A Framework for Comparison and Assessment of Synthetic RNA-Seq Data

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Numerous methods exist for generating synthetic bulk and single-cell RNA sequencing (RNA-seq) data.
• These synthetic datasets are crucial for benchmarking bioinformatics algorithms in various applications.

Purpose of the Study:

• To propose a general framework for comparing synthetic RNA-seq data generation tools.
• To guide researchers in selecting appropriate RNA-seq simulation algorithms based on specific study objectives.

Main Methods:

• Development of a comparative framework for evaluating synthetic RNA-seq data generation methods.
• Assessment of different algorithms for their suitability for diverse bioinformatics tasks.

Main Results:

• The framework allows for systematic comparison of synthetic data generation approaches.
• Identification of optimal tools for specific downstream analyses like differential expression and data integration.

Conclusions:

• The proposed framework empowers researchers to make informed decisions when choosing synthetic RNA-seq data simulation software.
• Facilitates the selection of the most effective RNA-seq data generation method for particular research questions.