Stability of statistical results is essential in drug development. Many research groups develop in-house pipelines for data processing and analysis, incorporating with R packages, internal computations, and even integrate with other language like Python. However, untracked tool updates can lead to result variability or pipeline failures.

At Silence Therapeutics, we've implemented a three-tiered approach to ensure result reliability, reproducibility, and robustness:

1. Automated semantic versioning: Tracks changes in our data pipeline when internal or external developers update functions.
2. Docker containerization: Ensures consistent environments for reproducible results.
3. Nextflow integration: Enables scalable execution of our containerized pipelines on AWS cloud.

This comprehensive system provides a solid foundation for our drug development efforts by maintaining pipeline integrity and result consistency.
Wei-Hung Pan
Silence Therapeutics
R, Docker and Nextflow – Building the stable Data pipeline
Stability of statistical results is essential in drug development. Many research groups develop in-house pipelines for data processing and analysis, incorporating with R packages, internal computations, and even integrate with other language like Python. However, untracked tool updates can lead to result variability or pipeline failures.  At Silence Therapeutics, we've implemented a three-tiered approach to ensure result reliability, reproducibility, and robustness  1. Automated semantic versioning Tracks changes in our data pipeline when internal or external developers update functions. 2. Docker containerization Ensures consistent environments for reproducible results. 3. Nextflow integration Enables scalable execution of our containerized pipelines on AWS cloud.  This comprehensive system provides a solid foundation for our drug development efforts by maintaining pipeline integrity and result consistency.