151 - Deacon part 1

In this Software Deep Dive episode, we talk with Dr. Bede Constantinides (University of Birmingham) about Deacon, a tool for removing host DNA reads from metagenomic datasets. We discuss why host read removal is a deceptively difficult problem, the limitations of alignment-based approaches, and how Deacon evolved from Bede's earlier tool Hostile. The conversation covers practical issues such as human contamination in metagenomes, balancing sensitivity vs specificity when filtering reads, and the computational challenges of working with large human reference datasets and pangenomes. This episode focuses on the background and motivation for Deacon. The next episode will dive deeper into how the method works. Links Deacon https://github.com/bede/deacon Hostile https://github.com/bede/hostile/ Bede Constantinides http://bede.im/ nf-core taxprofiler https://nf-co.re/taxprofiler Kraken2 https://ccb.jhu.edu/software/kraken2/

In this episode, Lee, Nabil, and Andrew experiment with “vibe coding” bioinformatics tools using AI coding assistants. The goal: quickly build useful genomics utilities that run entirely in the browser via WebAssembly, without requiring command-line installs or servers. Nabil states: “Even if you don’t want to use this technology, you should pay attention - because everyone else will.” They discuss how existing bioinformatics programs can be compiled to WebAssembly and wrapped with simple browser interfaces so analyses run locally on a user’s machine. This keeps genomic data private while making tools easier to access. The prototype tools discussed in the episode are available here: https://genomicx.github.io/ These examples show how browser-based bioinformatics might work for lightweight tasks such as genome comparisons and basic sequence analysis. Topics Covered * Using AI tools to rapidly prototype bioinformatics software * Compiling genomics programs to WebAssembly * Running analyses locally in the browser * Privacy advantages of keeping genomic data on the user’s computer * Practical limits of browser-based computation Try the tools and let us know what you think using the hashtag #genomicx

Join hosts Kieren Sharma (Artificially Ever After podcast, University of Bristol) and Andrew Page (MicroBinfie podcast, Origin Sciences) for a compelling live panel discussion exploring the dynamic intersection of artificial intelligence and the biosciences. In this episode, our expert panel discusses: 🔬 The AI Revolution in Biology - How machine learning and deep learning have transformed everything from microscopy image processing to drug discovery, enabling researchers to move from analyzing single images to conducting statistical analysis on massive datasets. 🤖 The LLM Era - Real experiences with generative AI tools like ChatGPT and Claude in research workflows, from accelerating literature reviews to writing thousands of lines of code through prompt engineering rather than manual coding. ⚖️ Finding Balance - When AI is genuinely transformative versus when it's just hype, including practical guidance on choosing between sophisticated transformer models and simpler, more energy-efficient approaches like random forests. 🔒 Safety & Trust Challenges - The "lethal trifecta" of untrusted data, private information, and internet access; reproducibility concerns; and the critical importance of validation in scientific AI applications. 🤝 Building Interdisciplinary Bridges - Honest insights about the mutual trust deficit between biologists and data scientists, the importance of finding a "buddy" in the other discipline, and why starting with the biological problem (not the model) is essential for meaningful results. 💡 Practical Advice for Computer Scientists - What skills and mindsets are needed to successfully apply computational expertise to new domains, from understanding domain-specific language to recognizing the complexity of biological problems. Featured Panelists: Dr. Mark Basham - Science Director for AI and Informatics, Rosalind Franklin Institute Dr. Elisa Pedone - Senior Research Scientist, CellVoyant (AI for cell therapy development) James Thomas - Senior Data Scientist, Jean Golding Institute, University of Bristol Key Themes: AI in drug discovery • machine learning in microscopy • generative AI in research • interdisciplinary collaboration • data quality challenges • environmental impact of AI • the future of computational biology This episode was recorded live at the Bridging AI & Biosciences Workshop in the University of Bristol and is part of AIBio UK's mission to highlight innovative applications of artificial intelligence across the biosciences.

Final part of our discussion/debate on NextFlow

Part two of debating NextFlow

Andrew, Nabil, and Lee debate about NextFlow!

Nabil and Lee bring a guest host Clint to talk with some of the people behind Pathoplexus http://pathoplexus.org/ * Dr. Emma Hodcroft - https://pathoplexus.org/about/eb * Dr. Theo Sanderson - https://pathoplexus.org/about/development-team * Mr. Arthur Shem Kasambula - https://pathoplexus.org/about/development-team

Nabil and Lee bring a guest host Clint to talk with some of the people behind Pathoplexus http://pathoplexus.org/ * Dr. Emma Hodcroft - https://pathoplexus.org/about/eb * Dr. Theo Sanderson - https://pathoplexus.org/about/development-team * Mr. Arthur Shem Kasambula - https://pathoplexus.org/about/development-team

143 - where we catch up on our professional lives by Microbial Bioinformatics

The crew talks about the genomeqc ecosystem. Check it out at https://happykhan.github.io/qualibact/ Errata: The project name was chaned to qualibact and is no longer genomeqc as mentioned on the episode.