DolphinNext
DolphinNext orchestrates distributed, scalable Nextflow-based workflows for parallel processing and reproducible analysis of high-throughput genomics and next-generation sequencing (NGS) data.
Key Features:
- Nextflow integration: Uses Nextflow to define and execute scalable workflows for genomic data processing.
- Distributed processing: Executes tasks in parallel across distributed compute resources to handle large NGS datasets.
- Software containers: Employs software containers to encapsulate software dependencies for reproducible execution across environments.
- Modular pipeline design: Supports modular composition of pipeline components to enable reuse and composition of analytical steps.
- Scalability: Enables scaling of workflow execution to accommodate high-throughput genomics workloads.
- Portability: Provides portability of workflows and runtime environments across different compute infrastructures.
- Reproducibility: Preserves consistent results across computing environments through containerized workflows and workflow versioning.
- Flexibility: Allows pipelines to support multiple NGS data types and analysis use cases without pipeline redesign.
Scientific Applications:
- High-throughput sequencing analysis: Parallel processing of NGS datasets for large-scale genomics studies.
- Large-scale genomic data processing: Distributed computation of big genomic datasets to accelerate analysis throughput.
- Reproducible computational biology: Deployment of containerized workflows to enable reproducible analyses across environments.
Methodology:
Implements Nextflow workflow orchestration with software containers to enable parallel execution across distributed compute resources, modular pipeline composition, portability, and reproducibility.
Topics
Details
- Cost:
- Free of charge
- Tool Type:
- web application, workflow
- Added:
- 1/18/2021
- Last Updated:
- 3/1/2021
Operations
Publications
Kucukural A. DolphinNext: A Graphical User Interface for Distributed Data Processing of High Throughput Genomics. J Biomol Tech. 2019;30(Suppl):S47.
PMCID: PMC6938066