Aurabiome

In bioinformatics, pipelines — whether for RNA-seq, variant calling, or microbiome analysis - are typically *static*. They represent a snapshot of “best practices” at the time they were built. We download them, plug in our data, and hope the parameters work well enough. But as anyone working in genomics or microbiome science knows, every dataset is different. The ideal parameters for a cancer genome won’t fit a soil metagenome or a gut microbiome sample from a newborn. Tuning these parameters manually is time-consuming, costly, and requires expert knowledge. Imagine an intelligent assistant that doesn’t just run a pipeline — it  thinks  about how to improve it for your specific data and research goal. You tell it: > “Optimize my metagenomic pipeline to detect low-abundance pathogens in stool samples.” The agent runs a small pilot batch, evaluates the output, tweaks trimming thresholds, adjusts assembly parameters, re-evaluates, and iterates — until the results align with yo...