Long-Read Sequencing Fuels AI Models in Drug Discovery

Artificial intelligence is transforming drug discovery, but its effectiveness hinges entirely on data quality. As biopharma organizations deploy AI across therapeutic target identification, protein function prediction, and patient stratification, they're learning that incomplete or error-prone biological datasets fundamentally limit what machine learning models can discover.

Pacific Biosciences argues that highly accurate long-read sequencing technology addresses this challenge by providing the rich, multidimensional data that AI models require to generate meaningful biological insights. The company positions its HiFi sequencing platform as foundational infrastructure for AI-driven biopharma research.

Why it matters

AI models can only recognize patterns that exist in their training data. When genomic datasets contain sequencing errors, fragmented transcripts, or missing structural variants, algorithms learn artifacts rather than biology. For complex diseases where subtle genomic variation drives pathology, this data quality gap directly impacts target discovery, biomarker identification, and clinical trial design.

Multiomic integration drives next-generation models

Today's AI applications increasingly integrate multiple biological data types—genomes, transcriptomes, proteomes, imaging, and clinical records—to build comprehensive disease models. DNA provides the genetic blueprint while RNA reveals how cells interpret that blueprint in real time. Together, these complementary layers enable AI to connect genotype to phenotype more effectively.

This multiomic approach supports several critical biopharma applications: discovering novel therapeutic targets, predicting functional impacts of genetic variants, understanding gene regulation pathways, designing RNA and gene therapies, identifying precision medicine biomarkers, and improving patient stratification in trials.

What HiFi sequencing delivers

HiFi sequencing generates highly accurate long reads that resolve genomic regions historically difficult to characterize. The technology enables confident detection of structural variants, resolution of repetitive and medically relevant regions, phasing of variants across extended haplotypes, characterization of repeat expansions, and generation of accurate de novo genome assemblies that better represent population diversity.

A distinctive capability: HiFi whole-genome sequencing captures native DNA methylation information alongside sequence data in every run, without additional sample preparation. This epigenetic layer is increasingly valuable for AI models, as DNA methylation influences gene regulation, cellular identity, and disease progression. Researchers can train models on both genetic and epigenetic information from a single experiment.

For RNA analysis, long-read sequencing observes full-length transcripts directly rather than reconstructing them computationally. This approach characterizes complete transcript isoforms, detects alternative splicing events, identifies novel transcripts, measures allele-specific expression, and resolves gene fusions—functional data that short-read technologies often miss.

Building confidence across the AI pipeline

By providing more complete and accurate views of genomic and transcriptomic variation, high-quality long-read data strengthens the entire AI development process. Models can learn biological relationships rather than compensate for missing information. This foundation supports more accurate variant interpretation, stronger target and biomarker discovery, improved patient stratification, and more robust biological foundation models.

The integration of genomic sequence, DNA methylation, and full-length transcript data allows AI to identify relationships across multiple biological layers—connecting genetic variants to expression changes, methylation patterns to gene regulation, alternative splicing to disease mechanisms, and molecular profiles to clinical outcomes.

These details were first reported by Pacific Biosciences in a blog post on AI-driven biopharma research.