bvalderrama.bsky.social
PhD student doing Computational Biology at APC microbiome 🇮🇪
Personal website: https://rb.gy/455ftf
Microbiome-Gut-Brain axis | Gut permeability | Bioinformatics🦠🧬💻 | Stats
29 posts
337 followers
326 following
Prolific Poster
Conversation Starter
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Finally, I’d like to thank again everyone involved in this research: Paulina Calderón-Romero, Thomaz, @biothomaz.bsky.social, and of course to my supervisors: Aonghus Lavelle, Ger Clarke, and John @jfcryan.bsky.social. Also, thanks to the centre APC @apcmicrobiomeirel.bsky.social
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We show how semiautomated project screening can better represent regional biodiversities 🌎. The workflow is available for researchers building similar archives for other regions or body sites. The data generated is open, and new features are coming, so keep an eye on GitHub: shorturl.at/i9P1G (8/8)
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We improved estimates of regional biodiversity and identified countries with the greatest potential to uncover more biodiversity in future sampling efforts—an analysis that’s the first of its kind and a critical guide for regions with limited research resources. (7/8).
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After analysing >30 projects—most not included in any compendium before—we show how saMBA expanded our understanding of the human gut microbiome with samples from nearly every country in the region 🌎. We also found that nearly a third were likely discarded due to past sample size restrictions (6/8).
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Why South America? Simple: it’s one of the regions with the fewest microbiome samples but some of the highest gut microbiome diversity among its inhabitants ☝️ (shorturl.at/qT1I6). Plus, I’m from Chile, so there's an emotional link 😂 (5/8)
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Inspired by their work and after talking with Richard (first author in shorturl.at/qT1I6), I recreated the workflow from the global compendium, which is currently unavailable to other users. Then, with my coauthors, we curated a list of projects to be included in our archive: saMBA (4/8).
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So, global compendiums have analysed gut microbiomes using unified workflows. But resource-driven choices can unintentionally perpetuate some representativity issues these efforts aimed to fix—like filtering based on project size and metadata annotation. (3/8)
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We know public microbiome data is dominated by samples from Europe and North America. That’s a problem since microbe-health associations often don’t translate across global populations. (2/8)
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Feedback on the chapter is welcome!
It was a pleasure to contribute to the book Orchestrating Microbiome Analysis (OMA) shorturl.at/BI6ny. I hope to do it again—and that more people join in as well!
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Special shout-out to @jatinnagpal.bsky.social. Working with you was a very nice experience, and I'm sure there will be more collaborations comming 🥳.
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Joining this project ~2 years ago allowed me to be part of the APC and the Cryan lab, so it has an special place in my heart. This was also the last time I worked with pipettes (for the safety of my lab mates 😂) before going back to fully computational work.
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Here we present an integrated framework of in-silico 💻, in-vitro 🧪 and in-vivo 🐟 approaches to identify bacterial strains 🦠 with the potential to:
- degrade prebiotics,
- produce neuroactive molecules 🧠 , and
- alter stress-related gene expression and behaviour in zebrafish model
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Thanks Ken! 🙌
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Thanks to my supervisors @jfcryan.bsky.social, Ger Clarke and Aonghus Lavell for supporting my ideas. Thanks to @biothomaz.bsky.social, friend and colleague, for all the science chat 😂. Also thanks to Paulina Calderon, for her help spotting relevant studies in the literature
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Scenario A: One researcher gets their hands on a large dataset. Brainstorms 100 hypotheses, runs analyses for each one, only publishes the significant ones.
Scenario B: Public dataset. 100 researchers download it, each tests 1 hypothesis. Only the significant ones get published.
Which is better?