The Bioinformatics Lab podcast, hosted by Kevin Libuit and Andrew Page, begins with an exploration of Large Language Models (LLMs), like ChatGPT. The hosts underscore the ubiquity and growing influence of these models in today's world, noting their rising awareness among the global adult population.

They delve into the realm of bioinformatics, discussing the transformative potential of LLMs in this space. They elucidate how these models can process and interpret vast datasets, a task crucial in modern biology. They further delve into the specifics of genomic sequence interpretation, highlighting how LLMs can aid in deciphering the complex language of genes, thus providing invaluable insights into biological functions and disorders.

The conversation also touches upon the role of LLMs in drug discovery. They emphasize the ability of these models to predict protein structures, which is a pivotal step in the development of new medications. This application could revolutionize the pharmaceutical industry by making the drug discovery process more efficient and cost-effective.

However, the use of LLMs in bioinformatics is not without challenges. The hosts bring attention to ethical considerations, particularly the risk of misuse of genetic information and the implications for personal privacy. They stress the need for robust data privacy measures to protect individual's genetic data from unauthorized access or use.

Throughout the podcast, the hosts converse with several guests including researchers and industry experts, to glean insights into the real-world applications of LLMs in bioinformatics. These guests share their hands-on experiences, underlining both the promising breakthroughs and the hurdles they have encountered in their work. 

A recurring theme in these discussions is the interdisciplinary nature of bioinformatics. The guests emphasize the value of collaboration between different domains, such as biology, computer science, and data science. They argue that the successful deployment of LLMs in bioinformatics hinges on this interplay between diverse fields.

In the concluding segment of the podcast, the hosts reflect on the key themes from their discussion. They underscore the potential of LLMs to reshape the landscape of bioinformatics and the broader scientific community. However, they also caution listeners about the rapid pace of technological change, urging them to stay abreast of these evolving technologies and consider their ethical ramifications.

Ep 1 A new world of Large Language Models for bioinformatics

In this episode of the Bioinformatics Lab podcast, hosts Kevin Libuit and Andrew Page discuss the intricacies of implementing AI technologies in bioinformatics laboratories. They express excitement about AI's potential but caution that understanding its implementation and limitations is essential.

The hosts draw analogies and provide examples to illustrate the out-of-the-box readiness of AI technologies like GPT. They emphasize the importance of enthusiasm and expert champions to drive the technology forward in the field while also highlighting potential pitfalls, such as AI-generated misinformation and "hallucinations."

They explore concerns about AI being influenced by biased or incorrect data and the importance of expert review to prevent erroneous outcomes. The conversation includes references to legal cases, a Stanford paper, and concerns about platforms like Reddit and Twitter where misinformation can be prevalent.

In practical terms, they share advice on implementing AI in a lab, emphasizing the importance of starting with familiar tasks, being vigilant for errors, not using sensitive data, and making these tools easily accessible to employees. They stress that AI won't replace human experts but can make workflows more efficient.

The hosts wrap up with a call to action, urging adoption of these technologies and cautioning against falling behind the younger generation. They express an overall optimistic tone, emphasizing the importance of responsible use and understanding AI in the bioinformatics field. The discussion serves as both an encouragement and a warning, showcasing the incredible potential of AI while stressing the need for expertise, critical thinking, and caution.

Ep2 Getting started with AI in bioinformatics

The hosts Kevin and Andrew discuss whether bioinformaticians will be replaced by AI and robots. They note how AI is already being used to generate synthetic voices, images, and video of people, threatening industries like voice acting and extras. Bioinformatics is a complex field requiring creative problem-solving, so full automation seems far off. AI will likely play an assistive role to bioinformaticians. AI may reduce the number of bioinformaticians needed due to increased productivity, but demand for the skillset is very high.The role of bioinformaticians will evolve as tasks become more automated, requiring adaptation to new technologies. Ethical issues around plagiarism, copyright, and data integrity need to be considered with AI generation of content. Education will need to adapt by teaching modern methods, though institutional change is often slow. New practitioners born into an AI-enabled world may have very different skills and workflows than existing bioinformaticians. The hosts are optimistic AI will augment human capabilities rather than outright replace bioinformatics roles. More focus can be put on creativity. In summary, the hosts believe bioinformaticians will not be fully replaced by AI but need to evolve their skills as automation increases productivity. Ethical challenges remain around proper AI usage.

Ep3 Will we be replaced by AI robots?

In this episode of The Bioinformatics Lab podcast, hosts Kevin and Andrew discuss how bioinformaticians can get started with integrating AI into their workflows. 

They begin by recommending signing up for ChatGPT on OpenAI as the very first step. ChatGPT provides a chatbot interface to access the GPT models, allowing users to have conversations and get text responses. Paying for a premium account gives access to GPT-4, the most advanced chatbot model. An alternative free option is Anthropic's Claude.

With access to ChatGPT or Claude, some starter ways bioinformaticians can make use of it are asking it to explain bioinformatics algorithms and concepts at an introductory level, getting summaries of code snippets to understand what they are doing, and assisting with scientific writing like structuring outlines or email drafts. Kevin emphasizes starting with well-defined, widely used algorithms as the chatbot is best with established knowledge versus highly specialized niche topics.

Beyond using it for explanations, ChatGPT can help generate code. Andrew gives the example of using it to determine the best Python libraries for building a certain type of web application with specific requirements. Rather than extensive manual research, ChatGPT provided tailored recommendations on the optimal modern tools to use.  

They note that writing effective prompts is key to getting useful responses from the chatbot. Providing context about the goal and domain helps ChatGPT give relevant answers. An iterative approach can refine the prompts and answers. Kevin compares it to conversing with a person and guiding them towards the information you want.

For coding specifically, GitHub Copilot is recommended, which integrates with coding environments like VSCode. It makes AI-generated code suggestions as you are programming. Comments at the top of a code file provide the context for Copilot to suggest relevant code blocks and functions. This automation speeds up development significantly.

In summary, getting started with AI integration into bioinformatics just requires signing up for ChatGPT and GitHub Copilot. Between the natural language conversations of ChatGPT and the coding assistance of Copilot, these tools provide an excellent starter kit to increase efficiency and productivity by leveraging AI.

Ep4 AI starter kit for Bioinformaticians

Kevin Libuit began his career in public health bioinformatics in Virginia, USA, while Andrew Page's experience mainly leans towards the academic side.
   
Differences between Academia and Public Health:
   - Academia is flexible, allows for experimentation, and tends to focus on niche, blue-sky research.
   - Public health emphasizes quality, validation, reproducibility, and the impact on real-world actions, such as informing government responses or legal actions related to health concerns.

Tool Development and Use:
   - Academia often reinvents tools, leading to many solutions for the same problem.
   - Public health focuses on the implementation and consistent use of tools for actionable insights.
   - Challenges arise when academic tools are left unsupported after their initial development, whereas public health relies on long-term tool support.
   - Containerization, like Docker, helps maintain a consistent and reproducible tool environment.
   
Training and Workforce:
   - Academia tends to be cutting-edge, while public health focuses on stability and intent.
   - Upskilling public health scientists to use bioinformatics tools is crucial, often emphasizing tool usage rather than deep technical understanding.
   
Collaboration:
   - Close collaboration between academia and public health yields the best outcomes.
   - Such collaborations help in aligning the innovation from the academic world with the real-world applications in public health.
   
Publication:
   - Manuscripts pull together all elements of a study, ensuring clarity, validity, and ease of implementation across different labs. They help translate work from one public health lab to another.

Conclusion: The episode delved into the distinctions and overlaps between academic and public health bioinformatics, highlighting the importance of collaboration and communication between the two realms.

Ep5 Public Health Perspective

Hosts Kevin Libuit and Andrew Page discuss public health bioinformatics.
- They delve into the regional differences in the implementation of bioinformatics in public health, comparing the U.S. and the U.K.
- Kevin shares his experience from the Virginia State Public Health Sciences. In 2016, public health labs in the U.S. began transitioning from traditional techniques to whole genome sequencing.
- Kevin was one of the first bioinformatics scientists in Virginia and was part of a small group of state-level bioinformatics scientists. 
- Challenges faced by states in bioinformatics implementation weren't always easily addressed by federal bodies like the CDC due to state-specific issues.
- The group of state-level bioinformatics scientists, including Kevin, formed a consortium named StaPH-B to share their experiences and challenges.
- The podcast touches on the roles of federal bodies like the FDA and CDC and the complexities they bring.
- Andrew then provides insights into the U.K.'s public health structure, noting the differences between nations like Scotland, England, Wales, and Northern Ireland.
- The discussion pivots to the importance and challenges of data sharing, both in the U.S. and Europe.
- The hosts also touch on the importance of bioinformatics in solving outbreaks, with a mention of the Artic network which provides expertise during health crises.
- The episode concludes with Kevin mentioning projects like the StaPH-B Docker builds and the potential for more collaborations in the future.

Ep 6 Regional Differences in  Public Health

Hosted by Kevin Libuit and Andrew Page, the podcast focuses on the evolution of tech adoption in pathogen genomics. The narrative begins with the shift from traditional software packages to modern cloud-based GUIs. Early software installations were cumbersome, often taking weeks and requiring an in-depth understanding of dependencies and compilers. There was also a notable shift in programming languages from Ruby and Perl to Python. Package managers evolved from systems like Debian and Homebrew to Conda and BioConda, with Mamba offering further installation improvements. The podcast touched on the use of Make as an early workflow manager and its evolution to tools like snake make, next flow, and Galaxy. The role of GUIs in democratizing access to bioinformatics tools and the transition from on-prem servers to cloud computing was also discussed. However, there were concerns about the security implications of cloud computing in certain countries. The episode also delved into the challenges of closed-source tools and emphasized the benefits of open-source alternatives, especially those funded by public money. Finally, the potential influence of AI and machine learning on genomics was explored, acknowledging initial resistance but recognizing the inevitable integration of these technologies in the future. The podcast highlighted the tech journey in pathogen genomics, the significance of open-source tools, and the exciting prospects brought about by AI and machine learning.

Ep 7 Technological Advancements in public health bioinformatics

A quick update from Andrew from the Global Microbial Identifier conference in Vancouver, Canada.

Ep8 Global Microbial Identifier in a nutshell

Manuscript Discussed: https://www.science.org/doi/10.1126/sciadv.adi0497

Summary

This conversation discusses a study that explores the relationship between the gut microbiome and cognitive function. The study design involved collecting stool samples and cognitive function scores from a diverse population of children. The researchers used various statistical methods, including linear models and machine learning, to analyze the data. The results showed associations between specific microbial profiles and cognitive function scores, as well as correlations with brain regions. The study suggests the potential for using the gut microbiome as a screening tool and for future interventions to improve cognitive development.

Takeaways

The gut microbiome may play a role in cognitive development and function.
Specific microbial profiles are associated with cognitive function scores.
Machine learning techniques can help identify important microbial features.
The gut microbiome may have implications for brain regions and cognitive performance.

Ep30 Part 2: Gut-Brain Axis with Kevin Bonham, Part Two

PHA4GE Ten Best Practices for Public Health Bioinformatics Pipelines:
https://github.com/pha4ge/public-health-pipeline-best-practices/blob/main/docs/pipeline-best-practices.md

Summary

In this episode, Andrew Page and Kevin Libuit discuss best practices for public health bioinformatics pipelines. They highlight the importance of code availability, open source licensing, version control, workflow management systems, and containerized and packaged software. These practices aim to improve transparency, reproducibility, and interoperability in the field of bioinformatics.

Takeaways

Code availability and open source licensing are crucial for transparency and collaboration in public health bioinformatics.
Version control allows for the tracking of software changes and facilitates collaboration.
Workflow management systems provide standardization and interoperability in pipeline development.
Containerized and packaged software ensures reproducibility and simplifies software installation.

Ep 32: Best Practices - Pipeline Development, Part One

Summary

In this conversation, Sharvari Narendra shares her career journey in bioinformatics, which includes experiences in academia, industry, and a startup. She emphasizes the importance of exploring different career tracks and finding alignment with personal passions. Sharvari highlights the creativity and problem-solving skills required in bioinformatics, as well as the need to understand the tools and pipelines used in data analysis. She also discusses the differences between working in academia and industry, including flexibility and the level of collaboration. Sharvari encourages early career professionals to attend conferences, network, and continue learning in order to advance in the field.

Ep 40 Career Spotlight: Sharvari Narendra

Summary

In this episode, Kevin Libuit and Andrew Page discuss their personal experiences with job hunting in the bioinformatics field, emphasizing the importance of professional networks and genuine relationships. They share anecdotes about how their careers were shaped by connections made through academia and social interactions. The conversation highlights actionable advice for job seekers, including leveraging social media and engaging with the community to build a robust professional network.

Takeaways

All jobs can stem from knowing the right people.
Building a professional network is essential for career growth.
Genuine curiosity and kindness can lead to unexpected opportunities.
Networking should be organic, not forced or transactional.
Social media platforms are valuable tools for connecting with others.
Engaging in community discussions can enhance visibility and opportunities.
Conferences provide a great avenue for networking and learning.
It's important to have a clear online presence, like LinkedIn.
Networking is about mutual interest, not just self-promotion.
Being helpful and interested in others can pay off in the long run.



Ep. 46: The Job Hunt Experience pt. 01

Summary

In this episode of the Buy Informatics Lab podcast, Kevin Libuit and Andrew Page discuss the intricacies of the job market from a recruiter's perspective. They share insights on how to effectively navigate the recruitment process, the importance of leveraging professional networks, and the role of online platforms like LinkedIn and GitHub in identifying and attracting talent. The conversation emphasizes the significance of having a strong digital footprint and the strategies that can help candidates stand out in a competitive job market.

Takeaways

Successful hires often come from personal networks.
Recommendations from trusted colleagues add value.
Passion for bioinformatics is crucial in candidates.
Being findable on LinkedIn is essential for job seekers.
A strong digital presence reduces friction in recruitment.
Effective communication skills are vital for collaboration.
Separating personal and professional online identities is advisable.
Engagement in technical communities can highlight talent.
GitHub profiles serve as portfolios for developers.
A clear digital footprint enhances job application success.



EP 47: The Job Hunt Experience pt. 02

Kalamari GitHub: https://github.com/lskatz/Kalamari
& Public Health Announcement: https://journals.asm.org/doi/10.1128/mra.00963-24

Summary

In this episode of the Bioinformatics Lab podcast, Kevin Libuit and Andrew Page discuss a public health announcement regarding the Kalamari database, a curated reference database for public health pathogenomics. They explore the importance of high-quality genomic data, the implications of mobile genetic elements, and the various applications of the database in public health. The conversation highlights the collaborative efforts of experts in the field and the significance of reliable data in making informed public health decisions.

Takeaways

The Kalamari database is a curated resource for public health.
High-quality genomic data is essential for accurate pathogen identification.
Public health decisions rely on the accuracy of genomic data.
Mobile genetic elements complicate species typing in bioinformatics.
The collaboration of experts enhances the quality of the database.
The database allows for effective quality control in laboratories.
Understanding plasmids is crucial for outbreak investigations.
The CDC's involvement underscores the importance of public health infrastructure.
The database is accessible and user-friendly for public health applications.
This initiative represents a significant advancement in pathogen genomics.




EP 48: The Kalamari Database

Summary

In this episode of the Bioinformatics Lab Podcast, Kevin Libuit and Andrew Page discuss the ongoing H5N1 outbreak, its implications for public health, and the economic impact on agriculture. They explore the nature of avian influenza, the risks of mammalian spillover, and the importance of genomic surveillance in understanding and combating the virus. The conversation highlights the rapid advancements in vaccine development and the need for ongoing monitoring to prevent future outbreaks.

Takeaways

H5N1 is a highly pathogenic avian influenza affecting poultry and cattle.
Influenza viruses are adept at recombining, posing pandemic risks.
The current H5N1 outbreak has significant economic impacts on U.S. agriculture.
Mammalian spillover from birds to cattle raises public health concerns.
Surveillance data is crucial for understanding the spread of H5N1.
Genomic sequencing aids in vaccine development and monitoring mutations.
Pasteurization is vital in preventing the spread of H5N1 through dairy products.
The economic impact of H5N1 in the U.S. is already over a billion dollars.
Public health risks from H5N1 remain low but could increase with mutations.
Collaboration between public health labs and tech communities is essential for effective response.

EP 49: H5N1 Outbreak

Summary

In this episode of the Bioinformatics Lab podcast, Kevin Libuit and Andrew Page discuss the art and challenges of naming bioinformatics tools. They explore various naming conventions, the significance of unique and memorable names, and the creative strategies employed by developers. The conversation also touches on the importance of trademark considerations and the potential pitfalls of naming conflicts in the bioinformatics community.

Takeaways

Naming bioinformatics tools is crucial for usability.
A good name should be unique and memorable.
Backronyms are a common naming strategy.
Consistency in naming is important for software development.
Names can be inspired by personal experiences or local culture.
Trademark issues can arise with popular names.
Creative themes can enhance the identity of tools.
Names should be easy to Google and not offensive.
Developers should consider the implications of their chosen names.
A well-thought-out name can aid in communication and collaboration.



EP 50: Software Naming

Summary

In this episode, Kevin Libuit and Andrew Page discuss the complexities of software bugs, focusing on the distinction between user errors and software errors. They explore the importance of understanding user needs and the challenges of software compatibility, particularly in the context of bioinformatics. The conversation emphasizes the need for a multidisciplinary approach to software development and troubleshooting.

Takeaways

A lot of software lacks proper engineering and testing.
User errors often stem from simple mistakes like formatting.
Debugging requires careful attention to detail.
New line endings can cause compatibility issues across systems.
User documentation is crucial for effective troubleshooting.
Understanding user needs can prevent software errors.
Software engineers sometimes create unnecessary complexities.
Output formats should be user-friendly and compatible.
A multidisciplinary approach enhances software development.
Effective communication between users and developers is essential.



EP 51: Software Bugs

Summary
In this episode of the Bioinformatics Lab podcast, Kevin Libuit and Krisandra Allen discuss the integration of bioinformatics and epidemiology, focusing on the challenges and solutions in utilizing genomic sequencing data for infectious disease surveillance. They explore the roles of epidemiologists and bioinformaticians, the importance of communication and collaboration, and the need for bi-directional learning to enhance public health practices. The conversation emphasizes the significance of building relationships and understanding each other's fields to effectively turn sequencing data into actionable insights for disease control.

Takeaways
Epidemiologists work on the front lines of public health.
Genomic data can help build and confirm hypotheses in investigations.
Integrating genetic data with epidemiological data enhances disease surveillance.
Surveillance systems must be designed to drive public health action.
Communication between labs and epidemiologists is crucial for effective data use.
Bi-directional learning is essential for both epidemiologists and bioinformaticians.
Genomic epidemiologists can bridge the gap between bioinformatics and epidemiology.
Building relationships is the first step towards effective collaboration.
Training opportunities should be available for both sides to learn from each other.
Collaboration is key to successfully integrating bioinformatics into public health.

EP 52: Genomic Epidemiology with Krisandra Allen

Summary

In this episode of the Bioinformatics Lab podcast, Kevin Libuit and Andrew Page discuss the evolution of remote bioinformatics work, exploring the benefits and challenges of remote work, the importance of in-person interactions, and the ongoing debate surrounding the return to office policies. They share personal experiences and insights on how remote work has transformed their professional lives, the impact of commuting on quality of life, and the necessity of fostering communication and connection in a virtual environment.

Takeaways

Remote work allows hiring the best talent globally.
Quality of life improves with reduced commuting time.
In-person interactions enhance team dynamics and relationships.
Informal conversations are crucial for problem-solving.
Virtual communication lacks the nuances of face-to-face interactions.
Creating informal communication spaces is essential in remote work.
Return to office policies are a complex issue.
Isolation can negatively impact mental health in remote work.
Intentionality is key in fostering workplace culture remotely.
Balancing remote work with in-person meetings is vital.



EP 53: Remote Work

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.

EP 54: Pathogen Genomics in Healthcare w/ Alex Sundermann

Summary

In this conversation, Dr. Momo Vuyisich discusses the significance of metatranscriptomics in understanding the microbiome and its impact on human health. He emphasizes the importance of gene expression over gene content, the challenges of metabolomics, and the innovations in RNA technology that have made metatranscriptomics more accessible and cost-effective. Dr. Vujicic also shares insights on building a successful biotech company, the future of diagnostics, and how to define a healthy microbiome based on biochemical reactions. In this conversation, Momo and Kevin delve into the complexities of gut health, particularly focusing on hydrogen sulfide production and its implications for overall health. They discuss the intricacies of personalized nutrition, the limitations of traditional health metrics, and the need for a paradigm shift in nutritional science towards preventive maintenance. The conversation emphasizes the importance of understanding individual microbiome profiles to create tailored dietary recommendations, moving away from one-size-fits-all approaches.

More info at https://www.viome.com/

EP 55: Microbiome and Human Health with Momo Vuyisich

Ketih's Blog, Omics Omics: https://omicsomics.blogspot.com/

Summary

In this conversation, Keith Robison discusses the inception of his blog Omix, the evolution of social media in the scientific community, and the importance of feedback and community engagement in scientific writing. He delves into the changing landscape of sequencing technologies, highlighting the emergence of new companies and the implications for biodiversity research. The discussion also touches on the future of sequencing, the challenges of cost reduction, and the potential for sequencing technologies to explore previously uncharted biological phenomena.



EP 56: Open Conversation with Keith Robison

Tommy's Blog: https://divingintogeneticsandgenomics.com/

Summary  In this conversation, Kevin Libuit and Tommy Tang explore the journey of communication in science, particularly in the field of bioinformatics. They discuss the importance of sharing knowledge, the transition from wet lab to computational biology, and the evolution of bioinformatics tools. Tommy shares his experiences with learning Unix, the balance between remote work and in-person interactions, and the mindset of finding opportunities in crises. The conversation highlights the significance of humility, continuous learning, and the interconnectedness of roles within the scientific community.

Takeaways

Tommy started his blog to document his learning journey in bioinformatics.
Criticism can be a positive opportunity for growth and collaboration.
Journaling experiences helps in personal growth and reflection.
Unix skills are foundational for bioinformatics and computational biology.
The transition from wet lab to computational biology can be challenging but rewarding.
Understanding the data generation process is crucial for analysis.
In-person interactions enhance communication and collaboration.
The biotech startup environment fosters agility and innovation.
Mindset shifts can turn crises into opportunities.
The distinction between analysts and developers is becoming more pronounced in the field.

EP 57: Open Conversation with Tommy Tang

In this episode of the Bioinformatics Lab podcast, Gültekin Ünal shares his unique journey from veterinary medicine to bioinformatics, highlighting the pivotal moments that shaped his career. 

For more information about the PHA4GE organization discussed in this episode, please visit: https://pha4ge.org/

He discusses the importance of microbiology in understanding diseases, the impact of COVID-19 on public health practices, and the significance of the One Health approach in integrating animal and human health. Gültekin emphasizes the role of bioinformatics in pathogen surveillance and the need for collaboration within the scientific community to address global health challenges.

EP 58: From Veterinary Medicine to Pathogen Genomics with Gültekin Ünal

In this conversation, Amy Gaskin shares her journey into bioinformatics, detailing her transition from an ecological background to a career in public health. She discusses the pivotal moments that led her to discover her passion for bioinformatics, the essential skills she acquired during her master's program, and her experiences working in pathogen genomics during the COVID-19 pandemic. Amy emphasizes the importance of workflow management, containerization, and standardization in bioinformatics, particularly in the context of public health. She also reflects on her current work modernizing HIV pipelines and the collaborative efforts within the field.


EP 59: Navigating Bioinformatics and Public Health with Amy Gaskin

In this episode of the Bioinformatics Lab Podcast, Janina Krumbeck shares her unique journey from Germany to the United States, detailing her academic experiences and the challenges she faced during her PhD program. She discusses the importance of mentorship, the transition from academia to industry, and her current role in the microbiomics team at Zymo Research. Janina emphasizes the value of persistence and finding a fulfilling career path in the biotech industry.

EP 60: NGS for Veterinary Diagnostics with Janina Krumbeck (MiDog)

Summary

In this engaging conversation, Kaylie Zapanta, a PhD in neurophysiology, discusses her journey from academia to her current role at MyDog, where she focuses on veterinary diagnostics through NGS data. The discussion highlights the complexities of microbial interactions, the importance of effective communication of scientific data to clinicians, and the future of diagnostics in veterinary medicine, emphasizing the shift towards sequencing technologies over traditional methods.

EP 61: MiDog Technical Deep Dive with Kaylie Zapanta

Summary

In this episode of the Bioinformatics Lab podcast, James Oteno shares his journey as a bioinformatician and genomic epidemiologist, discussing the intersection of bioinformatics and public health, particularly in the context of emerging pathogens in Africa. The conversation covers the importance of both wet lab and dry lab skills, the role of KEMRI in global health, and the impact of COVID-19 on public health practices. Oteno also delves into the MPOX outbreak, the rapid sequencing technology used in Ebola outbreaks, and the future of genomic epidemiology in Africa, emphasizing the need for collaborative networks and equitable partnerships in global health efforts.



EP 62: Public Health Pathogen Genomics in Africa with James Otieno

In this episode of the Bioinformatics Lab Podcast, Zachary Konkel shares his journey from studying chemistry to specializing in mycology and bioinformatics. He discusses the significance of fungi in medicine, particularly in producing antimicrobial compounds, and the challenges faced in fungal genomics. Zach highlights the development of mycotools (https://github.com/xonq/mycotools) as a solution for analyzing fungal genomes and the importance of phylogenetics in understanding fungal pathogens like Candida auris. 

The conversation also touches on the growing concern of antifungal resistance and the potential for next-generation sequencing to improve outbreak detection. Finally, Zach shares his passion for mushroom foraging, connecting his scientific interests with personal hobbies.



EP 63: Mycotic Bioinformatics with Zachary Konkel

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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