are included in each workflow template:

1. targets.txt: initial one provided by user; downstream targets_*.txt files are generated automatically
2. *.param/cwl: defines parameter for input/output file operations, e.g.:
   • hisat2-se/hisat2-mapping-se.cwl
   • hisat2-se/hisat2-mapping-se.yml
3. *_run.sh: optional bash scripts
4. Configuration files for computer cluster environments (skip on single machines):
   • .batchtools.conf.R: defines the type of scheduler for batchtools pointing to template file of cluster, and located in user's home directory
   • *.tmpl: specifies parameters of scheduler used by a system, e.g. Torque, SGE, Slurm, etc.

Structure of targets file

The targets file defines all input files (e.g. FASTQ, BAM, BCF) and sample comparisons of an analysis workflow. The following shows the format of a sample targets file included in the package. It also can be viewed and downloaded from systemPipeR's GitHub repository here. In a target file with a single type of input files, here FASTQ files of single-end (SE) reads, the first three columns are mandatory including their column names, while it is four mandatory columns for FASTQ files of PE reads. All subsequent columns are optional and any number of additional columns can be added as needed.

Users should note here, the usage of targets files is optional when using systemPipeR's new CWL interface. They can be replaced by a standard YAML input file used by CWL. Since for organizing experimental variables targets files are extremely useful and user-friendly. Thus, we encourage users to keep using them.

Structure of targets file for single-end (SE) samples

library(systemPipeR)
targetspath <- system.file("extdata", "targets.txt", package="systemPipeR") 
read.delim(targetspath, comment.char = "#")[1:4,]

To work with custom data, users need to generate a targets file containing the paths to their own FASTQ files and then provide under targetspath the path to the corresponding targets file.

Structure of targets file for paired-end (PE) samples

For paired-end (PE) samples, the structure of the targets file is similar, where users need to provide two FASTQ path columns: FileName1 and FileName2 with the paths to the PE FASTQ files.

targetspath <- system.file("extdata", "targetsPE.txt", package="systemPipeR")
read.delim(targetspath, comment.char = "#")[1:2,1:6]

Sample comparisons

Sample comparisons are defined in the header lines of the targets file starting with '# <CMP>'.

readLines(targetspath)[1:4]

The function readComp imports the comparison information and stores it in a list. Alternatively, readComp can obtain the comparison information from the corresponding SYSargs object (see below). Note, these header lines are optional. They are mainly useful for controlling comparative analyses according to certain biological expectations, such as identifying differentially expressed genes in RNA-Seq experiments based on simple pair-wise comparisons.

readComp(file=targetspath, format="vector", delim="-")

Structure of the new param files and construct SYSargs2 container

SYSargs2 stores all the information and instructions needed for processing a set of input files with a single or many command-line steps within a workflow (i.e. several components of the software or several independent software tools). The SYSargs2 object is created and fully populated with the loadWF and renderWF functions, respectively.

In CWL, files with the extension .cwl define the parameters of a chosen command-line step or workflow, while files with the extension .yml define the input variables of command-line steps. Note, input variables provided by a targets file can be passed on to a SYSargs2 instance via the inputvars argument of the renderWF function.

hisat2.cwl <- system.file("extdata", "cwl/hisat2/hisat2-se/hisat2-mapping-se.cwl", package="systemPipeR")
yaml::read_yaml(hisat2.cwl)

hisat2.yml <- system.file("extdata", "cwl/hisat2/hisat2-se/hisat2-mapping-se.yml", package="systemPipeR")
yaml::read_yaml(hisat2.yml)

The following imports a .cwl file (here hisat2-mapping-se.cwl) for running the short read aligner HISAT2 [@Kim2015-ve]. The loadWorkflow and renderWF functions render the proper command-line strings for each sample and software tool.

library(systemPipeR)
targets <- system.file("extdata", "targets.txt", package="systemPipeR")
dir_path <- system.file("extdata/cwl/hisat2/hisat2-se", package="systemPipeR")
WF <- loadWF(targets=targets, wf_file="hisat2-mapping-se.cwl",
                   input_file="hisat2-mapping-se.yml",
                   dir_path=dir_path)

WF <- renderWF(WF, inputvars=c(FileName="_FASTQ_PATH1_", SampleName="_SampleName_"))

Several accessor methods are available that are named after the slot names of the SYSargs2 object.

names(WF)

Of particular interest is the cmdlist() method. It constructs the system commands for running command-line software as specified by a given .cwl file combined with the paths to the input samples (e.g. FASTQ files) provided by a targets file. The example below shows the cmdlist() output for running HISAT2 on the first SE read sample. Evaluating the output of cmdlist() can be very helpful for designing and debugging .cwl files of new command-line software or changing the parameter settings of existing ones.

cmdlist(WF)[1]

The output components of SYSargs2 define the expected output files for each step in the workflow; some of which are the input for the next workflow step, here next SYSargs2 instance (see Figure 2).

output(WF)[1]
modules(WF)
targets(WF)[1]
targets.as.df(targets(WF))[1:4,1:4]
output(WF)[1]
cwlfiles(WF)
inputvars(WF)

In an 'R-centric' rather than a 'CWL-centric' workflow design the connectivity among workflow steps is established by writing all relevant output with the writeTargetsout function to a new targets file that serves as input to the next loadWorkflow and renderWF call. By chaining several SYSargs2 steps together one can construct complex workflows involving many sample-level input/output file operations with any combination of command-line or R-based software. Alternatively, a CWL-centric workflow design can be used that defines all/most workflow steps with CWL workflow and parameter files. Due to time and space restrictions, the CWL-centric approach is not covered by this tutorial.

Third-party software tools {#tools}

Current, systemPipeR provides the param file templates for third-party software tools. Please check the listed software tools.

library(dplyr)
library(kableExtra)
SYS_software <- system.file("extdata", "SYS_software.csv", package="systemPipeR")
#SYS_software <- "SYS_software.csv"
software <- read.delim(SYS_software, sep=",", comment.char = "#")
colors <- colorRampPalette((c("darkseagreen", "indianred1")))(length(unique(software$Category)))
id <- as.numeric(c((unique(software$Category))))
software %>%
  mutate(Step = cell_spec(Step, color = "white", bold = T,
    background = factor(Category, id, colors)
  )) %>%
   select(Tool, Description, Step) %>%
  arrange(Tool) %>% 
  kable(escape = F, align = "c", col.names = c("Tool Name", "Description", "Step")) %>%
  kable_styling(c("striped", "hover", "condensed"), full_width = T) %>%
  scroll_box(width = "80%", height = "500px")

Remember, if you desire to run any of these tools, make sure to have the respective software installed on your system and configure in the PATH. You can check as follows:

tryCL(command="grep") 

Structure of param file and SYSargs container (Previous version)

The param file defines the parameters of a chosen command-line software. The following shows the format of a sample param file provided by this package.

parampath <- system.file("extdata", "tophat.param", package="systemPipeR")
read.delim(parampath, comment.char = "#")

The systemArgs function imports the definitions of both the param file and the targets file, and stores all relevant information in a SYSargs object (S4 class). To run the pipeline without command-line software, one can assign NULL to sysma instead of a param file. In addition, one can start systemPipeR workflows with pre-generated BAM files by providing a targets file where the FileName column provides the paths to the BAM files. Note, in the following example the usage of suppressWarnings() is only relevant for building this vignette. In typical workflows it should be removed.

targetspath <- system.file("extdata", "targets.txt", package="systemPipeR") 
args <- suppressWarnings(systemArgs(sysma=parampath, mytargets=targetspath))
args

Several accessor methods are available that are named after the slot names of the SYSargs object.

names(args)

Of particular interest is the sysargs() method. It constructs the system commands for running command-lined software as specified by a given param file combined with the paths to the input samples (e.g. FASTQ files) provided by a targets file. The example below shows the sysargs() output for running TopHat2 on the first PE read sample. Evaluating the output of sysargs() can be very helpful for designing and debugging param files of new command-line software or changing the parameter settings of existing ones.

sysargs(args)[1]
modules(args)
cores(args)
outpaths(args)[1]

The content of the param file can also be returned as JSON object as follows (requires rjson package).

systemArgs(sysma=parampath, mytargets=targetspath, type="json")

How to run a Workflow

This tutorial introduces the basic ideas and tools needed to build a specific workflow from preconfigured templates.

Load sample data and workflow templates

library(systemPipeRdata)
genWorkenvir(workflow="rnaseq")
setwd("rnaseq")

Setup and Requirements

To go through this tutorial, you need the following software installed:

• R/>=3.6.2
• systemPipeR R package (version 1.22)
• Hisat2/2.1.0

If you desire to build your pipeline with any different software, make sure to have the respective software installed and configured in your PATH. To make sure if the configuration is right, you always can test as follow:

tryCL(command="hisat2") ## "All set up, proceed!"

Project Initialization

The Project management structure is essential, especially for reproducibility and efficiency in the analysis. Here we show how to construct an instance of this S4 object class by the initWF function. The object of class SYSarsgsList storing all the configuration information for the project and allows management and control at a high level.

script <- "systemPipeRNAseq.Rmd"
targetspath <- "targets.txt"
sysargslist <- initWF(script = script, targets = targetspath)

Project Initialization in a Temporary Directory

library(systemPipeRdata)
script <- system.file("extdata/workflows/rnaseq", "systemPipeRNAseq.Rmd", package="systemPipeRdata")
targets <- system.file("extdata", "targets.txt", package="systemPipeR")
dir_path <- tempdir()
SYSconfig <- initProject(projPath=dir_path, targets=targets, script=script, overwrite = TRUE)
sysargslist_temp <- initWF(sysconfig ="SYSconfig.yml")

Configuration and run of the project

sysargslist <- configWF(x=sysargslist, input_steps = "1:3")
sysargslist <- runWF(sysargslist = sysargslist, steps = "ALL")
sysargslist <- runWF(sysargslist = sysargslist, steps = "1:2")

How to Use Pipes with systemPipeR

At first encounter, you may wonder whether an operator such as %>% can really be all that beneficial; but as you may notice, it semantically changes your code in a way that makes it more intuitive to both read and write.

Consider the following example, in which the steps are the initialization, configuration and running the entire workflow.

library(systemPipeR)
sysargslist <- initWF(script ="systemPipeRNAseq.Rmd", overwrite = T) %>%
    configWF(input_steps = "1:3") %>%
    runWF(steps = "1:2")

How to run the workflow on a cluster

This section of the tutorial provides an introduction to the usage of the systemPipeR features on a cluster.

Now open the R markdown script *.Rmdin your R IDE (_e.g._vim-r or RStudio) and run the workflow as outlined below. If you work under Vim-R-Tmux, the following command sequence will connect the user in an interactive session with a node on the cluster. The code of the Rmd script can then be sent from Vim on the login (head) node to an open R session running on the corresponding computer node. This is important since Tmux sessions should not be run on the computer nodes.

q("no") # closes R session on head node

```{bash node_environment, eval=FALSE} srun --x11 --partition=short --mem=2gb --cpus-per-task 4 --ntasks 1 --time 2:00:00 --pty bash -l module load R/3.4.2 R

Now check whether your R session is running on a computer node of the cluster and not on a head node.

```r
system("hostname") # should return name of a compute node starting with i or c 
getwd() # checks current working directory of R session
dir() # returns content of current working directory

Parallelization on clusters

Alternatively, the computation can be greatly accelerated by processing many files in parallel using several compute nodes of a cluster, where a scheduling/queuing system is used for load balancing. For this the clusterRun function submits the computing requests to the scheduler using the run specifications defined by runCommandline.

To avoid over-subscription of CPU cores on the compute nodes, the value from yamlinput(args)['thread'] is passed on to the submission command, here ncpus in the resources list object. The number of independent parallel cluster processes is defined under the Njobs argument. The following example will run 18 processes in parallel using for each 4 CPU cores. If the resources available on a cluster allow running all 18 processes at the same time then the shown sample submission will utilize in total 72 CPU cores. Note, clusterRun can be used with most queueing systems as it is based on utilities from the batchtools package which supports the use of template files (*.tmpl) for defining the run parameters of different schedulers. To run the following code, one needs to have both a conf file (see .batchtools.conf.R samples here) and a template file (see *.tmpl samples here) for the queueing available on a system. The following example uses the sample conf and template files for the Slurm scheduler provided by this package.

library(batchtools)
resources <- list(walltime=120, ntasks=1, ncpus=4, memory=1024)
reg <- clusterRun(args, FUN = runCommandline, more.args = list(args=args, make_bam=TRUE, dir=FALSE), 
                  conffile = ".batchtools.conf.R", template = "batchtools.slurm.tmpl", 
                  Njobs=18, runid="01", resourceList=resources)
getStatus(reg=reg)
waitForJobs(reg=reg)

Workflow steps overview

Define environment settings and samples

A typical workflow starts with generating the expected working environment containing the proper directory structure, input files, and parameter settings. To simplify this task, one can load one of the existing NGS workflows templates provided by systemPipeRdata into the current working directory. The following does this for the rnaseq template. The name of the resulting workflow directory can be specified under the mydirname argument. The default NULL uses the name of the chosen workflow. An error is issued if a directory of the same name and path exists already. On Linux and OS X systems one can also create new workflow instances from the command-line of a terminal as shown here. To apply workflows to custom data, the user needs to modify the targets file and if necessary update the corresponding .cwl and .yml files. A collection of pre-generated .cwl and .yml files are provided in the param/cwl subdirectory of each workflow template. They are also viewable in the GitHub repository of systemPipeRdata (see here).

library(systemPipeR)
library(systemPipeRdata)
genWorkenvir(workflow="rnaseq", mydirname=NULL)
setwd("rnaseq")

Read Preprocessing

Preprocessing with preprocessReads function

The function preprocessReads allows to apply predefined or custom read preprocessing functions to all FASTQ files referenced in a SYSargs2 container, such as quality filtering or adaptor trimming routines. The paths to the resulting output FASTQ files are stored in the output slot of the SYSargs2 object. Internally, preprocessReads uses the FastqStreamer function from the ShortRead package to stream through large FASTQ files in a memory-efficient manner. The following example performs adaptor trimming with the trimLRPatterns function from the Biostrings package. After the trimming step a new targets file is generated (here targets_trimPE.txt) containing the paths to the trimmed FASTQ files. The new targets file can be used for the next workflow step with an updated SYSargs2 instance, e.g. running the NGS alignments with the trimmed FASTQ files.

Construct SYSargs2 object from cwl and yml param and targets files.

targets <- system.file("extdata", "targets.txt", package="systemPipeR")
dir_path <- system.file("extdata/cwl/preprocessReads/trim-se", package="systemPipeR")
trim <- loadWorkflow(targets=targets, wf_file="trim-se.cwl", input_file="trim-se.yml", dir_path=dir_path)
trim <- renderWF(trim, inputvars=c(FileName="_FASTQ_PATH1_", SampleName="_SampleName_"))
trim

targetsPE <- system.file("extdata", "targetsPE.txt", package="systemPipeR")
dir_path <- system.file("extdata/cwl/preprocessReads/trim-pe", package="systemPipeR")
trim <- loadWorkflow(targets=targetsPE, wf_file="trim-pe.cwl", input_file="trim-pe.yml", dir_path=dir_path)
trim <- renderWF(trim, inputvars=c(FileName1="_FASTQ_PATH1_", FileName2="_FASTQ_PATH2_", SampleName="_SampleName_"))
trim
output(trim)[1:2]

preprocessReads(args=trim, Fct="trimLRPatterns(Rpattern='GCCCGGGTAA', 
                subject=fq)", batchsize=100000, overwrite=TRUE, compress=TRUE)

The following example shows how one can design a custom read preprocessing function using utilities provided by the ShortRead package, and then run it in batch mode with the 'preprocessReads' function (here on paired-end reads).

filterFct <- function(fq, cutoff=20, Nexceptions=0) {
    qcount <- rowSums(as(quality(fq), "matrix") <= cutoff, na.rm=TRUE)
    # Retains reads where Phred scores are >= cutoff with N exceptions
    fq[qcount <= Nexceptions] 
}
preprocessReads(args=trim, Fct="filterFct(fq, cutoff=20, Nexceptions=0)", 
                batchsize=100000)

Preprocessing with TrimGalore!

TrimGalore! is a wrapper tool to consistently apply quality and adapter trimming to fastq files, with some extra functionality for removing Reduced Representation Bisulfite-Seq (RRBS) libraries.

targets <- system.file("extdata", "targets.txt", package="systemPipeR")
dir_path <- system.file("extdata/cwl/trim_galore/trim_galore-se", package="systemPipeR")
trimG <- loadWorkflow(targets=targets, wf_file="trim_galore-se.cwl", input_file="trim_galore-se.yml", dir_path=dir_path)
trimG <- renderWF(trimG, inputvars=c(FileName="_FASTQ_PATH1_", SampleName="_SampleName_"))
trimG
cmdlist(trimG)[1:2]
output(trimG)[1:2]
## Run Single Machine Option
trimG <- runCommandline(trimG[1], make_bam = FALSE)

Preprocessing with Trimmomatic

targetsPE <- system.file("extdata", "targetsPE.txt", package="systemPipeR")
dir_path <- system.file("extdata/cwl/trimmomatic/trimmomatic-pe", package="systemPipeR")
trimM <- loadWorkflow(targets=targetsPE, wf_file="trimmomatic-pe.cwl", input_file="trimmomatic-pe.yml", dir_path=dir_path)
trimM <- renderWF(trimM, inputvars=c(FileName1="_FASTQ_PATH1_", FileName2="_FASTQ_PATH2_", SampleName="_SampleName_"))
trimM
cmdlist(trimM)[1:2]
output(trimM)[1:2]
## Run Single Machine Option
trimM <- runCommandline(trimM[1], make_bam = FALSE)

FASTQ quality report

The following seeFastq and seeFastqPlot functions generate and plot a series of useful quality statistics for a set of FASTQ files including per cycle quality box plots, base proportions, base-level quality trends, relative k-mer diversity, length and occurrence distribution of reads, number of reads above quality cutoffs and mean quality distribution.
The function seeFastq computes the quality statistics and stores the results in a relatively small list object that can be saved to disk with save() and reloaded with load() for later plotting. The argument klength specifies the k-mer length and batchsize the number of reads to a random sample from each FASTQ file.

fqlist <- seeFastq(fastq=infile1(trim), batchsize=10000, klength=8)
pdf("./results/fastqReport.pdf", height=18, width=4*length(fqlist))
seeFastqPlot(fqlist)
dev.off()

Figure 5: FASTQ quality report

Parallelization of FASTQ quality report on a single machine with multiple cores.

f <- function(x) seeFastq(fastq=infile1(trim)[x], batchsize=100000, klength=8)
fqlist <- bplapply(seq(along=trim), f, BPPARAM = MulticoreParam(workers=4))
seeFastqPlot(unlist(fqlist, recursive=FALSE))

Parallelization of FASTQ quality report via scheduler (e.g. Slurm) across several compute nodes.

library(BiocParallel); library(batchtools)
f <- function(x) {
  library(systemPipeR)
  targetsPE <- system.file("extdata", "targetsPE.txt", package="systemPipeR")
  dir_path <- system.file("extdata/cwl/preprocessReads/trim-pe", package="systemPipeR")
  trim <- loadWorkflow(targets=targetsPE, wf_file="trim-pe.cwl", input_file="trim-pe.yml", dir_path=dir_path)
  trim <- renderWF(trim, inputvars=c(FileName1="_FASTQ_PATH1_", FileName2="_FASTQ_PATH2_", SampleName="_SampleName_"))
  seeFastq(fastq=infile1(trim)[x], batchsize=100000, klength=8)
}
resources <- list(walltime=120, ntasks=1, ncpus=4, memory=1024) 
param <- BatchtoolsParam(workers = 4, cluster = "slurm", template = "batchtools.slurm.tmpl", resources = resources)
fqlist <- bplapply(seq(along=trim), f, BPPARAM = param)
seeFastqPlot(unlist(fqlist, recursive=FALSE))

NGS Alignment software

After quality control, the sequence reads can be aligned to a reference genome or transcriptome database. The following sessions present some NGS sequence alignment software. Select the most accurate aligner and determining the optimal parameter for your custom data set project.

For all the following examples, it is necessary to install the respective software and export the PATH accordingly. If it is available Environment Module in the system, you can load all the request software with moduleload(args) function.

Alignment with HISAT2 using SYSargs2

The following steps will demonstrate how to use the short read aligner Hisat2 [@Kim2015-ve] in both interactive job submissions and batch submissions to queuing systems of clusters using the systemPipeR's new CWL command-line interface.

The parameter settings of the aligner are defined in the hisat2-mapping-se.cwl and hisat2-mapping-se.yml files. The following shows how to construct the corresponding SYSargs2 object, here args.

targets <- system.file("extdata", "targets.txt", package="systemPipeR")
dir_path <- system.file("extdata/cwl/hisat2/hisat2-se", package="systemPipeR")
args <- loadWorkflow(targets=targets, wf_file="hisat2-mapping-se.cwl", input_file="hisat2-mapping-se.yml", dir_path=dir_path)
args <- renderWF(args, inputvars=c(FileName="_FASTQ_PATH1_", SampleName="_SampleName_"))
args
cmdlist(args)[1:2]
output(args)[1:2]

Subsetting SYSargs2 class slots for each workflow step.

subsetWF(args, slot="input", subset='FileName')[1:2] ## Subsetting the input files for this particular workflow 
subsetWF(args, slot="output", subset=1, index=1)[1:2]  ## Subsetting the output files for one particular step in the workflow 
subsetWF(args, slot="step", subset=1)[1] ## Subsetting the command-lines for one particular step in the workflow 
subsetWF(args, slot="output", subset=1, index=1, delete=TRUE)[1] ## DELETING specific output files

Build Hisat2 index.

dir_path <- system.file("extdata/cwl/hisat2/hisat2-idx", package="systemPipeR")
idx <- loadWorkflow(targets=NULL, wf_file="hisat2-index.cwl", input_file="hisat2-index.yml", dir_path=dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx)

## Run 
runCommandline(idx, make_bam = FALSE)

Interactive job submissions in a single machine

To simplify the short read alignment execution for the user, the command-line can be run with the runCommandline function. The execution will be on a single machine without submitting to a queuing system of a computer cluster. This way, the input FASTQ files will be processed sequentially. By default runCommandline auto detects SAM file outputs and converts them to sorted and indexed BAM files, using internally the Rsamtools package [@Rsamtools]. Besides, runCommandline allows the user to create a dedicated results folder for each workflow and a sub-folder for each sample defined in the targets file. This includes all the output and log files for each step. When these options are used, the output location will be updated by default and can be assigned to the same object.

runCommandline(args, make_bam = FALSE) ## generates alignments and writes *.sam files to ./results folder 
args <- runCommandline(args, make_bam = TRUE) ## same as above but writes files and converts *.sam files to sorted and indexed BAM files. Assigning the new extention of the output files to the object args.

If available, multiple CPU cores can be used for processing each file. The number of CPU cores (here 4) to use for each process is defined in the *.yml file. With yamlinput(args)['thread'] one can return this value from the SYSargs2 object.

Parallelization on clusters

Alternatively, the computation can be greatly accelerated by processing many files in parallel using several compute nodes of a cluster, where a scheduling/queuing system is used for load balancing. For this the clusterRun function submits the computing requests to the scheduler using the run specifications defined by runCommandline.

To avoid over-subscription of CPU cores on the compute nodes, the value from yamlinput(args)['thread'] is passed on to the submission command, here ncpus in the resources list object. The number of independent parallel cluster processes is defined under the Njobs argument. The following example will run 18 processes in parallel using for each 4 CPU cores. If the resources available on a cluster allow running all 18 processes at the same time then the shown sample submission will utilize in total 72 CPU cores. Note, clusterRun can be used with most queueing systems as it is based on utilities from the batchtools package which supports the use of template files (*.tmpl) for defining the run parameters of different schedulers. To run the following code, one needs to have both a conf file (see .batchtools.conf.R samples here) and a template file (see *.tmpl samples here) for the queueing available on a system. The following example uses the sample conf and template files for the Slurm scheduler provided by this package.

library(batchtools)
resources <- list(walltime=120, ntasks=1, ncpus=4, memory=1024)
reg <- clusterRun(args, FUN = runCommandline, more.args = list(args=args, make_bam=TRUE, dir=FALSE), 
                  conffile = ".batchtools.conf.R", template = "batchtools.slurm.tmpl", 
                  Njobs=18, runid="01", resourceList=resources)
getStatus(reg=reg)
waitForJobs(reg=reg)

Check and update the output location if necessary.

args <- output_update(args, dir=FALSE, replace=TRUE, extension=c(".sam", ".bam")) ## Updates the output(args) to the right location in the subfolders
output(args)

Create new targets file

To establish the connectivity to the next workflow step, one can write a new targets file with the writeTargetsout function. The new targets file serves as input to the next loadWorkflow and renderWF call.

names(clt(args))
writeTargetsout(x=args, file="default", step = 1, 
                new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)

Alignment with HISAT2 and SAMtools

Alternatively, it possible to build an workflow with HISAT2 and SAMtools.

targets <- system.file("extdata", "targets.txt", package="systemPipeR")
dir_path <- system.file("extdata/cwl/workflow-hisat2/workflow-hisat2-se", package="systemPipeR")
WF <- loadWorkflow(targets=targets, wf_file="workflow_hisat2-se.cwl", input_file="workflow_hisat2-se.yml", dir_path=dir_path)
WF <- renderWF(WF, inputvars=c(FileName="_FASTQ_PATH1_", SampleName="_SampleName_"))
WF
cmdlist(WF)[1:2]
output(WF)[1:2]

Alignment with Tophat2

The NGS reads of this project can also be aligned against the reference genome sequence using Bowtie2/TopHat2 [@Kim2013-vg; @Langmead2012-bs].

Build Bowtie2 index.

dir_path <- system.file("extdata/cwl/bowtie2/bowtie2-idx", package="systemPipeR")
idx <- loadWorkflow(targets=NULL, wf_file="bowtie2-index.cwl", input_file="bowtie2-index.yml", dir_path=dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx)

## Run in single machine
runCommandline(idx, make_bam = FALSE)

The parameter settings of the aligner are defined in the tophat2-mapping-pe.cwl and tophat2-mapping-pe.yml files. The following shows how to construct the corresponding SYSargs2 object, here tophat2PE.

targetsPE <- system.file("extdata", "targetsPE.txt", package = "systemPipeR")
dir_path <- system.file("extdata/cwl/tophat2/tophat2-pe", package="systemPipeR")
tophat2PE <- loadWorkflow(targets = targetsPE, wf_file = "tophat2-mapping-pe.cwl", 
    input_file = "tophat2-mapping-pe.yml", dir_path = dir_path)
tophat2PE <- renderWF(tophat2PE, inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", 
    SampleName = "_SampleName_"))
tophat2PE
cmdlist(tophat2PE)[1:2]
output(tophat2PE)[1:2]

## Run in single machine
tophat2PE <- runCommandline(tophat2PE[1], make_bam = TRUE)

Parallelization on clusters.

resources <- list(walltime=120, ntasks=1, ncpus=4, memory=1024) 
reg <- clusterRun(tophat2PE, FUN = runCommandline, more.args = list(args=tophat2PE, make_bam=TRUE, dir=FALSE), 
                  conffile = ".batchtools.conf.R", template = "batchtools.slurm.tmpl", 
                  Njobs=18, runid="01", resourceList=resources)
waitForJobs(reg=reg)

Create new targets file

names(clt(tophat2PE))
writeTargetsout(x=tophat2PE, file="default", step = 1, 
                new_col = "tophat2PE", new_col_output_index = 1, overwrite = TRUE)

Alignment with Bowtie2 (e.g. for miRNA profiling)

The following example runs Bowtie2 as a single process without submitting it to a cluster.

Building the index:

dir_path <- system.file("extdata/cwl/bowtie2/bowtie2-idx", package="systemPipeR")
idx <- loadWorkflow(targets=NULL, wf_file="bowtie2-index.cwl", input_file="bowtie2-index.yml", dir_path=dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx)

## Run in single machine
runCommandline(idx, make_bam = FALSE)

Building all the command-line:

targetsPE <- system.file("extdata", "targetsPE.txt", package = "systemPipeR")
dir_path <- system.file("extdata/cwl/bowtie2/bowtie2-pe", package="systemPipeR")
bowtiePE <- loadWorkflow(targets = targetsPE, wf_file = "bowtie2-mapping-pe.cwl", 
    input_file = "bowtie2-mapping-pe.yml", dir_path = dir_path)
bowtiePE <- renderWF(bowtiePE, inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", 
    SampleName = "_SampleName_"))
bowtiePE
cmdlist(bowtiePE)[1:2]
output(bowtiePE)[1:2]

Running all the jobs to computing nodes.

resources <- list(walltime=120, ntasks=1, ncpus=4, memory=1024) 
reg <- clusterRun(bowtiePE, FUN = runCommandline, more.args = list(args=bowtiePE, dir = FALSE), 
    conffile = ".batchtools.conf.R", template = "batchtools.slurm.tmpl", 
    Njobs = 18, runid = "01", resourceList = resources)
getStatus(reg = reg)

Alternatively, it possible to run all the jobs in a single machine.

bowtiePE <- runCommandline(bowtiePE)

Create new targets file.

names(clt(bowtiePE))
writeTargetsout(x=bowtiePE, file="default", step = 1, 
                new_col = "bowtiePE", new_col_output_index = 1, overwrite = TRUE)

Alignment with BWA-MEM (e.g. for VAR-Seq)

The following example runs BWA-MEM as a single process without submitting it to a cluster. ##TODO: add reference

Build the index:

dir_path <- system.file("extdata/cwl/bwa/bwa-idx", package="systemPipeR")
idx <- loadWorkflow(targets=NULL, wf_file="bwa-index.cwl", input_file="bwa-index.yml", dir_path=dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx) # Indexes reference genome

## Run 
runCommandline(idx, make_bam = FALSE)

Running the alignment:

targetsPE <- system.file("extdata", "targetsPE.txt", package = "systemPipeR")
dir_path <- system.file("extdata/cwl/bwa/bwa-pe", package="systemPipeR")
bwaPE <- loadWorkflow(targets = targetsPE, wf_file = "bwa-pe.cwl", 
    input_file = "bwa-pe.yml", dir_path = dir_path)
bwaPE <- renderWF(bwaPE, inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", 
    SampleName = "_SampleName_"))
bwaPE
cmdlist(bwaPE)[1:2]
output(bwaPE)[1:2]
## Single Machine
bwaPE <- runCommandline(args= bwaPE, make_bam=FALSE) 

## Cluster
library(batchtools)
resources <- list(walltime = 120, ntasks = 1, ncpus = 4, memory = 1024)
reg <- clusterRun(bwaPE, FUN = runCommandline, more.args = list(args=bwaPE, dir = FALSE), 
    conffile = ".batchtools.conf.R", template = "batchtools.slurm.tmpl", 
    Njobs = 18, runid = "01", resourceList = resources)
getStatus(reg = reg)

Create new targets file.

names(clt(bwaPE))
writeTargetsout(x=bwaPE, file="default", step = 1, 
                new_col = "bwaPE", new_col_output_index = 1, overwrite = TRUE)

Alignment with Rsubread (e.g. for RNA-Seq)

The following example shows how one can use within the \Rpackage{systemPipeR} environment the R-based aligner \Rpackage{Rsubread}, allowing running from R or command-line.

## Build the index:
dir_path <- system.file("extdata/cwl/rsubread/rsubread-idx", package="systemPipeR")
idx <- loadWorkflow(targets = NULL, wf_file = "rsubread-index.cwl", 
                     input_file = "rsubread-index.yml", dir_path = dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx)
runCommandline(args= idx, make_bam = FALSE) 

## Running the alignment:
targets <- system.file("extdata", "targets.txt", package = "systemPipeR")
dir_path <- system.file("extdata/cwl/rsubread/rsubread-se", package="systemPipeR")
rsubread <- loadWorkflow(targets = targets, wf_file = "rsubread-mapping-se.cwl", 
                     input_file = "rsubread-mapping-se.yml", dir_path = dir_path)
rsubread <- renderWF(rsubread, inputvars = c(FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_"))
rsubread
cmdlist(rsubread)[1]

## Single Machine
rsubread <- runCommandline(args=rsubread[1]) 

Create new targets file.

names(clt(rsubread))
writeTargetsout(x=rsubread, file="default", step = 1, 
                new_col = "rsubread", new_col_output_index = 1, overwrite = TRUE)

Alignment with gsnap (e.g. for VAR-Seq and RNA-Seq)

Another R-based short read aligner is gsnap from the gmapR package [@Wu2010-iq]. The code sample below introduces how to run this aligner on multiple nodes of a compute cluster.

## Build the index:
dir_path <- system.file("extdata/cwl/gsnap/gsnap-idx", package="systemPipeR")
idx <- loadWorkflow(targets = NULL, wf_file = "gsnap-index.cwl", 
                     input_file = "gsnap-index.yml", dir_path = dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx)
runCommandline(args= idx, make_bam = FALSE) 

## Running the alignment:
targetsPE <- system.file("extdata", "targetsPE.txt", package = "systemPipeR")
dir_path <- system.file("extdata/cwl/gsnap/gsnap-pe", package="systemPipeR")
gsnap <- loadWorkflow(targets = targetsPE, wf_file = "gsnap-mapping-pe.cwl", 
                     input_file = "gsnap-mapping-pe.yml", dir_path = dir_path)
gsnap <- renderWF(gsnap, inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_"))
gsnap
cmdlist(gsnap)[1]
output(gsnap)[1]

## Cluster
library(batchtools)
resources <- list(walltime = 120, ntasks = 1, ncpus = 4, memory = 1024)
reg <- clusterRun(gsnap, FUN = runCommandline, more.args = list(args=gsnap, make_bam=FALSE), 
    conffile = ".batchtools.conf.R", template = "batchtools.slurm.tmpl", 
    Njobs = 18, runid = "01", resourceList = resources)
getStatus(reg = reg)
gsnap <- output_update(gsnap, dir=FALSE, replace=TRUE, extension=c(".sam", ".bam")) 

Create new targets file.

names(clt(gsnap))
writeTargetsout(x=gsnap, file="default", step = 1, 
                new_col = "gsnap", new_col_output_index = 1, overwrite = TRUE)

Create symbolic links for viewing BAM files in IGV

The genome browser IGV supports reading of indexed/sorted BAM files via web URLs. This way it can be avoided to create unnecessary copies of these large files. To enable this approach, an HTML directory with Http access needs to be available in the user account (e.g. home/publichtml) of a system. If this is not the case then the BAM files need to be moved or copied to the system where IGV runs. In the following, htmldir defines the path to the HTML directory with http access where the symbolic links to the BAM files will be stored. The corresponding URLs will be written to a text file specified under the _urlfile_ argument.

symLink2bam(sysargs=args, htmldir=c("~/.html/", "somedir/"), 
            urlbase="http://myserver.edu/~username/", 
        urlfile="IGVurl.txt")

Read counting for mRNA profiling experiments

Create txdb (needs to be done only once).

library(GenomicFeatures)
txdb <- makeTxDbFromGFF(file="data/tair10.gff", format="gff", dataSource="TAIR", organism="Arabidopsis thaliana")
saveDb(txdb, file="./data/tair10.sqlite")

The following performs read counting with summarizeOverlaps in parallel mode with multiple cores.

library(BiocParallel)
txdb <- loadDb("./data/tair10.sqlite")
eByg <- exonsBy(txdb, by="gene")
outpaths <- subsetWF(args, slot="output", subset=1, index=1)
bfl <- BamFileList(outpaths, yieldSize=50000, index=character())
multicoreParam <- MulticoreParam(workers=4); register(multicoreParam); registered()
counteByg <- bplapply(bfl, function(x) summarizeOverlaps(eByg, x, mode="Union", ignore.strand=TRUE, inter.feature=TRUE, singleEnd=TRUE))

# Note: for strand-specific RNA-Seq set 'ignore.strand=FALSE' and for PE data set 'singleEnd=FALSE'
countDFeByg <- sapply(seq(along=counteByg), 
                      function(x) assays(counteByg[[x]])$counts)
rownames(countDFeByg) <- names(rowRanges(counteByg[[1]])); colnames(countDFeByg) <- names(bfl)
rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts=x, ranges=eByg))
write.table(countDFeByg, "results/countDFeByg.xls", col.names=NA, quote=FALSE, sep="\t")
write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names=NA, quote=FALSE, sep="\t")

Please note, in addition to read counts this step generates RPKM normalized expression values. For most statistical differential expression or abundance analysis methods, such as edgeR or DESeq2, the raw count values should be used as input. The usage of RPKM values should be restricted to specialty applications required by some users, e.g. manually comparing the expression levels of different genes or features.

Read counting with summarizeOverlaps using multiple nodes of a cluster.

library(BiocParallel)
f <- function(x) {
    library(systemPipeR); library(BiocParallel); library(GenomicFeatures)
    txdb <- loadDb("./data/tair10.sqlite")
    eByg <- exonsBy(txdb, by="gene")
    args <- systemArgs(sysma="param/tophat.param", mytargets="targets.txt")
    outpaths <- subsetWF(args, slot="output", subset=1, index=1)
    bfl <- BamFileList(outpaths, yieldSize=50000, index=character())
    summarizeOverlaps(eByg, bfl[x], mode="Union", ignore.strand=TRUE, inter.feature=TRUE, singleEnd=TRUE)
}
resources <- list(walltime=120, ntasks=1, ncpus=4, memory=1024) 
param <- BatchtoolsParam(workers = 4, cluster = "slurm", template = "batchtools.slurm.tmpl", resources = resources)
counteByg <- bplapply(seq(along=args), f, BPPARAM = param)
countDFeByg <- sapply(seq(along=counteByg),
                      function(x) assays(counteByg[[x]])$counts)
rownames(countDFeByg) <- names(rowRanges(counteByg[[1]])); colnames(countDFeByg) <- names(outpaths)

Useful commands for monitoring the progress of submitted jobs

getStatus(reg=reg)
outpaths <- subsetWF(args, slot="output", subset=1, index=1)
file.exists(outpaths)
sapply(1:length(outpaths), function(x) loadResult(reg, id=x)) # Works after job completion

Read and alignment count stats

Generate a table of read and alignment counts for all samples.

read_statsDF <- alignStats(args) 
write.table(read_statsDF, "results/alignStats.xls", row.names=FALSE, quote=FALSE, sep="\t")

The following shows the first four lines of the sample alignment stats file provided by the systemPipeR package. For simplicity the number of PE reads is multiplied here by 2 to approximate proper alignment frequencies where each read in a pair is counted.

read.table(system.file("extdata", "alignStats.xls", package="systemPipeR"), header=TRUE)[1:4,]

Parallelization of read/alignment stats on single machine with multiple cores.

f <- function(x) alignStats(args[x])
read_statsList <- bplapply(seq(along=args), f, 
                           BPPARAM = MulticoreParam(workers=8))
read_statsDF <- do.call("rbind", read_statsList)

Parallelization of read/alignment stats via scheduler (e.g. Slurm) across several compute nodes.

library(BiocParallel); library(batchtools)
f <- function(x) {
    library(systemPipeR)
    targets <- system.file("extdata", "targets.txt", package="systemPipeR")
    dir_path <- "param/cwl/hisat2/hisat2-se" ## TODO: replace path to system.file 
    args <- loadWorkflow(targets=targets, wf_file="hisat2-mapping-se.cwl", input_file="hisat2-mapping-se.yml", dir_path=dir_path)
    args <- renderWF(args, inputvars=c(FileName="_FASTQ_PATH1_", SampleName="_SampleName_"))
    args <- output_update(args, dir=FALSE, replace=TRUE, extension=c(".sam", ".bam")) 
    alignStats(args[x])
}
resources <- list(walltime=120, ntasks=1, ncpus=4, memory=1024) 
param <- BatchtoolsParam(workers = 4, cluster = "slurm", template = "batchtools.slurm.tmpl", resources = resources)
read_statsList <- bplapply(seq(along=args), f, BPPARAM = param)
read_statsDF <- do.call("rbind", read_statsList)

Read counting for miRNA profiling experiments

Download miRNA genes from miRBase.

system("wget ftp://mirbase.org/pub/mirbase/19/genomes/My_species.gff3 -P ./data/")
gff <- import.gff("./data/My_species.gff3")
gff <- split(gff, elementMetadata(gff)$ID)
bams <- names(bampaths); names(bams) <- targets$SampleName
bfl <- BamFileList(bams, yieldSize=50000, index=character())
countDFmiR <- summarizeOverlaps(gff, bfl, mode="Union", ignore.strand=FALSE, inter.feature=FALSE) # Note: inter.feature=FALSE important since pre and mature miRNA ranges overlap
rpkmDFmiR <- apply(countDFmiR, 2, 
                   function(x) returnRPKM(counts=x, gffsub=gff))
write.table(assays(countDFmiR)$counts, "results/countDFmiR.xls", col.names=NA, quote=FALSE, sep="\t")
write.table(rpkmDFmiR, "results/rpkmDFmiR.xls", col.names=NA, quote=FALSE, sep="\t")

Correlation analysis of samples

The following computes the sample-wise Spearman correlation coefficients from the rlog (regularized-logarithm) transformed expression values generated with the DESeq2 package. After transformation to a distance matrix, hierarchical clustering is performed with the hclust function and the result is plotted as a dendrogram (sample_tree.pdf).

library(DESeq2, warn.conflicts=FALSE, quietly=TRUE); library(ape, warn.conflicts=FALSE)
countDFpath <- system.file("extdata", "countDFeByg.xls", package="systemPipeR")
countDF <- as.matrix(read.table(countDFpath))
colData <- data.frame(row.names=targets.as.df(targets(args))$SampleName, condition=targets.as.df(targets(args))$Factor)
dds <- DESeqDataSetFromMatrix(countData = countDF, colData = colData, design = ~ condition)
d <- cor(assay(rlog(dds)), method="spearman")
hc <- hclust(dist(1-d))
plot.phylo(as.phylo(hc), type="p", edge.col=4, edge.width=3, show.node.label=TRUE, no.margin=TRUE)

**Figure 6:** Correlation dendrogram of samples for _`rlog`_ values.

Alternatively, the clustering can be performed with RPKM normalized expression values. In combination with Spearman correlation the results of the two clustering methods are often relatively similar.

rpkmDFeBygpath <- system.file("extdata", "rpkmDFeByg.xls", package="systemPipeR")
rpkmDFeByg <- read.table(rpkmDFeBygpath, check.names=FALSE)
rpkmDFeByg <- rpkmDFeByg[rowMeans(rpkmDFeByg) > 50,]
d <- cor(rpkmDFeByg, method="spearman")
hc <- hclust(as.dist(1-d))
plot.phylo(as.phylo(hc), type="p", edge.col="blue", edge.width=2, show.node.label=TRUE, no.margin=TRUE)

DEG analysis with edgeR

The following run_edgeR function is a convenience wrapper for identifying differentially expressed genes (DEGs) in batch mode with edgeR's GML method [@Robinson2010-uk] for any number of pairwise sample comparisons specified under the cmp argument. Users are strongly encouraged to consult the edgeR vignette for more detailed information on this topic and how to properly run edgeR on data sets with more complex experimental designs.

targets <- read.delim(targetspath, comment="#")
cmp <- readComp(file=targetspath, format="matrix", delim="-")
cmp[[1]]
countDFeBygpath <- system.file("extdata", "countDFeByg.xls", package="systemPipeR")
countDFeByg <- read.delim(countDFeBygpath, row.names=1)
edgeDF <- run_edgeR(countDF=countDFeByg, targets=targets, cmp=cmp[[1]], independent=FALSE, mdsplot="")

Filter and plot DEG results for up and down-regulated genes. Because of the small size of the toy data set used by this vignette, the FDR value has been set to a relatively high threshold (here 10%). More commonly used FDR cutoffs are 1% or 5%. The definition of 'up' and 'down' is given in the corresponding help file. To open it, type ?filterDEGs in the R console.

DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=10))

**Figure 7:** Up and down regulated DEGs identified by _`edgeR`_.

names(DEG_list)
DEG_list$Summary[1:4,]

DEG analysis with DESeq2

The following run_DESeq2 function is a convenience wrapper for identifying DEGs in batch mode with DESeq2 [@Love2014-sh] for any number of pairwise sample comparisons specified under the cmp argument. Users are strongly encouraged to consult the DESeq2 vignette for more detailed information on this topic and how to properly run DESeq2 on data sets with more complex experimental designs.

degseqDF <- run_DESeq2(countDF=countDFeByg, targets=targets, cmp=cmp[[1]], independent=FALSE)

Filter and plot DEG results for up and down-regulated genes.

DEG_list2 <- filterDEGs(degDF=degseqDF, filter=c(Fold=2, FDR=10))

**Figure 8:** Up and down regulated DEGs identified by _`DESeq2`_.

Venn Diagrams

The function overLapper can compute Venn intersects for large numbers of sample sets (up to 20 or more) and vennPlot can plot 2-5 way Venn diagrams. A useful feature is the possibility to combine the counts from several Venn comparisons with the same number of sample sets in a single Venn diagram (here for 4 up and down DEG sets).

vennsetup <- overLapper(DEG_list$Up[6:9], type="vennsets")
vennsetdown <- overLapper(DEG_list$Down[6:9], type="vennsets")
vennPlot(list(vennsetup, vennsetdown), mymain="", mysub="", colmode=2, ccol=c("blue", "red"))

**Figure 9:** Venn Diagram for 4 Up and Down DEG Sets.

GO term enrichment analysis of DEGs

Obtain gene-to-GO mappings

The following shows how to obtain gene-to-GO mappings from biomaRt (here for A. thaliana) and how to organize them for the downstream GO term enrichment analysis. Alternatively, the gene-to-GO mappings can be obtained for many organisms from Bioconductor's *.db genome annotation packages or GO annotation files provided by various genome databases. For each annotation, this relatively slow preprocessing step needs to be performed only once. Subsequently, the preprocessed data can be loaded with the load function as shown in the next subsection.

library("biomaRt")
listMarts() # To choose BioMart database
listMarts(host="plants.ensembl.org")
m <- useMart("plants_mart", host="plants.ensembl.org")
listDatasets(m)
m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="plants.ensembl.org")
listAttributes(m) # Choose data types you want to download
go <- getBM(attributes=c("go_id", "tair_locus", "namespace_1003"), mart=m)
go <- go[go[,3]!="",]; go[,3] <- as.character(go[,3])
go[go[,3]=="molecular_function", 3] <- "F"; go[go[,3]=="biological_process", 3] <- "P"; go[go[,3]=="cellular_component", 3] <- "C"
go[1:4,]
dir.create("./data/GO")
write.table(go, "data/GO/GOannotationsBiomart_mod.txt", quote=FALSE, row.names=FALSE, col.names=FALSE, sep="\t")
catdb <- makeCATdb(myfile="data/GO/GOannotationsBiomart_mod.txt", lib=NULL, org="", colno=c(1,2,3), idconv=NULL)
save(catdb, file="data/GO/catdb.RData")

Batch GO term enrichment analysis

Apply the enrichment analysis to the DEG sets obtained in the above differential expression analysis. Note, in the following example the FDR filter is set here to an unreasonably high value, simply because of the small size of the toy data set used in this vignette. Batch enrichment analysis of many gene sets is performed with the GOCluster_Report function. When method="all", it returns all GO terms passing the p-value cutoff specified under the cutoff arguments. When method="slim", it returns only the GO terms specified under the myslimv argument. The given example shows how one can obtain such a GO slim vector from BioMart for a specific organism.

load("data/GO/catdb.RData")
DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=50), plot=FALSE)
up_down <- DEG_list$UporDown; names(up_down) <- paste(names(up_down), "_up_down", sep="")
up <- DEG_list$Up; names(up) <- paste(names(up), "_up", sep="")
down <- DEG_list$Down; names(down) <- paste(names(down), "_down", sep="")
DEGlist <- c(up_down, up, down)
DEGlist <- DEGlist[sapply(DEGlist, length) > 0]
BatchResult <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="all", id_type="gene", CLSZ=2, cutoff=0.9, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL)
library("biomaRt")
m <- useMart("plants_mart", dataset="athaliana_eg_gene", host="plants.ensembl.org")
goslimvec <- as.character(getBM(attributes=c("goslim_goa_accession"), mart=m)[,1])
BatchResultslim <- GOCluster_Report(catdb=catdb, setlist=DEGlist, method="slim", id_type="gene", myslimv=goslimvec, CLSZ=10, cutoff=0.01, gocats=c("MF", "BP", "CC"), recordSpecGO=NULL)

Plot batch GO term results

The data.frame generated by GOCluster_Report can be plotted with the goBarplot function. Because of the variable size of the sample sets, it may not always be desirable to show the results from different DEG sets in the same bar plot. Plotting single sample sets is achieved by subsetting the input data frame as shown in the first line of the following example.

gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$CLID), ]
gos <- BatchResultslim
pdf("GOslimbarplotMF.pdf", height=8, width=10); goBarplot(gos, gocat="MF"); dev.off()
goBarplot(gos, gocat="BP")
goBarplot(gos, gocat="CC")

Figure 10: GO Slim Barplot for MF Ontology.

Clustering and heat maps

The following example performs hierarchical clustering on the rlog transformed expression matrix subsetted by the DEGs identified in the above differential expression analysis. It uses a Pearson correlation-based distance measure and complete linkage for cluster join.

library(pheatmap)
geneids <- unique(as.character(unlist(DEG_list[[1]])))
y <- assay(rlog(dds))[geneids, ]
pdf("heatmap1.pdf")
pheatmap(y, scale="row", clustering_distance_rows="correlation", clustering_distance_cols="correlation")
dev.off()

Figure 11: Heat map with hierarchical clustering dendrograms of DEGs.

Workflow templates

The intended way of running systemPipeR workflows is via *.Rmd files, which can be executed either line-wise in interactive mode or with a single command from R or the command-line. This way comprehensive and reproducible analysis reports can be generated in PDF or HTML format in a fully automated manner by making use of the highly functional reporting utilities available for R. The following shows how to execute a workflow (e.g., systemPipeRNAseq.Rmd) from the command-line.

```{bash command-line, eval=FALSE} Rscript -e "rmarkdown::render('systemPipeRNAseq.Rmd')"

Templates for setting up custom project reports are provided as _`*.Rmd`_ files by the helper package _`systemPipeRdata`_ and in the vignettes subdirectory of _`systemPipeR`_. The corresponding HTML of these report templates are available here: [_`systemPipeRNAseq`_](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRNAseq.html), [_`systemPipeRIBOseq`_](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRIBOseq.html), [_`systemPipeChIPseq`_](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeChIPseq.html) and [_`systemPipeVARseq`_](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeVARseq.html). To work with _`*.Rnw`_ or _`*.Rmd`_ files efficiently, basic knowledge of [_`Sweave`_](https://www.stat.uni-muenchen.de/~leisch/Sweave/) or [_`knitr`_](http://yihui.name/knitr/) and [_`Latex`_](http://www.latex-project.org/) or [_`R Markdown v2`_](http://rmarkdown.rstudio.com/) is required. 

## RNA-Seq sample

Load the RNA-Seq sample workflow into your current working directory.

```r
library(systemPipeRdata)
genWorkenvir(workflow="rnaseq")
setwd("rnaseq")

Run workflow

Next, run the chosen sample workflow systemPipeRNAseq (PDF, Rmd) by executing from the command-line make -B within the rnaseq directory. Alternatively, one can run the code from the provided *.Rmd template file from within R interactively.

The workflow includes following steps:

1. Read preprocessing
   • Quality filtering (trimming)
   • FASTQ quality report
2. Alignments: Tophat2 (or any other RNA-Seq aligner)
3. Alignment stats
4. Read counting
5. Sample-wise correlation analysis
6. Analysis of differentially expressed genes (DEGs)
7. GO term enrichment analysis
8. Gene-wise clustering

ChIP-Seq sample

Load the ChIP-Seq sample workflow into your current working directory.

library(systemPipeRdata)
genWorkenvir(workflow="chipseq")
setwd("chipseq")

Run workflow

Next, run the chosen sample workflow systemPipeChIPseq_single (PDF, Rmd) by executing from the command-line make -B within the chipseq directory. Alternatively, one can run the code from the provided *.Rmd template file from within R interactively.

The workflow includes the following steps:

1. Read preprocessing
   • Quality filtering (trimming)
   • FASTQ quality report
2. Alignments: Bowtie2 or rsubread
3. Alignment stats
4. Peak calling: MACS2, BayesPeak
5. Peak annotation with genomic context
6. Differential binding analysis
7. GO term enrichment analysis
8. Motif analysis

VAR-Seq sample

VAR-Seq workflow for the single machine

Load the VAR-Seq sample workflow into your current working directory.

library(systemPipeRdata)
genWorkenvir(workflow="varseq")
setwd("varseq")

Run workflow

Next, run the chosen sample workflow systemPipeVARseq_single (PDF, Rmd) by executing from the command-line make -B within the varseq directory. Alternatively, one can run the code from the provided *.Rmd template file from within R interactively.

The workflow includes following steps:

1. Read preprocessing
   • Quality filtering (trimming)
   • FASTQ quality report
2. Alignments: gsnap, bwa
3. Variant calling: VariantTools, GATK, BCFtools
4. Variant filtering: VariantTools and VariantAnnotation
5. Variant annotation: VariantAnnotation
6. Combine results from many samples
7. Summary statistics of samples

VAR-Seq workflow for computer cluster

The workflow template provided for this step is called systemPipeVARseq.Rmd (PDF, Rmd). It runs the above VAR-Seq workflow in parallel on multiple compute nodes of an HPC system using Slurm as the scheduler.

Ribo-Seq sample

Load the Ribo-Seq sample workflow into your current working directory.

library(systemPipeRdata)
genWorkenvir(workflow="riboseq")
setwd("riboseq")

Run workflow

Next, run the chosen sample workflow systemPipeRIBOseq (PDF, Rmd) by executing from the command-line make -B within the ribseq directory. Alternatively, one can run the code from the provided *.Rmd template file from within R interactively.

The workflow includes following steps:

1. Read preprocessing
   • Adaptor trimming and quality filtering
   • FASTQ quality report
2. Alignments: Tophat2 (or any other RNA-Seq aligner)
3. Alignment stats
4. Compute read distribution across genomic features
5. Adding custom features to the workflow (e.g. uORFs)
6. Genomic read coverage along with transcripts
7. Read counting
8. Sample-wise correlation analysis
9. Analysis of differentially expressed genes (DEGs)
10. GO term enrichment analysis
11. Gene-wise clustering
12. Differential ribosome binding (translational efficiency)

Version information

Note: the most recent version of this tutorial can be found here.

sessionInfo()

Funding

This project is funded by NSF award ABI-1661152.

References

systemPipeR documentation built on Jan. 26, 2021, 2 a.m.