PDXNet Workflows

Workflows on the Cancer Genomics Cloud

The PDX Coordination Center implemented standardized data processing workflows for all data uploaded by PDXNet researchers to the Cancer Genomics Cloud. The workflows are implemented in Common Workflow Language to facilitate portability. In the renewal, the PDCCC plan is to create a Nextflow version of the workflows.

We list a selection of workflows below. For an up-to-date list of available PDX workflows, please visit the Cancer Genomics Cloud Public App gallery.

Requirements

Required WES files and metadata for computation:

1. WES Target bed file or capture array information (e.g., Agilent v5, Roche)

2. Sample ID (e.g., sequencing sample ID, passaged PDX ID)

3. Case ID (e.g., model ID)

4. Sample type (tumor / normal)

5. Gender (if available)

6. Paired-end ID (i.e., 1 or 2; if not apparent from file names)

Required RNA metadata for computation:

1. Library prep kit used / method (e.g., stranded, non-stranded)

2. Sample ID (e.g., sequencing sample ID, passaged PDX ID)

3. Case ID (e.g., model ID)

4. Paired-end ID (i.e., 1 or 2; if not apparent from file names)

RNA

PDX RNA Expression Estimation Workflow

This RSEM workflow (RSEM 1.2.31) for quantifying gene expression uses the STAR aligner and is optimized to work with FASTQ input files.

PDX RNA Expression Estimation Workflow - Single End
This RSEM workflow (RSEM 1.2.31) for quantifying gene expression uses the STAR aligner and is optimized to work with FASTQ input files.

RNA Expression Estimation Workflow for Patient Tumor
This RSEM workflow (RSEM 1.2.31) for quantifying gene expression uses the STAR aligner and is optimized to work with FASTQ input files.

RNA Expression Estimation Workflow - Patient Tumor - Single End
This RSEM workflow (RSEM 1.2.31) for quantifying gene expression uses the STAR aligner and is optimized to work with FASTQ input files.

Whole Exome Sequencing

PDX WES CNV (Xenome) Tumor-Normal Workflow
This Whole Exome Sequencing (WES) Tumor-Normal workflow identifies copy number variants from a human exome experiment by primarily using the Broad Institute's best-practices workflow for alignment and the Sequenza R package to estimate genome wide copy number.

PDX WES Tumor-Normal (Xenome) with Variant Calling, CNV estimation, TMB, MSI, and HRD scores
This Whole Exome Sequencing (WES) tumor-normal workflow first uses the Broad Institute's best-practices workflow for read alignment, and then analyzes those data in several ways.

PDX WES Tumor-Only (Xenome) with Variant Calling, MSI, and TMB scores

This Whole Exome Sequencing (WES) tumor-normal workflow first uses the Broad Institute's best-practices workflow for read alignment, and then analyzes those data in several ways.

1. Identifies variants from a human exome experiment with GATK-4 Mutect2 for variant calling.

2. Calculates microsatellite instability (MSI) status using MSIsensor2

3. Calculates tumor mutation burden (TMB) score using filtered variants.

WES Tumor-Normal with Variant Calling, CNV estimation, TMB, MSI, and HRD scores

This Whole Exome Sequencing (WES) tumor-normal workflow first uses the Broad Institute's best-practices workflow for read alignment, and then analyzes those data in several ways.

1. Identifies variants from a human exome experiment with GATK-4 Mutect2 for variant calling.

2. Estimates genome wide copy number with the Sequenza R package.

3. Calculates tumor mutation burden (TMB) score using filtered variants.

4. Calculates microsatellite instability (MSI) status using Mantis

5. Calculated Homologous recombination deficiency (HRD) score using scarHRD with output from Sequenza

WES Tumor-Only with Variant Calling, MSI, and TMB scores
This Whole Exome Sequencing (WES) tumor-only workflow first uses the Broad Institute's best-practices workflow for read alignment, and then analyzes those data in several ways.

1. Identifies variants from a human exome experiment with GATK-4 Mutect2 for variant calling.

2. Calculates microsatellite instability (MSI) status using MSIsensor2

3. Calculates tumor mutation burden (TMB) score using filtered variants.