The quality of the sequencing data generated in the laboratory has a direct impact on the accuracy and reliability of downstream bioinformatic results. Poor-quality or biased sequence data can reduce accuracy of de novo assembly, variant calling and estimates of microbial abundance, directly impacting organism identification, pathogen detection, outbreak analysis, and prediction of antimicrobial resistance.
The landscape of clinical microbiology and public health is evolving faster than ever. At BugSeq, we are continuously enhancing our platform to ensure laboratories have the most advanced, accurate, and rapid bioinformatics tools at their disposal. In the first quarter of 2026, we focused our efforts on four major areas: deepening our viral surveillance capabilities, optimizing our pipelines for the rapid turnaround of the new Illumina MiSeq i100, expanding our expert-curated Antimicrobial Resistance (AMR) databases, and seamlessly integrating with Oxford Nanopore’s EPI2ME Desktop environment.
Pathogen agnostic diagnostic assays, driven by metagenomic next-generation sequencing (mNGS), enable diagnosis and surveillance of pathogens unlike any other technology. However, a lack of bioinformatic frameworks and evidence to guide reporting thresholds has made the technology too complex for most labs. BugSeq’s latest innovation, BugID, brings intuitive, evidence-based thresholds within reach for your metagenomic assay.
2025 has been a definitive year for the adoption of next-generation sequencing (NGS) in clinical microbiology. We have moved beyond asking if sequencing should be used, to demonstrating how it solves problems that traditional microbiology cannot.
As we look forward to 2026, we took the opportunity to look back at some of our favorite publications from the past year. These studies showcase the versatility of BugSeq, from untangling diagnostic discrepancies to tracking high-priority fungal threats and characterizing emerging pathogens.
In 2024, BugSeq awarded a research grant to Dr. Georgios Kitsios, physician-scientist and Associate Professor of Medicine at the University of Pittsburgh. Dr. Kitsios leads innovative research in applying metagenomic sequencing to critical care medicine—specifically, tackling the challenge of ventilator-associated pneumonia (VAP), a life-threatening infection that affects up to half of mechanically ventilated patients. Our team at BugSeq was excited by his team’s VAP-MAPS project, which is harnessing cutting-edge nanopore sequencing and advanced bioinformatics to bring rapid, actionable diagnostics to the ICU. We caught up with Dr. Kitsios to learn more about the project and how his team is using BugSeq to advance VAP diagnostics.
Detecting and understanding antimicrobial resistance (AMR) is a critical challenge in modern microbiology, with far-reaching implications for public health and clinical decision-making. In a previous blog post, BugSeq’s Approach to AMR, we outlined the importance of combining genomic insights with machine learning (ML) to enhance AMR detection. However, not all computational approaches to AMR prediction enable the same accuracy, insight, and power. In this post, we discuss the advantages and limitations of different approaches and demonstrate how the approach BugSeq takes—expert-augmented machine learning combined with a curated AMR database—offers a more robust, interpretable, and powerful path for phenotypic AMR prediction (genomic AST).
The first half of 2025 marked a significant leap forward for the BugSeq platform, delivering powerful new capabilities to tackle the most pressing challenges in infectious disease. We’re introducing AI-powered report summaries to accelerate interpretation, leading accuracy AMR phenotype prediction (coming in a separate blog post), advanced fungal diagnostics to combat emerging pathogens, and next-generation surveillance tools that fuse genomic and epidemiological data. These updates are engineered to empower laboratories with deeper insights, greater accuracy, and faster time to action.
Infection control poses a daunting challenge for clinical microbiology labs: pathogens are constantly evolving, and outbreaks can spread with alarming speed. Timely, accurate insights are the key to staying ahead, preventing outbreaks before they happen and rapidly mitigating them when they do. BugSeq’s Infection Control solutions currently help hundreds of labs be proactive about infection control. By harnessing advanced clustering methods and real-time insights, we empower laboratories and healthcare facilities to detect, track, and prevent the spread of infectious diseases more efficiently than ever.
Next-generation sequencing is enabling a new paradigm of microbiology assays within the clinical and public health laboratory. These assays are enabling identification of microorganisms from culture or directly from sample, study of antimicrobial resistance, and investigation of relatedness between isolates.While many of these assays are used for surveillance purposes, with global changes to many regulatory frameworks, there is a growing need for assays that can be used in a regulated environment. BugSeq is invested in enabling reporting from our results to improve human health. Below, we detail some of the unique capabilities of BugSeq to enable integration into mission-critical assays.
2024 was another incredible year for sequencing in diagnostic and public health labs. As sequencing moves closer to routine use for clinical and public health microbiology, we are amazed by the breadth of sequencing applications that microbiologists, epidemiologists, and laboratory scientists are using to reduce the burden of infectious diseases.
As we approach 2025, and what will assuredly be another year of innovation for sequencing in the infectious disease field, we took the opportunity to look back at some of our favorite publications of 2024. We were amazed by the range of applications that public health professionals were choosing to use sequencing for; this list includes studies using sequencing for outbreak investigation, antimicrobial resistance prediction, and agnostic pathogen detection.