HaTSPiL

HaTSPiL processes high-throughput sequencing (HTS) and next-generation sequencing (NGS) data to manage reproducible, metadata-driven analysis workflows.

Key Features:

• Python-based implementation: The pipeline is implemented in Python.
• Modular architecture: A modular design allows components and workflows to be tailored or extended for specific analyses.
• Human-readable barcoding filename convention: Barcoding filenames encode essential sample metadata in a human-readable format.
• Automated workflow and parameter selection: The pipeline automatically selects appropriate workflows and parameters based on the encoded sample metadata.
• Integration of external software: Different software tools are integrated into cohesive workflows to produce consistent analysis outputs.
• Support for HTS/NGS analyses: The framework handles a range of analyses typical for high-throughput and next-generation sequencing datasets.

Scientific Applications:

• Basic research: Analysis of HTS/NGS datasets for experimental genomics and molecular biology studies.
• Health-related studies: Sequence data processing and workflow management for clinical and translational research contexts.

Methodology:

Implemented in Python; uses human-readable barcoding filenames that encode sample metadata to drive automated selection of workflows and parameters, and integrates multiple external software tools into cohesive, reproducible workflows.