Andrea Telatin

@telatin.bsky.social

Head of bioinformatics at the Quadram Institute - https://github.com/telatin/

🚨 New research by us! 📣 Most British drivers don't know how dangerous SUVs are to other road users, but adding information to SUV marketing materials seems to do almost nothing to change people's plans to buy SUVs Here's a 🧵 on why harder action is going to be needed 1/10

A mockup SUV advert image with a prototype safety warning

I’m looking for an automated way to read others’s scientific data without giving credit or acknowledgement, and also claim full credit for insights from it. And I want it to have a fitting name OAI: say no more

OpenAI {bot}@openaibot.bsky.social · 4mo ago

Introducing GPT-Rosalind, our frontier reasoning model built to support research across biology, drug discovery, and translational medicine. Video: https://twitter.com/openai/status/2044861690911850863

Big win for South Norfolk: we’re getting HALF of England’s new dental training places. I’ve raised this in Parliament and directly with ministers. After years of being overlooked, we are now front of the queue. South Norfolk voted for change in 2024 and Labour has listened. More to come.

Systematic detection of abnormal samples reveals widespread mislabeling in metagenomic studies | bioRxiv https://www.biorxiv.org/content/10.64898/2026.03.22.713545v1?rss=1

Systematic detection of abnormal samples reveals widespread mislabeling in metagenomic studies

The human microbiome plays a critical role in health and disease, and its dynamic nature has made longitudinal sampling a key strategy for elucidating microbiome disease relationships. Although the gut microbiome generally stabilizes over time, a subset of samples frequently shows marked deviations from an individual baseline profile. We refer to these as abnormal samples. To analyze these abnormal samples, we developed a three stage workflow to identify and classify these abnormal samples to figure out the underlying causes of these abnormal samples. Moreover, we systematically investigated abnormal samples across 16 publicly available metagenomic datasets, comprising a total of 5,171 metagenomes. Our analysis revealed that abnormal samples are often the result of mislabeling during sample collection, processing, or sequencing. Of which, fecal samples from family are more likely mislabeled. We found evidence of mislabeling in 75% of longitudinal datasets, involving up to dozens of samples per study, and in 25% of randomly selected cross sectional datasets. Additional factors such as disease status (e.g., inflammatory bowel disease), sampling intervals, and sampling density may also contribute to sample abnormalities owing to true biological variations. These findings highlight that mislabeling is a common yet underrecognized issue in microbiome research. Our work underscores the importance of identifying and correcting abnormal samples to ensure data integrity in microbiome studies and provides a practical solution for quality control in large scale metagenomic datasets. ### Competing Interest Statement The authors have declared no competing interest. National Key R&D Program of China, 2024YFC3405800 General Program of the Natural Science Foundation of Fujian Province, 2023Y9116, 2024J01577 Government of Fuqing city, 2019B003 High-level Talents Research Start-up Project of Fujian Medical University, XRCZX2020037, XRCZX2022001, XRCZX2023030, XCRZX2023004, XRCZX2023005

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