1 Introduction

This guide will get you started to use Seven Bridges API with the R client package sbgr, and guide you through the steps needed to run the FastQC Analysis pipeline, a simple public pipeline on the Seven Bridges Genomics platform.

The following primary steps will be included:

• uploading files and setting the metadata;
• running the FastQC pipeline;
• downloading the results.

2 Preparatory works

source("http://bioconductor.org/biocLite.R")
biocLite("sbgr")

source("http://bioconductor.org/biocLite.R")
useDevel(devel = TRUE)
biocLite("sbgr")
useDevel(devel = FALSE)

# install.packages("devtools") if devtools was not installed
library("devtools")
install_github("road2stat/sbgr")

library("sbgr")

token = misc_get_auth_token()

## Enter the generated authentication token:
## 1:

print(token)

## [1] "58aeb140197001306386001f5b34aa78"

3 Create projects

Pipelines need to be executed within projects. We can either use a project that has already been created, or we can use the API to create a new one. Let’s create one from scratch. We do this using the function project_new(). The function requires a name for the project (we will use the name 'API tutorial') and a billing group, which could be retrived by the function billing(). You can also use the optional parameter description in project_new() to set the project description.

# Get the first billing group that we have access to
# and use it for the new project
billing_group_id = billing(token)[["items"]][[1]][["id"]]

# Create new project
sbgr_project = project_new(token, name = "sbgr tutorial",
                           description = "sbgr tutorial project",
                           billing_group_id = billing_group_id)

# The list sbgr_project now contains the ID of the new project,
# which we will use in the subsequent calls
sbgr_project_id = sbgr_project[["id"]]
print(sbgr_project_id)

## [1] "dbf274a0-6dc5-4453-be87-b16965add1aa"

The created project could be seen on the Seven Bridges platform.

4 Get project details

We will need the ID of a project to edit the file metatdata and execute pipelines. We can get the details of a project by using project_details().

project_details(token, project_id = sbgr_project_id)

$id
[1] "dbf274a0-6dc5-4453-be87-b16965add1aa"

$name
[1] "sbgr tutorial"

$description
[1] "sbgr tutorial project"

$my_permissions
$my_permissions$write
[1] TRUE

$my_permissions$copy
[1] TRUE

$my_permissions$execute
[1] TRUE

$my_permissions$admin
[1] TRUE

The returned list contains the details of the project that you can access with your auth token. Each project is listed with its ID number, name, description, and your detailed permissions to access the project.

5 Upload files and set file metadata

Download the archive of sample data files sample1.tgz and assume we have extracted the files to the current R working directory. We will upload the extracted file sample1.fastq in this example.

misc_get_uploader('~/')  # download the SBG CLI uploader to home directory

file_id = misc_upload_cli(token, project_id = sbgr_project_id,
                          file = "sample1.fastq", uploader = '~/sbg-uploader/')
# suppose we have extracted sample1.fastq into current R working directory

print(file_id)

## [1] "559c41ebe4b0566fd159709a"

file_list(token, project_id = sbgr_project_id)

## $items
## $items[[1]]
## $items[[1]]$id
## [1] "559c41ebe4b0566fd159709a"
##
## $items[[1]]$name
## [1] "sample1.fastq"
##
## $items[[1]]$size
## [1] 16
##
## $items[[1]]$origin
## $items[[1]]$origin$upload_name
## [1] "1436362037099"
##
##
## $items[[1]]$metadata
## $items[[1]]$metadata$file_type
## [1] "fastq"
##
## $items[[1]]$metadata$seq_tech
## [1] "illumina"
##
## $items[[1]]$metadata$sample
## [1] "sample1"

file_meta_update(token, project_id = sbgr_project_id,
                 file_id    = file_id,
                 file_type  = "fastq", seq_tech = "Illumina",
                 qual_scale = "illumina18",
                 sample     = "example_human_Illumina",
                 library    = "Test", paired_end = "1")

## $id
## [1] "559c41ebe4b0566fd159709a"
##
## $name
## [1] "sample1.fastq"
##
## $size
## [1] 16
##
## $origin
## $origin$upload_name
## [1] "1436361569553"
##
##
## $metadata
## $metadata$file_type
## [1] "fastq"
##
## $metadata$qual_scale
## [1] "illumina18"
##
## $metadata$seq_tech
## [1] "Illumina"
##
## $metadata$sample
## [1] "example_human_Illumina"
##
## $metadata$library
## [1] "Test"
##
## $metadata$paired_end
## [1] "1"

6 List public pipelines and add pipelines to project

We want to run the FastQC pipeline in our project, so we will need its ID number to identify it. Since the FastQC pipeline is available in the Seven Bridges public pipelines repository, we will now list all of these pipelines along with their IDs to find the ID that we need. We can list all public pipelines via pipeline_list_pub():

pipeline_list = pipeline_list_pub(token)
# convert public pipeline list to a matrix
pipeline_mat = matrix(unlist(pipeline_list$items), ncol = 2, byrow = TRUE)
print(pipeline_mat)

## [,1]                       [,2]
## [1,] "534522d3d79f0049c0c9444d" "Targeted Capture Analysis - BWA + GATK 2.3.9-Lite (with Metrics)"
## [2,] "534522f6d79f0049c0c9444e" "Whole Exome Analysis - BWA + GATK 2.3.9-Lite (with Metrics)"
## [3,] "5345230dd79f0049c0c9444f" "Whole Genome Analysis - BWA + GATK 2.3.9-Lite (with Metrics)"
## [4,] "534521e5d79f0049c0c94445" "RNA-Seq Alignment for Ion Proton - TopHat + Bowtie 2"
## [5,] "53452154d79f0049c0c94442" "RNA-Seq Alignment - TopHat"
## [6,] "546ba1bbd79f00701cb276a6" "Cellular Research Precise Analysis Pipeline"
## [7,] "5392689dd79f007259672c79" "Amplicon Experiment QC"
## [8,] "53927c20d79f007259672c8e" "Amplicon Experiment QC - AmpliSeq Exomes"
## [9,] "534521d1d79f0049c0c94444" "RNA-Seq Alignment for Ion Proton - STAR + Bowtie 2"
## [10,] "53452130d79f0049c0c94441" "RNA-Seq Alignment - STAR"
## [11,] "53452273d79f0049c0c94448" "RNA-Seq Differential Expression - Cuffdiff (with Visualization)"
## [12,] "540dd19dd79f00766c174ead" "Fusion Transcript Detection - ChimeraScan"
## [13,] "5346ba3dd79f0049c0c944a6" "RNA-Seq De Novo Assembly - Trinity"
## [14,] "540dd2fad79f00766c174eb0" "Fusion Transcript Detection - STAR + Chimera"
## [15,] "534521f5d79f0049c0c94446" "RNA-Seq De Novo Assembly and Analysis - Trinity"
## [16,] "534520eed79f0049c0c9443a" "FastQC Analysis"
## [17,] "5345222cd79f0049c0c94447" "RNA-Seq Differential Expression - Cuffdiff"
## [18,] "53b2e456d79f004c55605245" "Trio analysis pipeline (Whole Exome)"
## [19,] "53b2d73bd79f004c5560523d" "Trio analysis pipeline (Whole Genome)"
## [20,] "53b2a2f0d79f004c55605227" "Illumina BAM to FASTQ"
## [21,] "534522b0d79f0049c0c9444b" "Stanford HugeSeq WGS - from Aligned Reads (BAM)"
## [22,] "53452dd2d79f0049c0c94459" "Alignment Metrics QC"
## [23,] "534522c4d79f0049c0c9444c" "Stanford HugeSeq WGS - from Unaligned Reads (FASTQ)"
## [24,] "53452299d79f0049c0c9444a" "Stanford HugeSeq WGS - Structural Variation Only"
## [25,] "53452289d79f0049c0c94449" "Stanford HugeSeq WGS - Skip Alignment"
## [26,] "5345210fd79f0049c0c94440" "On-target variant selection and QC"
## [27,] "534520ffd79f0049c0c9443b" "Merge FASTQ Files"
## [28,] "534520dad79f0049c0c94439" "Exome Variant Quality Score Recalibration - GATK 2.3.9-Lite"
## [29,] "534520c9d79f0049c0c94438" "Exome SNP calling Ion Torrent - BWA - MEM + GATK 2.3.9-Lite (with Metrics)"
## [30,] "534520b5d79f0049c0c94437" "Exome SNP Calling - Mosaik + FreeBayes"
## [31,] "534520a5d79f0049c0c94432" "Exome Coverage QC"
## [32,] "53452096d79f0049c0c94431" "Exome Analysis - BWA + GATK 1.6"
## [33,] "53452077d79f0049c0c94430" "Convert SFF files to FASTQ files"
## [34,] "5345205cd79f0049c0c9442f" "Convert SAM/BAM to FASTQ"

The pipeline we want is “FastQC Analysis”, so we will copy it to our project using the function pipeline_add():

# Get the FastQC pipeline ID number in pipeline_mat
pipeline_add(token, project_id_to = sbgr_project_id,
             pipeline_id =
               pipeline_mat[which(pipeline_mat[, 2] == "FastQC Analysis"), 1])

This call returns the details of the copied pipeline.

## $id
## [1] "559c3848896a5d236e19d4a2"
##
## $name
## [1] "FastQC Analysis"
##
## $description
## [1] "The FastQC tool, developed by the Babraham Institute, analyzes sequence data from FASTQ, BAM, or SAM files. It produces a set of metrics and charts that help identify technical problems with the data. It's a good idea to run this pipeline on files you receive from a sequencer or a collaborator to get a general idea of how well the sequencing experiment went. Results from this pipeline can inform if and how you should proceed with your analysis."
##
## $revision
## [1] "0"

The added pipeline could also be seen in the project we created on the Seven Bridges platform.

Note that the pipeline now has a new ID number. This identifies the pipeline within your project. The ID of a pipeline when listed in the public repository is different from its ID when listed inside your project.

7 Get pipeline details and find input nodes

Assume we have cloned the FastQC pipeline into our project via the above code or via the SBG platform. Now we need the new ID number of the FastQC pipeline in our project, so we can execute it via the API. To get the details of all available pipelines in your project, use pipeline_list_project():

project_pipeline = pipeline_list_project(token, project_id = sbgr_project_id)
print(project_pipeline)

This will return a list of the pipelines in the specified project:

## $items
## $items[[1]]
## $items[[1]]$id
## [1] "559c3848896a5d236e19d4a2"
##
## $items[[1]]$name
## [1] "FastQC Analysis"

Now we can extract the ID number of the pipeline:

pipeline_id = project_pipeline[["items"]][[1]][["id"]]

To input the file we uploaded to a pipeline, we have to get some more of the pipeline’s details. In particular, we will need to get the ID number of its input nodes. We can list the full details of our chosen pipeline inside the project using the function pipeline_details():

fastqc_details = pipeline_details(token, project_id = sbgr_project_id,
                 pipeline_id = pipeline_id)
print(fastqc_details)

The returned list contains the information of the pipeline. We can see that our chosen pipeline contains a single input node with ID 177252:

## $id
## [1] "559c3848896a5d236e19d4a2"
##
## $name
## [1] "FastQC Analysis"
##
## $description
## [1] "The FastQC tool, developed by the Babraham Institute, analyzes sequence data from FASTQ, BAM, or SAM files. It produces a set of metrics and charts that help identify technical problems with the data. It's a good idea to run this pipeline on files you receive from a sequencer or a collaborator to get a general idea of how well the sequencing experiment went. Results from this pipeline can inform if and how you should proceed with your analysis."
##
## $revision
## [1] "0"
##
## $inputs
## $inputs[[1]]
## $inputs[[1]]$id
## [1] 177252
##
## $inputs[[1]]$name
## [1] "FASTQ Reads"
##
## $inputs[[1]]$required
## [1] TRUE
##
## $inputs[[1]]$accepts_list
## [1] FALSE
##
## $inputs[[1]]$file_types
## $inputs[[1]]$file_types[[1]]
## [1] "bam"
##
## $inputs[[1]]$file_types[[2]]
## [1] "fastq"
##
## $inputs[[1]]$file_types[[3]]
## [1] "sam"
##
##
##
##
## $nodes
## $nodes[[1]]
## $nodes[[1]]$id
## [1] 680142
##
## $nodes[[1]]$name
## [1] "FastQC"
##
## $nodes[[1]]$parameters
## list()
##
##
##
## $outputs
## $outputs[[1]]
## $outputs[[1]]$id
## [1] 556132
##
## $outputs[[1]]$name
## [1] "FastQC Reports Archive"
##
##
## $outputs[[2]]
## $outputs[[2]]$id
## [1] 1274412
##
## $outputs[[2]]$name
## [1] "FastQC Charts"

This call returns a list of information about the FastQC pipeline. In particular, it gives the following information:

• id: The ID number of the pipeline, used by the API to uniquely refer to it
• revision: The revision of the pipeline
• name: The human-readable name of the pipeline
• description: A short description of what the pipeline’s function
• inputs: An array listing the pipeline’s input nodes, i.e. those pipeline apps which require files as input
• nodes: An array of intermediary nodes, representing the pipeline apps which transform the data. These may have editable parameters that should be set before running the pipeline, although in this particular pipeline example no parameters are editable.
• output: An array of output nodes, representing apps that produce files, reports, visualizations, etc.

We will need to pay special attention to the sub-string that is a list of key-value pairs marked inputs for pipeline input nodes. Under each input node is a list of suggested files. These are the publicly available files that Seven Bridges Genomics recommends providing as input. Typically these are reference files, such as reference genomes, SNP databases, known indels, etc.

By inspecting the inputs part of the response to the call we just made, we can see the names and ID numbers of the suggested files for each input app. Notice that there is an app named FASTQ, which takes fastq files as input. This will be where we input the file sample1.fastq we want to analyze. We could extract and store the input id as input_id:

input_id = fastqc_details$inputs[[1]]$id

8 Run the pipeline

Now we have collected all the necessary data to run the pipeline:

We can then run the pipeline task using task_run() to execute a task:

# put input_id and file_id into list
inputs = list(list(file_id))
names(inputs) = input_id

# put the task info into a list
task = list(
  "name" = "FastQC with sbgr",
  "description" = "FastQC task with sbgr",
  "pipeline_id" = pipeline_id,
  "inputs" = inputs
  )

# add and run the task
fastqc_task = task_run(token, project_id = sbgr_project_id, task_details = task)
print(fastqc_task)

If the request is well-constructed and contains valid IDs, it will return details of the newly-created task as a response:

## $id
## [1] "2bfb18cc-f969-43a1-af75-8d63c1e0f54a"
##
## $name
## [1] "FastQC with sbgr"
##
## $description
## [1] "FastQC task with sbgr"
##
## $pipeline_id
## [1] "559c3848896a5d236e19d4a2"
##
## $pipeline_revision
## [1] "0"
##
## $start_time
## [1] 1.436304e+12
##
## $status
## $status$status
## [1] "active"
##
##
## $inputs
## $inputs$`177252`
## $inputs$`177252`[[1]]
## [1] ":559c41ebe4b0566fd159709a"
##
##
##
## $parameters
## $parameters$`680142`
## $parameters$`680142`$files
## NULL
##
## $parameters$`680142`$value
## named list()
##
##
##
## $outputs
## $outputs$`556132`
## list()
##
## $outputs$`1274412`
## list()

9 Monitor the task and download the results

Tasks typically take a long time to finish. We can periodically (say, every 30 seconds) check the status of a task by using the function task_details():

while (TRUE) {
  task_running = task_details(token, project_id = sbgr_project_id,
                              task_id = fastqc_task$id)
  cat("Running FastQC task...\n")
  if (task_running[["status"]][["status"]] != "active") break
  Sys.sleep(30)
}

print(task_running)

Usually after running for several minutes, the task should be finished, and you will receive emails when the task is finished. Alternatively, you could also monitor the task status in your dashboard on the SBG website. The output would contain the task information:

## $id
## [1] "4bf0a677-cc90-4eb8-bcb7-5759adc36449"
##
## $name
## [1] "FastQC with sbgr"
##
## $description
## [1] "FastQC task with sbgr"
##
## $pipeline_id
## [1] "559c3848896a5d236e19d4a2"
##
## $pipeline_revision
## [1] "0"
##
## $start_time
## [1] 1.43629e+12
##
## $status
## $status$status
## [1] "completed"
##
## $status$message
## [1] "Completed."
##
##
## $inputs
## $inputs$`177252`
## $inputs$`177252`[[1]]
## [1] ":559c41ebe4b0566fd159709a"
##
##
##
## $parameters
## $parameters$`680142`
## $parameters$`680142`$files
## NULL
##
## $parameters$`680142`$value
## named list()
##
##
##
## $outputs
## $outputs$`556132`
## $outputs$`556132`[[1]]
## [1] "559c44dbe4b07462e814cb7c"
##
##
## $outputs$`1274412`
## $outputs$`1274412`[[1]]
## [1] "559c44dbe4b0566fd1597562"

Once a task has finished, you can locate and download its output files. You will need an external program like wget to perform the download. We use the function file_download_url() to return the download URLs of the output file (for example, the first output file):

report_url = file_download_url(token, project_id = sbgr_project_id,
                               file_id = task_running[["outputs"]][[1]][[1]])
download.file(url = report_url[["url"]], destfile = "fastqc_report.zip",
              method = "wget")  # download the output archive using wget
untar("fastqc_report.zip")  # extract the zipped files
browseURL("sample1_fastqc/fastqc_report.html")  # open html report in browser

10 Additional resources

The SBG Developer Hub provides API Documentation, quickstart guide, SDK tutorials and additional information on developing your own applications using the Seven Bridges platform.

For a related example of running a series of tasks using a fixed pipeline and parameters, download the archive sbg_api_example.tar.gz which contains sample files and a full worked example in Python.

11 Acknowledgement

This documentation is based on the original Python version of the API documentation and tutorials. We gratefully appreciate the support and excellent job from the team at Seven Bridges Genomics Inc.

© Seven Bridges Genomics 2012 - 2015