A toolkit for enhanced reproducibility of RNASeq analysis for synthetic biologists

Area of Science:

• Synthetic biology
• Genomics
• Computational biology

Background:

• RNA sequencing (RNASeq) is crucial for synthetic biology's design-build-test-learn cycle.
• Analyzing RNASeq data is complex, involving multiple steps and computational challenges.
• Reproducibility in RNASeq experiments is vital for reliable synthetic biology research.

Purpose of the Study:

• To present a high-throughput pipeline for maximizing analytical reproducibility of RNASeq in synthetic biology.
• To explore the impact of experimental reproducibility on the validation of machine learning models.
• To enable high-throughput exploration of combinatorial designs in synthetic biology.

Main Methods:

• Developed a pipeline integrating traditional RNASeq processing tools with structured metadata tracking.
• Implemented the pipeline for analyzing synthetic biology experiments.
• Conducted two experiments: a control comparison and a machine learning model validation.

Main Results:

• The pipeline demonstrated improved analytical reproducibility for RNASeq data in synthetic biology.
• Control experiments revealed that reproducible protocols for one organism do not guarantee reproducibility in another.
• Lack of reproducibility in experimental runs can limit the validation accuracy of machine learning models.

Conclusions:

• The developed pipeline enhances the reproducibility of RNASeq analysis for synthetic biologists.
• Ensuring experimental reproducibility is critical for accurate biological insights and reliable machine learning model validation.
• This work addresses computational and experimental challenges in high-throughput synthetic biology research.