Partek
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      • Creating and Analyzing a Project
        • Creating a New Project
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      • Analyzing Single Cell RNA-Seq Data
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        • Start with pre-processed Space Ranger output files
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      • Single Cell RNA-Seq Analysis (Multiple Samples)
        • Getting started with the tutorial data set
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          • Troubleshooting
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        • Variant Callers
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          • Annotate Variants
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        • Copy Number Analysis (CNVkit)
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        • Export
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      • Data Viewer
      • Visualizations
        • Chromosome View
          • Launching the Chromosome View
          • Navigating Through the View
          • Selecting Data Tracks for Visualization
          • Visualizing the Results Using Data Tracks
          • Annotating the Results
          • Customizing the View
        • Dot Plot
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        • Sources of variation plot
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        • Correlation Plot
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          • REST API Command List
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      • How to select the best single cell quality control thresholds
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      • Detecting differential gene expression in single cell RNA-Seq analysis
      • Batch remover for single cell data
      • How to perform single cell RNA sequencing: exploratory analysis
      • Single Cell Multiomics Analysis: Strategies for Integration
      • Pathway Analysis: ANOVA vs. Enrichment Analysis
      • Studying Immunotherapy with Multiomics: Simultaneous Measurement of Gene and Protein
      • How to Integrate ChIP-Seq and RNA-Seq Data
      • Enjoy Responsibly!
      • To Boldly Go…
      • Get to Know Your Cell
      • Aliens Among Us: How I Analyzed Non-Model Organism Data in Partek Flow
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    • Release Notes
      • Release Notes Archive - Partek Flow 10
  • Partek Genomics Suite
    • Installation Guide
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      • Computer Host ID Retrieval
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      • Lists
        • Importing a text file list
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        • Starting with a list of genomic regions
        • Starting with a list of SNPs
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        • Additional options for lists
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      • Visualizing NGS Data
      • Chromosome View
      • Methylation Workflows
      • Trio/Duo Analysis
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      • LOH detection with an allele ratio spreadsheet
      • Import data from Agilent feature extraction software
      • Illumina GenomeStudio Plugin
        • Import gene expression data
        • Import Genotype Data
        • Export CNV data to Illumina GenomeStudio using Partek report plug-in
        • Import data from Illumina GenomeStudio using Partek plug-in
        • Export methylation data to Illumina GenomeStudio using Partek report plug-in
    • Tutorials
      • Gene Expression Analysis
        • Importing Affymetrix CEL files
        • Adding sample information
        • Exploring gene expression data
        • Identifying differentially expressed genes using ANOVA
        • Creating gene lists from ANOVA results
        • Performing hierarchical clustering
        • Adding gene annotations
      • Gene Expression Analysis with Batch Effects
        • Importing the data set
        • Adding an annotation link
        • Exploring the data set with PCA
        • Detect differentially expressed genes with ANOVA
        • Removing batch effects
        • Creating a gene list using the Venn Diagram
        • Hierarchical clustering using a gene list
        • GO enrichment using a gene list
      • Differential Methylation Analysis
        • Import and normalize methylation data
        • Annotate samples
        • Perform data quality analysis and quality control
        • Detect differentially methylated loci
        • Create a marker list
        • Filter loci with the interactive filter
        • Obtain methylation signatures
        • Visualize methylation at each locus
        • Perform gene set and pathway analysis
        • Detect differentially methylated CpG islands
        • Optional: Add UCSC CpG island annotations
        • Optional: Use MethylationEPIC for CNV analysis
        • Optional: Import a Partek Project from Genome Studio
      • Partek Pathway
        • Performing pathway enrichment
        • Analyzing pathway enrichment in Partek Genomics Suite
        • Analyzing pathway enrichment in Partek Pathway
      • Gene Ontology Enrichment
        • Open a zipped project
        • Perform GO enrichment analysis
      • RNA-Seq Analysis
        • Importing aligned reads
        • Adding sample attributes
        • RNA-Seq mRNA quantification
        • Detecting differential expression in RNA-Seq data
        • Creating a gene list with advanced options
        • Visualizing mapped reads with Chromosome View
        • Visualizing differential isoform expression
        • Gene Ontology (GO) Enrichment
        • Analyzing the unexplained regions spreadsheet
      • ChIP-Seq Analysis
        • Importing ChIP-Seq data
        • Quality control for ChIP-Seq samples
        • Detecting peaks and enriched regions in ChIP-Seq data
        • Creating a list of enriched regions
        • Identifying novel and known motifs
        • Finding nearest genomic features
        • Visualizing reads and enriched regions
      • Survival Analysis
        • Kaplan-Meier Survival Analysis
        • Cox Regression Analysis
      • Model Selection Tool
      • Copy Number Analysis
        • Importing Copy Number Data
        • Exploring the data with PCA
        • Creating Copy Number from Allele Intensities
        • Detecting regions with copy number variation
        • Creating a list of regions
        • Finding genes with copy number variation
        • Optional: Additional options for annotating regions
        • Optional: GC wave correction for Affymetrix CEL files
        • Optional: Integrating copy number with LOH and AsCN
      • Loss of Heterozygosity
      • Allele Specific Copy Number
      • Gene Expression - Aging Study
      • miRNA Expression and Integration with Gene Expression
        • Analyze differentially expressed miRNAs
        • Integrate miRNA and Gene Expression data
      • Promoter Tiling Array
      • Human Exon Array
        • Importing Human Exon Array
        • Gene-level Analysis of Exon Array
        • Alt-Splicing Analysis of Exon Array
      • NCBI GEO Importer
    • Webinars
    • White Papers
      • Allele Intensity Import
      • Allele-Specific Copy Number
      • Calculating Genotype Likelihoods
      • ChIP-Seq Peak Detection
      • Detect Regions of Significance
      • Genomic Segmentation
      • Loss of Heterozygosity Analysis
      • Motif Discovery Methods
      • Partek Genomics Suite Security
      • Reads in RNA-Seq
      • RNA-Seq Methods
      • Unpaired Copy Number Estimation
    • Release Notes
    • Version Updates
    • TeamViewer Instructions
  • Getting Help
    • TeamViewer Instructions
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On this page
  • Generate an authentication token
  • Create a project
  • Upload a group of samples
  • Assign sample attributes
  • Run a pipeline
  • Add a collaborator to a project
  • Transfer feature lists
  • Monitor a folder and upload files as they are created
  • Monitor the queue and send a notification if there are too many waiting tasks
  • Additional Assistance
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  1. Partek Flow
  2. User Manual
  3. Enterprise Features and Toolkits

REST API

PreviousEnterprise Features and ToolkitsNextREST API Command List

Last updated 8 days ago

With the Partek Flow REST API, you can create custom solutions to query or drive your server. Below are some common use cases for the REST API:

A complete reference for the API can be found on the or by visiting [server]/api/v1/servlets

The referenced Python library can be downloaded .

Generate an authentication token

An access token can be generated from the System information section of the settings page.

Alternatively, GetToken.py will generate a token:

python GetToken.py --server localhost:8080 --user admin

you will be prompted to enter your password.

This token can be specified as the token parameter.

curl --form token=cUOWY0VvkSFagr... http://localhost:8080/flow/api/v1/users/list

Create a project

Flow organizes data by projects and they can be created and managed by the REST API.

To create a project:

curl -X POST --form token=$FLOW_TOKEN --form project="My Project" http://localhost:8080/flow/api/v1/projects

The server will respond with JSON data describing the new project:

{"name":"My Project","id":"0","description":"","owner":"0","userRoles":{"0":"Project owner"},"outputFolders":{"0":"/home/flow/FlowData/Project_My Project"},"diskUsage":"0 GB","lastModifiedTimeStamp":1506013662476,"lastModifiedDate":"12:00 PM","data":[]}

The new project will appear on the Flow homepage:

Upload a group of samples

UploadSamples.py is a python script that can create samples within a project by uploading files:

python UploadSamples.py --verbose --token $FLOW_TOKEN --server http://localhost:8080 --project "My Project" --files ~/MoreData/REST/sample1.fastq.gz ~/MoreData/REST/sample2.fastq.gz ~/MoreData/REST/sample3.fastq.gz ~/MoreData/REST/sample4.fastq.gz

This operation will generate a data node on the Analyses tab for the imported samples:

Assign sample attributes

We can associate attributes with samples for use in visualizations and statistical analysis:

python AddAttribute.py -v --server http://localhost:8080 --token $FLOW_TOKEN --project_name "My Project" --sample_name sample1 --attribute Type --value Case
python AddAttribute.py -v --server http://localhost:8080 --token $FLOW_TOKEN --project_name "My Project" --sample_name sample2 --attribute Type --value Case
python AddAttribute.py -v --server http://localhost:8080 --token $FLOW_TOKEN --project_name "My Project" --sample_name sample3 --attribute Type --value Control
python AddAttribute.py -v --server http://localhost:8080 --token $FLOW_TOKEN --project_name "My Project" --sample_name sample4 --attribute Type --value Control

The sample attributes can be viewed and managed on the data tab:

Run a pipeline

To run a pipeline, first we need to know its name.

We can get the name of a pipeline from the GUI or from the API:

wget -q -O - http://localhost:8080/flow/api/v1/pipelines/list$AUTHDETAILS | python -m json.tool | gvim -

Many pipelines also require that library files are specified.

You can get the list of required inputs for the pipeline from the API:

This particular pipeline requires a bowtie index and an annotation model:

The request to launch the pipeline needs to specify one resource ID for each input.

These IDs can be found using the API:

Get the IDs for the library files that match the required inputs

wget -q -O - "http://localhost:8080/flow/api/v1/library_files/list${AUTHDETAILS}&assembly=hg19" |  python -m json.tool | gvim -
[
    {
        "annotationModel": "",
        "assembly": "hg19",
        "description": "Reference sequence",
        "fileType": "Genome sequence",
        "id": 100
    },
    {
        "annotationModel": "",
        "assembly": "hg19",
        "description": "Cytoband",
        "fileType": "cytoBand.txt",
        "id": 101
    },
    {
        "annotationModel": "",
        "assembly": "hg19",
        "description": "Bowtie index",
        "fileType": "Bowtie Index",
        "id": 102
    },
    {
        "annotationModel": "hg19_refseq_15_05_07_v2",
        "assembly": "hg19",
        "description": "Annotation file: hg19_refseq_15_05_07_v2",
        "fileType": "Annotation model",
        "id": 103
    }
]

The pipeline can be launched in any project using RunPython.py

python RunPipeline.py  -v --server http://localhost:8080 --token $FLOW_TOKEN --project_id 0 --pipeline AlignAndQuantify --inputs 102,103

This action will cause two tasks to start running:

Alternatively, UploadSamples.py can create the project, upload the samples and launch the pipeline in one step:

python UploadSamples.py -v --server http://localhost:8080 --token $FLOW_TOKEN --files ~/sampleA.fastq.gz ~/sampleB.fastq.gz --project NewProject --pipeline AlignAndQuantify --inputs 102,103

Add a collaborator to a project

To add a collaborator to a project:

curl -X PUT "http://localhost:8080/flow/api/v1/projects?project=ProjectName&collaborator=user1&role=Collaborator&token=$FLOW_TOKEN"

Transfer feature lists

curl --form token=$TO_TOKEN --form url=http://from:8080/flow/api/v1/feature_lists/export?token=$FROM_TOKEN  http://to:8080/flow/api/v1/feature_lists/import

Monitor a folder and upload files as they are created

#!/bin/bash
inotifywait -m $PATH_TO_MONITOR -e create -e moved_to |
  while read path action file; do
      if [[ $file == *.fastq.gz ]]; then
              echo "Uploading $file"
              python UploadSamples.py -v --server $SERVER --token $FLOW_TOKEN --files $path/$file --project "$PROJECT"
      fi
  done

Monitor the queue and send a notification if there are too many waiting tasks

#!/bin/bash
while true; do
      result=`python QueueStatistics.py --server $SERVER --token $TOKEN --max_waiting $MAX_WAITING`
      if [ $? -eq 1 ]; then
              /usr/bin/notify-send $result
              exit 1
      fi
      sleep $INTERVAL
done

Additional Assistance

A pipeline is a series of tasks used to process and analyze genomic data. You can read more about pipelines

If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.

here
http://localhost:8080/flow/api/v1/pipelines/inputs?project_id=0&pipeline=AlignAndQuantify
our support page
REST API Command List
here
Generate an authentication token
Create a project
Upload a group of samples
Assign sample attributes
Run a pipeline
Add a collaborator to a project
Transfer feature lists
Monitor a folder and upload files as they are created
Monitor the queue and send a notification if there are too many waiting tasks