NEAT: a framework for building fully automated NGS pipelines and analyses

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Next-generation sequencing (NGS) data analysis is a standard yet complex task in life sciences.
• Existing tools often lack vertical integration, posing challenges for non-expert users.
• Wet-lab scientists require accessible solutions for comprehensive NGS data manipulation and analysis.

Purpose of the Study:

• To introduce the NExt generation Analysis Toolbox (NEAT), a vertically integrated software solution for NGS data analysis.
• To provide non-expert users, including wet-lab scientists, with an intuitive platform for building, running, and analyzing NGS datasets.
• To enable users to perform complex analyses without programming experience through double-clickable executables.

Main Methods:

• Development of double-clickable executables for ease of use and implementation.
• Centralized project file management for workflow control, including sample information and analysis parameters.
• Integration with institutional clusters for customizable resource allocation (storage, job submission, wall time).

Main Results:

• NEAT offers an efficient ( <24 hours completion time), intuitive, and easy-to-implement NGS analysis workflow.
• Customizable parameters allow adaptation to institutional cluster environments.
• Built-in tools facilitate rapid visualization and summarization of NGS data, including metagenomic and differential gene expression analysis.
• Small output file sizes enable easy data manipulation, consolidation, and sharing across users and institutions.

Conclusions:

• NEAT provides a robust, efficient, and comprehensive tool for analyzing massive NGS datasets.
• The framework empowers novice users to overcome technical hurdles in large dataset manipulation.
• NEAT enhances reproducibility for advanced users in the NGS era.
• The toolbox is publicly available for broader accessibility and adoption.