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NAICNO / accelerated_genomics

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NAIC Accelerated Genomics

Overview

NAIC Accelerated Genomics is a scalable and reproducible suite of GPU-enabled Next Generation Sequencing (NGS) analysis workflows/pipelines. It is implemented using the Clara Parabricks software, which is developed by NVIDIA. The main purpose of this suite is to accelerate NGS analysis using GPU platforms. Additionally, pipeline suite provides CPU-based pipelines for benchmarking and quality control (QC) purposes. This documentation provides a user guide for installing and using NAIC Accelerated Genomics suite.

Project Organization

├── LICENSE
├── README.md               <- The top-level README for developers using this project
├── data                    <- Data directory
│   ├── external_testdata   <- Test-data from third-party sources
│   ├── internal_testdata   <- Intermediate Test-data 
│   ├── processed           <- The final, canonical data sets for modeling
│
├── tools                   <- Bioinformatics tools used in NVIDIA Parabricks
│
├── references              <- User guide, manuals, and all other explanatory materials
├── workflows               <- Collection of scalable and reproducible workflows/pipelines that automate complex NGS raw-data processing tasks 
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├── notebooks               <- Jupyter notebooks with detailed analysis
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|     *NOTE:  NOT YET IMPLEMENTED*
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├── reports                 <- *NOTE:  NOT YET IMPLEMENTED (Generated analysis as HTML, PDF, LaTeX, etc.)*
│   └── figures             <- *Generated graphics and figures to be used in reporting*

User guide

NAIC Accelerated Genomics is a suite of complex NGS analysis pipelines that can be used to process and analyze NGS data. The suite provides GPU-based NGS pipelines for accelerated analysis, as well as CPU-based pipelines for benchmarking and quality control (QC) purposes. The following pipelines are available in the suite:

  1. Accelerated germline pipeline: This pipeline is GPU-based and leverages the power of GPUs for fast and efficient analysis of NGS data.
  2. CPU-based germline pipeline: This pipeline is CPU-based and can be used for benchmarking and comparison purposes.
  3. Raw data quality control pipeline (QC pipeline): This pipeline performs quality control tests on raw NGS data, ensuring data integrity and accuracy.
  4. Pipeline to evaluate results: This pipeline is used to evaluate and compare the results obtained from the GPU-based and CPU-based NGS pipelines.

Installation

Dependencies

NAIC Accelerated Genomics has the following dependencies that need to be installed before running the pipelines:

  wget -qO- https://get.nextflow.io | bash

System requirements for Accelerated NGS pipeline

Software and hardware requirements necessary to deploy Accelerated NGS pipeline, as outlined in the Parabricks.v4.0.0 guide:

Minimum System Specifications per GPU Configuration

Software Requirements

Reference Data

For various analyses, the NGS pipelines utilize specific reference datasets. Detailed information is available in the references readme page of the documentation.

Implementation and Execution Details

The following pipelines can be executed using a set of NextFlow commands:

  1. Accelerated germline pipeline
  2. CPU-based germline pipeline
  3. Raw data quality control pipeline (QC pipeline)
  4. Pipeline to evaluate results

These pipelines can be run employing different execution platforms, which are selectable via NextFlow command line parameters:

Accelerated germline pipeline (GPU pipeline)

To conduct germline sequence analysis using GPUs, execute the following command:

  ./nextflow run \
  germline_pipeline.nf \
  -profile <PROFILE> \
  --fastq_folder <"path to the directory with raw sequence data"> \
  --genome_folder <"path to the directory with reference data"> \
  --genome_json <JSON listing reference files> \
  --processor GPU \
  --target_regions <"path to the target region file"> \
  -with-report \
  -with-trace \
  -resume

Additional Resources: Accelerated germline pipeline (GPU pipeline)

For a comprehensive overview, refer to the Germline pipeline page, which includes a workflow diagram and description of processes. For details on NVIDIA's Clara Parabricks, consult the Parabricks readme page.

CPU germline-pipeline

To initiate the CPU-based germline sequence analysis, use the command below with the --processor CPU parameter:

  ./nextflow run \
  germline_pipeline.nf \
  -profile <PROFILE> \
  --fastq_folder <"path to the directory with raw sequence data"> \
  --genome_folder <"path to the directory with reference data"> \
  --genome_json <JSON listing reference files> \
  --processor CPU \
  --target_regions <"path to the target region file"> \
  -with-report \
  -with-trace \
  -resume

Additional Resources: CPU germline-pipeline

QC-pipeline

Perform quality control assessments on NGS data using the following command:

  ./nextflow run qc_cpu.nf \
  -profile <PROFILE> \
  --fastq_folder <"path to the directory with raw sequence data"> \
  --sample_name <name of the sample> \
  --target_regions <"path to the target region file"> \
  --bam_path <"path to the alignment file - BAM file"> \
  --bai_path <"path to the alignment-index file - BAI file"> \
  --genome_folder <"path to the directory with reference data"> \
  --genome_json <JSON listing reference files> \
  -with-report \
  -with-trace \
  -resume

Additional Resources: QC-pipeline

Results evaluation pipeline

To evaluate outcomes from both GPU and CPU germline pipelines, execute the following:

  ./nextflow run vc_eval.nf \
  -profile <PROFILE> \
  --sample_name NA12878_35x \
  --VC_GPU <VCF from GPU pipeline> \
  --VC_CPU <VCF from CPU pipeline> \
  --genome_folder <REFERENCE> \
  --genome_json reference_data.json \
  --target_regions <TARGET REGIONS> \
  --diff_map_regions <DIFFICULT TO MAP REGIONS provided by precisionFDA> \
  --functional_regions <FUNCTIONAL REGIONS provided by precisionFDA> \
  -with-trace

Additional Resources: Results evaluation pipeline