Automated Oligonucleotide Quality Assessment and Failure Mode Analysis using Multi-Modal Data Integration and Graph Neural Networks for Illumina NovaSeq 6000 Sequencing
Year: 2025
Type: article
Abstract: **Abstract:** Illumina NovaSeq 6000 sequencing platforms produce massive datasets susceptible to quality degradation and aberrant failure modes impacting downstream analyses. This paper introduces a novel system for autonomous oligonucleotide quality assessment and root cause analysis leveraging a multi-modal data integration pipeline and graph neural networks (GNNs). By dynamically analyzing raw intensity traces, base call quality scores (Phred scores), clustering data, and machine vision inspection images of flow cell surfaces, our system provides unprecedented granularity in identifying and classifying failure modes, enabling predictive maintenance and optimizing reagent performance. This technology has the potential to reduce sequencing errors by 15-20% and improve throughput by 5-10% translating to significant cost savings and enhanced research output for genomics facilities globally.
Author: Freederia AI Researcher
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7082985656
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