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NGScloud2: optimized bioinformatic analysis using Amazon Web Services.
Fernando Mora-Márquez1, José Luis Vázquez-Poletti2, Unai López de Heredia1
1GI Sistemas Naturales e Historia Forestal, Dpto. Sistemas y Recursos Naturales, ETSI Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid, Madrid, Spain.
Peerj
|May 7, 2021
Summary
NGScloud2 is an updated bioinformatic system for cloud-based RNA sequencing analysis, offering cost savings and expanded tools for various genomic applications. It provides accessible infrastructure for complex data analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- NGScloud was an early cloud-based system for de novo RNAseq analysis of non-model species.
- Rapid advancements in cloud computing and bioinformatics tools rendered NGScloud obsolete.
- NGScloud2 has been developed to address these limitations and provide an updated solution.
Purpose of the Study:
- To present NGScloud2, an enhanced bioinformatic system for cloud-based genomic data analysis.
- To enable efficient and cost-effective analysis of various next-generation sequencing data types.
- To provide accessible cloud computing infrastructure for researchers lacking specialized hardware.
Main Methods:
- NGScloud2 leverages cloud computing infrastructure, specifically Amazon Web Services (AWS), with optimized instance types and spot instance capabilities for cost savings.
- The system incorporates updated and common applications for de novo RNAseq analysis.
- It includes tools for reference-based RNAseq, RADseq, and functional annotation workflows.
Main Results:
- NGScloud2 successfully processed pipelines for de novo RNAseq, reference-based RNAseq, RADseq, and functional annotation using real experimental data.
- Workflow performance estimates and optimization tips are provided.
- A qualitative comparison with the Galaxy framework is presented.
Conclusions:
- NGScloud2 offers an enhanced and expanded platform for cloud-based genomic data analysis, improving upon its predecessor.
- The system optimizes access to large-scale computing resources, making advanced bioinformatic tools accessible to a wider range of users.
- Code and companion utilities are publicly available to facilitate adoption and downstream analysis.
