In this episode of the Bioinformatics Lab Podcast, Mxolisi Nene shares his journey from a curious kid “scanning soil” with a stick and a broken Pentium II in rural KwaZulu-Natal to a bioinformatician and PhD candidate at the Agricultural Research Council in Pretoria. He walks through his path from animal science into bioinformatics, profiling the gut microbiomes of indigenous village chickens using 16S and metagenomic sequencing, and how wrestling with messy real-world data led him into multi-omics integration and machine learning. Mxolisi explains concepts like feature engineering, neural networks, and ecological “tipping points” in soil ecosystems—showing how combining metagenomic, metabolomic, proteomic, and genomic layers can help predict when an environment is on the brink of collapse, with implications for agriculture, food security, and even disease research.
We also dig into the philosophical side of his work: why the explosion of public omics data makes it almost a moral obligation to use these tools for better outbreak prevention and environmental stewardship, how conferences like PHA4GE in Cape Town and the AI working group are quietly seeding a new generation of multi-omics scientists, and what it feels like to realize that the five-year-old kid obsessed with dirt grew up to do exactly what he was pretending to do—only now with HPC clusters, neural nets, and GitHub.
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In this episode of the Bioinformatics Lab Podcast, Mxolisi Nene shares his journey from a curious kid “scanning soil” with a stick and a broken Pentium II in rural KwaZulu-Natal to a bioinformatician and PhD candidate at the Agricultural Research Council in Pretoria. He walks through his path from animal science into bioinformatics, profiling the gut microbiomes of indigenous village chickens using 16S and metagenomic sequencing, and how wrestling with messy real-world data led him into multi-omics integration and machine learning. Mxolisi explains concepts like feature engineering, neural networks, and ecological “tipping points” in soil ecosystems—showing how combining metagenomic, metabolomic, proteomic, and genomic layers can help predict when an environment is on the brink of collapse, with implications for agriculture, food security, and even disease research.
We also dig into the philosophical side of his work: why the explosion of public omics data makes it almost a moral obligation to use these tools for better outbreak prevention and environmental stewardship, how conferences like PHA4GE in Cape Town and the AI working group are quietly seeding a new generation of multi-omics scientists, and what it feels like to realize that the five-year-old kid obsessed with dirt grew up to do exactly what he was pretending to do—only now with HPC clusters, neural nets, and GitHub.
EP 54: Pathogen Genomics in Healthcare w/ Alex Sundermann
the bioinformatics lab
48 minutes
6 months ago
EP 54: Pathogen Genomics in Healthcare w/ Alex Sundermann
Pathogen genomics in healthcare: overcoming barriers to proactive surveillance: https://journals.asm.org/doi/10.1128/aac.01479-24
Summary
In this conversation, Kevin Libuit and Alex Sundermann delve into the significance of pathogen genomics in healthcare, discussing its role in infection prevention and control. They explore the transition from traditional methods to genomic surveillance, the challenges of operationalizing these technologies, and the ethical implications of transparency in patient care. The discussion also highlights the need for evidence-based practices, the importance of incentives for hospitals, and the potential for lowering barriers to entry for genomic technologies in healthcare settings. In this conversation, Alex Sundermann and Kevin Libuit discuss the critical role of genomic surveillance in infection prevention and control. They emphasize the need for actionable insights from genomic data, the importance of metadata, and the challenges of data sharing across institutions. Financial sustainability and legal implications of genomic practices are explored, alongside the necessity for community standards and equitable access to genomic technologies. The conversation concludes with a call for learning from outbreaks and the importance of publishing findings to improve patient safety.
Takeaways
Pathogen genomics enhances understanding of disease transmission in healthcare.
Genomic surveillance can significantly improve infection prevention efforts.
Public health has successfully utilized pathogen genomics for outbreak detection.
The evidence supporting genomic surveillance is compelling but underutilized in healthcare.
Operationalizing genomic surveillance requires collaboration between hospitals and payers.
Ethical considerations are crucial in the implementation of genomic technologies.
Incentives and regulations are needed to encourage hospitals to adopt genomic surveillance.
Lowering the barriers to entry can facilitate the adoption of genomic technologies.
Commercial partnerships can help hospitals access genomic sequencing services.
Methodological standards in genomics are essential for effective interpretation of results. Genomic data must be actionable for infection preventionists.
Metadata is crucial for effective genomic surveillance.
Data sharing across institutions is a significant challenge.
Financial sustainability is essential for genomic surveillance programs.
Legal liability may increase for hospitals not using genomic surveillance.
Community standards for genomic practices are needed.
Equitable access to genomic technologies is vital.
Learning from outbreaks can improve patient safety.
Publishing outbreak findings is essential for knowledge sharing.
Genomic surveillance can help identify misallocated resources.
the bioinformatics lab
In this episode of the Bioinformatics Lab Podcast, Mxolisi Nene shares his journey from a curious kid “scanning soil” with a stick and a broken Pentium II in rural KwaZulu-Natal to a bioinformatician and PhD candidate at the Agricultural Research Council in Pretoria. He walks through his path from animal science into bioinformatics, profiling the gut microbiomes of indigenous village chickens using 16S and metagenomic sequencing, and how wrestling with messy real-world data led him into multi-omics integration and machine learning. Mxolisi explains concepts like feature engineering, neural networks, and ecological “tipping points” in soil ecosystems—showing how combining metagenomic, metabolomic, proteomic, and genomic layers can help predict when an environment is on the brink of collapse, with implications for agriculture, food security, and even disease research.
We also dig into the philosophical side of his work: why the explosion of public omics data makes it almost a moral obligation to use these tools for better outbreak prevention and environmental stewardship, how conferences like PHA4GE in Cape Town and the AI working group are quietly seeding a new generation of multi-omics scientists, and what it feels like to realize that the five-year-old kid obsessed with dirt grew up to do exactly what he was pretending to do—only now with HPC clusters, neural nets, and GitHub.
