Blog¶

BugSeq’s Approach to AMR

Introduction

Antimicrobial resistance (AMR) poses a major threat to human and animal health. In 2019 alone, there were an estimated 1.27 million deaths attributable to AMR, and that number has likely grown since. For each person with an infection, AMR reduces the likelihood that they are treated with effective antimicrobials and recover from infection. Faster, more accurate detection of AMR from clinical, biothreat and surveillance samples is of paramount importance to curb the global burden of disease attributable to AMR. Prediction of phenotypic AMR using DNA (and RNA!) sequencing is a promising avenue to achieve this goal. At BugSeq, we have focused on combating AMR since our inception; to date, we have predicted AMR for hundreds of thousands of genomes. We often get questions about how our AMR analysis works; below, we detail our goals, latest approaches and updated thinking to bioinformatic analysis for AMR prediction.

BugSeq Bioinformatics Inc. Collaborative Effort to Develop and Validate an end-to-end Agnostic Diagnostic Assay Published

Introduction

BugSeq partnered with University of British Columbia Researchers, along with clinical and public health collaborators at Vancouver Coastal Health, BC Centre for Disease Control, and Canada’s Michael Smith Genome Sciences Centre to develop, automate, and validate an end-to-end metagenomic sequencing assay for agnostic detection of respiratory viral pathogens within 12-hours. The work was funded by the Biomedical Advanced Research and Development Authority (BARDA), part of the Administration for Strategic Preparedness and Response within the U.S. Department of Health and Human Services under contract number 75A50122C00027.

The results of this collaborative project were published last week in The Journal of Infectious Diseases. Gauthier et al. outlined the development and validation of a metagenomic sequencing assay, termed Rapid Pathogen Identification through Metagenomic Next-Generation Sequencing (RAPID-mNGS).

BugSeq Case Study: Providence Health Care

Introduction

Next generation sequencing (NGS) is transforming clinical microbiology. It has enabled an unprecedented understanding of microbes, from taxonomic identification to prediction of antimicrobial resistance and transmission. BugSeq’s turnkey bioinformatics platform enables the automated analysis of NGS for clinical and public health laboratories.

In our inaugural case study, we had the opportunity to connect with the Medical Microbiologists and Molecular Scientists at St. Paul’s Hospital, Providence Health Care to understand what is involved in building a sequencing program. Providence Health Care provides patient and family centered health care to all British Columbians. St. Paul’s Hospital is a major academic tertiary-care hospital with over 700 inpatient beds, and provides care to some of Vancouver’s most vulnerable populations.

Our Favorite Publications of 2023

2023 has been an incredible year for sequencing in diagnostic and public health labs. Thanks to the innovation and drive of molecular scientists, microbiologists, epidemiologists and laboratory technologists, major progress was made to bring sequencing closer to routine use.

As we look forward to all of the advances that 2024 is sure to bring, we took the opportunity to look back at our favorites from 2023. This list includes a mix of metagenomics, infection control and antimicrobial resistance; each of which is sure to have substantial impact on human health in the year to come and beyond.

Providing Reliable, Reproducible and Valid Results with Bioinformatic Versioning

BugSeq helps many clinical and public health labs to quickly identify pathogens, outbreaks, antimicrobial resistance and more. In order for labs to utilize BugSeq data, they need evidence that the results are both correct and reproducible. Imagine if a system diagnosed the same patient with different conditions depending on what day of the week it was - that system would not be very useful. More concretely, given a set of inputs, a system must produce the same outputs to be useful in a high-stakes setting. Our analysis solutions are no different - labs trust us not only for our accuracy, but also for our reproducibility. It allows them to rely on our results, and many customers have used this property to validate LDTs based on BugSeq. So how do we do it?