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tgirke committed Jul 28, 2024
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13 changes: 12 additions & 1 deletion vignettes/systemPipeR.Rmd
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Expand Up @@ -115,6 +115,16 @@ information in `targets` with CWL parameters are described
knitr::include_graphics("images/SPR_CWL_hello.png")
```

## Workflow templates
`systemPipeRdata`, a companion package to `systemPipeR`, offers a collection of
workflow templates that are ready to use. With a single command, users can
easily load these templates onto their systems. Once loaded, users have the
flexibility to utilize the templates as they are or modify them as needed. More
in-depth information can be found in the main vignette of systemPipeRdata,
which can be accessed
[here](https://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRdata.html).


## Other functionalities
<!-- _`systemPipeR's`_ CWL interface provides two
options to run command-line tools and workflows based on CWL. First, one can
Expand Down Expand Up @@ -167,7 +177,8 @@ The following demonstrates how to initialize, run and monitor workflows, and sub

__1. Create workflow environment.__ The chosen example uses the `genWorenvir` function from
the `systemPipeRdata` package to create an RNA-Seq workflow environment that is fully populated with a small test data set, including FASTQ files, reference genome and annotation data. After this, the user's R session needs to be directed
into the resulting `rnaseq` directory (here with `setwd`).
into the resulting `rnaseq` directory (here with `setwd`). A list of available workflow templates
is available in the vignette of the `systemPipeRdata` package [here](https://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRdata.html#wf-bioc-collection).

```{r eval=FALSE}
systemPipeRdata::genWorkenvir(workflow = "rnaseq")
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320 changes: 20 additions & 300 deletions vignettes/systemPipeR_workflows.Rmd
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---
title: "systemPipeR: Workflows collection"
author: "Author: Daniela Cassol ([email protected]) and Thomas Girke ([email protected])"
title: "systemPipeR: Workflow Templates"
author: "Author: Le Zhang, Daniela Cassol, and Thomas Girke"
date: "Last update: `r format(Sys.time(), '%d %B, %Y')`"
output:
BiocStyle::html_document:
Expand All @@ -10,12 +10,17 @@ output:
package: systemPipeR
vignette: |
%\VignetteEncoding{UTF-8}
%\VignetteIndexEntry{systemPipeR: Workflows collection}
%\VignetteIndexEntry{systemPipeR: Workflow Templates}
%\VignetteEngine{knitr::rmarkdown}
fontsize: 14pt
bibliography: bibtex.bib
---

<!--
- Compile from command-line
Rscript -e "rmarkdown::render('systemPipeR_workflows.Rmd', c('BiocStyle::html_document'), clean=F); knitr::knit('systemPipeR_workflows.Rmd', tangle=FALSE)"
-->

```{css, echo=FALSE}
pre code {
white-space: pre !important;
Expand All @@ -40,306 +45,21 @@ suppressPackageStartupMessages({
})
```

**Note:** the most recent version of this tutorial can be found <a href="http://www.bioconductor.org/packages/devel/bioc/vignettes/systemPipeR/inst/doc/systemPipeR.html">here</a>.

**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](https://doi.org/10.1186/s12859-016-1241-0).

# 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.

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 _`*.Rmd`_ files efficiently, basic knowledge of [_`knitr`_](http://yihui.name/knitr/) and [_`Latex`_](http://www.latex-project.org/) or [_`R Markdown v2`_](http://rmarkdown.rstudio.com/) is required.

## Directory Structure

```{r dir, eval=TRUE, echo=FALSE, out.width="100%", fig.align = "center", fig.cap= "*systemPipeR's* preconfigured directory structure."}
knitr::include_graphics(system.file("extdata/images", "spr_project.png", package = "systemPipeR"))
```

The working environment of the sample data loaded in the previous step contains
the following pre-configured directory structure. Directory names are indicated
in <span style="color:grey">***green***</span>. Users can change this
structure as needed, but need to adjust the code in their workflows
accordingly.

* <span style="color:green">_**workflow/**_</span> (*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.* <span style="color:green">_**rnaseq**_</span>, <span style="color:green">_**varseq**_</span>). Changing its name does not require any modifications in the run script(s).
+ **Important subdirectories**:
+ <span style="color:green">_**param/**_</span>
+ 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.
+ <span style="color:green">_**param/cwl/**_</span>: 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.
+ <span style="color:green">_**data/**_ </span>
+ FASTQ files
+ FASTA file of reference (*e.g.* reference genome)
+ Annotation files
+ etc.
+ <span style="color:green">_**results/**_</span>
+ 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.

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.

# RNA-Seq Workflow

This workflow demonstrates how to use various utilities for building and running automated end-to-end analysis workflows for _`RNA-Seq`_ data.

**The full workflow can be found here**:
[HTML](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRNAseq.html), [.Rmd](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRNAseq.Rmd), and [.R](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRNAseq.R).

## Loading package and workflow template

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

```{r genRna_workflow_single, eval=FALSE}
library(systemPipeRdata)
genWorkenvir(workflow="rnaseq")
setwd("rnaseq")
```

## Create the workflow

This template provides some common steps for a `RNAseq` workflow. One can add, remove, modify
workflow steps by operating on the `sal` object.

```{r project_rnaseq, eval=FALSE}
sal <- SPRproject()
sal <- importWF(sal, file_path = "systemPipeRNAseq.Rmd", verbose = FALSE)
```

**Workflow includes following steps:**

1. Read preprocessing
+ Quality filtering (trimming)
+ FASTQ quality report
2. Alignments: _`HISAT2`_ (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

## Run workflow

```{r run_rnaseq, eval=FALSE}
sal <- runWF(sal)
```

## Workflow visualization

```{r plot_rnaseq, eval=FALSE}
plotWF(sal)
```

## Report generation

```{r report_rnaseq, eval=FALSE}
sal <- renderReport(sal)
sal <- renderLogs(sal)
```

# ChIP-Seq Workflow

This workflow demonstrates how to use various utilities for building and running automated end-to-end analysis workflows for _`ChIP-Seq`_ data.

**The full workflow can be found here**: [HTML](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeChIPseq.html), [.Rmd](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeChIPseq.Rmd), and [.R](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeChIPseq.R).

## Loading package and workflow template

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

```{r genChip_workflow, eval=FALSE}
library(systemPipeRdata)
genWorkenvir(workflow="chipseq")
setwd("chipseq")
```

**Workflow includes following steps:**

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

## Create the workflow

This template provides some common steps for a `ChIPseq` workflow. One can add, remove, modify
workflow steps by operating on the `sal` object.

```{r project_chipseq, eval=FALSE}
sal <- SPRproject()
sal <- importWF(sal, file_path = "systemPipeChIPseq.Rmd", verbose = FALSE)
```

## Run workflow

```{r run_chipseq, eval=FALSE}
sal <- runWF(sal)
```

## Workflow visualization

```{r plot_chipseq, eval=FALSE}
plotWF(sal)
```

## Report generation

```{r report_chipseq, eval=FALSE}
sal <- renderReport(sal)
sal <- renderLogs(sal)
```

# VAR-Seq Workflow

This workflow demonstrates how to use various utilities for building and running automated end-to-end analysis workflows for _`VAR-Seq`_ data.

**The full workflow can be found here:** [HTML](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeVARseq.html), [.Rmd](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeVARseq.Rmd), and [.R](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeVARseq.R).

## Loading package and workflow template

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

```{r genVar_workflow_single, eval=FALSE}
library(systemPipeRdata)
genWorkenvir(workflow="varseq")
setwd("varseq")
```

**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

## Create the workflow

This template provides some common steps for a `VARseq` workflow. One can add, remove, modify
workflow steps by operating on the `sal` object.

```{r project_varseq, eval=FALSE}
sal <- SPRproject()
sal <- importWF(sal, file_path = "systemPipeVARseq.Rmd", verbose = FALSE)
```

## Run workflow

```{r run_varseq, eval=FALSE}
sal <- runWF(sal)
```

## Workflow visualization

```{r plot_varseq, eval=FALSE}
plotWF(sal)
```

## Report generation

```{r report_varseq, eval=FALSE}
sal <- renderReport(sal)
sal <- renderLogs(sal)
```

# Ribo-Seq Workflow

This workflow demonstrates how to use various utilities for building and running automated end-to-end analysis workflows for _`RIBO-Seq`_ data.

**The full workflow can be found here:**
[HTML](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRIBOseq.html), [.Rmd](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRIBOseq.Rmd), and [.R](http://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRIBOseq.R).

## Loading package and workflow template
# Redirect notification

Load the _`RIBO-Seq`_ sample workflow into your current working directory.
The
[systemPipeRdata](https://www.bioconductor.org/packages/devel/data/experiment/html/systemPipeRdata.html)
package provides a collection of pre-built workflow templates that are ready to
use from
[systemPipeR](https://www.bioconductor.org/packages/devel/bioc/html/systemPipeR.html).
These templates are described in detail in the associated `systemPipeRdata`
overview vignette
[here](https://www.bioconductor.org/packages/devel/data/experiment/vignettes/systemPipeRdata/inst/doc/systemPipeRdata.html),
which includes instructions on how to use them.

```{r genRibo_workflow_single, eval=FALSE}
library(systemPipeRdata)
genWorkenvir(workflow="riboseq")
setwd("riboseq")
```

**Workflow includes following steps:**

1. Read preprocessing
+ Adaptor trimming and quality filtering
+ FASTQ quality report
2. Alignments: _`HISAT2`_ (or any other RNA-Seq aligner)
3. Alignment stats
4. Compute read distribution across genomic features
5. Adding custom features to workflow (e.g. uORFs)
6. Genomic read coverage along 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)

This template provides some common steps for a `RIBOseq` workflow. One can add, remove, modify
workflow steps by operating on the `sal` object.

```{r project_riboseq, eval=FALSE}
sal <- SPRproject()
sal <- importWF(sal, file_path = "systemPipeRIBOseq.Rmd", verbose = FALSE)
```

## Run workflow

```{r run_riboseq, eval=FALSE}
sal <- runWF(sal)
```

## Workflow visualization

```{r plot_riboseq, eval=FALSE}
plotWF(sal)
```

## Report generation

```{r report_riboseq, eval=FALSE}
sal <- renderReport(sal)
sal <- renderLogs(sal)
```

# Version information

```{r sessionInfo}
sessionInfo()
```

# Funding

This project is funded by NSF award [ABI-1661152](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661152).
This project is funded by awards from the National Science Foundation ([ABI-1661152](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661152)],
and the National Institute on Aging of the National Institutes of Health ([U19AG023122](https://reporter.nih.gov/project-details/9632486)).

# References

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