Building Conversational Genomics

Modern genomics has made remarkable progress in automating the early stages of analysis. Sequencing technology continues to improve in both cost and throughput, and variant calling pipelines reliably identify millions of genetic differences from reference genomes. But when it comes to interpreting those variants, there’s a bottleneck: iterative data exploration.

When Exploration Becomes the Bottleneck

While the initial processing is increasingly streamlined, variant interpretation remains time-consuming. The iteration cycle looks like this:

A researcher notices several variants in BRCA1 (a cancer-related gene) and wants to compare their frequencies across European vs Asian populations. That analytical question triggers a context switch—from analysis mode to coding mode. Now they’re writing a script to parse the genomic data file, query gnomAD (a population frequency database) for each variant, aggregate results by ancestry, and generate a comparison plot. They wait for results, generate the visualization, and by the time it appears ...