Are your bioinformatics pipelines slow, crashing, or hard to scale? In this video, Scott Schreirey from PTP breaks down how to streamline and optimize bioinformatics workflows using AWS features like Batch, S3, and SageMaker.
Watch the full video on YouTube
Problem: Is Your Pipeline Inefficient, Slow, or Keeps Crashing?
As a computational biologist, you’re likely working with sequencing platforms like Illumina, PacBio, 10x Genomics, or Vizgen—and your pipelines process massive volumes of data from FastQ, H5AD, or VCF files. But as research scales and instruments evolve, those pipelines can become bottlenecks.
You might have a pipeline that works... most of the time. But it’s slow, or unreliable, or hard to automate. As you approach critical milestones—like funding rounds or clinical trial validation—these inefficiencies cost time and opportunity. Scaling and parallelizing pipelines within AWS can eliminate these challenges.
AWS Features That Accelerate Your Workflows
Nextflow and Airflow are powerful tools for managing workflows, especially when combined with AWS Batch, which automates parallel job processing. These jobs can be triggered automatically when new data is generated, using scalable infrastructure configured with optimized compute instances.
Once processed, data is stored in Amazon S3 in a usable format—whether that’s for visualization or structured formats like JSON used to train machine learning models in SageMaker.
These improvements aren’t just about performance. In many cases, pipeline processing time has been reduced by over 70%, while also decreasing cloud spend—thanks to more efficient automation and job orchestration.
If you’re interested, check out PTP CloudOps for Life Sciences Startups on AWS Marketplace
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