How Cloud-Native Pipelines Delivered Scalable, Automated RNA Data Analysis
Value Delivered
1,000+ Samples Processed Daily
Hours Reduced to Minutes per Sample
Fully Automated, End-to-End Workflow
For one publicly listed biotech company, the limits of a monolithic bioinformatics pipeline were slowing progress. Their existing RNAVar (nfcore – beta release) and RNASeq (nfcore stable release) pipelines relied on manual data input from NCBI and single-instance execution—making large-scale RNA analysis inefficient, inconsistent, and difficult to scale.
A Modular, Cloud-Based RNA Sequencing Pipeline
To overcome these challenges, the company partnered with Clovertex to redesign its RNA analysis framework using Nextflow and AWS Batch. Together, we built a modular, automated pipeline that streamlined every stage, from data retrieval to mutation calling.
Key outcomes included:
- Automated Data Retrieval – The FetchNGS pipeline downloads raw FastQ files directly from NCBI and other public repositories, eliminating manual uploads.
- High-Performance RNA Analysis – RNASeq and RNAVar pipelines leverage STAR, SALMON, and GATK4for alignment, quantification, and variant calling.
- AWS-Powered Scalability – Using AWS Lambda, S3, and Batch, the pipeline runs thousands of samples in parallel with automated QC and real-time monitoring.
- Interactive Quality Control – Built-in scripts and visual QC plots ensure data accuracy and integrity at every step.
Result: From Hours to Minutes
The new modular pipeline transformed RNA data analysis into a scalable, high-throughput system capable of running over 1,000 samples per job.
- Processing Speed: Reduced from hours to just minutes per sample.
- Massive Throughput: More than 1,000 samples processed daily.
- Improved Data Integrity: Automated checks ensure reliable, reproducible results.
- Cost Efficiency: Container scaling and on-demand EC2 instances maximize performance with minimal waste.
Enabling Faster, Smarter Research
By modernizing RNA sequencing with Nextflow and AWS cloud services, Clovertex helped the biotech company achieve a scalable, efficient, and cost-effective bioinformatics solution. Scientists can now focus on insights rather than infrastructure, accelerating discoveries, improving data quality, and empowering advanced machine learning research.
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