Arteria

Arteria automates orchestration and execution of sequencing data processing using an event-driven, workflow-based architecture with RESTful micro-services to manage data from raw acquisition to final analysis for massive parallel sequencing technologies.

Key Features:

• Event-Driven Automation: Employs an event-driven model that triggers and orchestrates tasks in response to specific events during sequencing data management.
• Modular Micro‑service Architecture: Uses a modular design that integrates bioinformatic micro-services to provide flexibility and scalability.
• Workflow-Based Process Management: Models sequencing data handling as defined workflows covering steps from raw data acquisition to final analysis.
• RESTful Micro‑services for Execution: Implements execution through RESTful micro-services, enabling independent component development and maintenance.
• Implementation Language: Written primarily in Python.

Scientific Applications:

• Sequencing core facility deployment: Implemented in three sequencing core facilities to automate data handling, reduce manual intervention, and minimize errors.
• High-throughput sequencing data management: Manages and accelerates throughput for large volumes of data generated by massive parallel sequencing technologies.

Methodology:

The methodology comprises three conceptual levels: orchestration using an event-based automation model, process definition by modeling sequencing data processing as workflows, and execution via RESTful micro‑services.