Seer to highlight how proteomics complements genomics at ASHG 2025

Seer (SEER) announced its participation at the upcoming American Society of Human Genetics 2025 Annual Meeting, taking place October 14-18 in Boston. As human genetics research increasingly moves from sequence to function, Seer’s presence at ASHG reflects how proteomics is becoming an essential complement to genomics in understanding disease biology and accelerating precision medicine. Seer will host a featured CoLab session and will be represented in multiple scientific presentations demonstrating how the Proteograph Product Suite enables researchers to translate genomic data into biological and clinical insight. Seer CoLab Session: Advancing Precision Medicine Through Multi-Omics: Clinical Insights from Xenotransplantation and Fibrosis This featured session will highlight how multi-omic approaches powered by Seer’s Proteograph Product Suite are transforming translational genomics and clinical research. Brendan Keating, PhD, Associate Professor, NYU Langone Health, will present findings from gene-edited pig organ xenotransplants into humans, where integrated omics revealed immune and physiological dynamics critical for advancing compassionate use and first-in-human trials. Gloria Sheynkman, PhD, Assistant Professor, University of Virginia School of Medicine, will discuss how proteomic profiling identified isoform-specific biomarkers predicting survival differences in idiopathic pulmonary fibrosis, pointing to new avenues for targeted therapy in complex disease. Together, these talks show how proteomics and genomics converge to uncover novel biomarkers, deepen understanding of disease mechanisms, and advance clinical translation. In addition to the CoLab session, several independent research groups will present findings generated using Seer’s Proteograph platform, reflecting its growing adoption across global academic and clinical institutions. These studies explore how Seer’s proteomic data can clarify and enhance prior findings from affinity-based methods and integrate with genomic datasets for disease discovery.