🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Latest with Beisel Lab and Yanying Yu: Expanding the flexibility of base editing for high-throughput screens in bacteria. academic.oup.com/nar/advance-ar…

Looking to perform genome-wide screens in bacteria? 🦠 Look no further than base editors—with a few tweaks #HIRI’s Beisel Lab Lars Barquist Yanying Yu introduced to enhance the number of targetable sites. #baseediting #CRISPR #Cas9 doi.org/10.1093/nar/gk…

Very excited to finally see our study of global RNA stability in Salmonella out today in @PNASnews, where we use Bayesian statistical modeling to show bacterial transcripts have subminute half-lives, far shorter than previously thought. pnas.org/doi/10.1073/pn… 1/n

Antibiotic class with potent in vivo activity targeting lipopolysaccharide synthesis in Gram-negative bacteria | PNAS pnas.org/doi/10.1073/pn…

We just published a short paper on iMeta to introduce SeqKit's new functionality, performance optimizations, and improved user-friendliness, since the initial publication 7 years ago. onlinelibrary.wiley.com/doi/10.1002/im…

The findings were published today in Nucleic Acids Res: doi.org/10.1093/nar/gk… Congratulations to all authors! 👏🥳 Yanying Yu Beatriz Beamud @LisaOsbelt StrowigLab Sylvain Brisse Lars Barquist Prof. Udi Qimron 😏 אודי קימרון David Bikard Beisel Lab

Thrilled to share TAC–TIC, a high-throughput genetics method to identify triggers or blockers of bacterial toxin-antitoxin systems. Led by Jacob Bobonis and Alessio Yang, it allows screening thousands of genes against any TA or Abi (with toxicity) in a week. nature.com/articles/s4159…

Latest from the group on inference of bacterial regulation from RNA-seq data using deep learning: Network depth affects inference of gene sets from bacterial transcriptomes using denoising autoencoders academic.oup.com/bioinformatics…

Postdoctoral Position on the Genomics of Infectious Diseases and Antimicrobial Resistance jobs.sciencecareers.org/job/656197/pos…

In case you missed this: WHO 2024 update on the priority pathogen groups for which new treatments are needed who.int/publications/i…

Interested in RNA-targeting antisense technologies to manipulate and kill bacteria? Come join us in Würzburg this September -- registration is open (and free!) helmholtz-hiri.de/en/asobiotics2…

DECODING GENOMES - from sequences to phylodynamics: We are proud to present our book on how to analyse genomic sequences with an evolutionary perspective. See decodinggenomes.org for a free pdf, Amazon links for print orders, and further information. Great team effort 1/2

🚨BREAKING: the The Royal Society publishes "Science in the Age of AI - How AI is changing the nature and method of scientific research," and it's a must-read for everyone interested in AI & science. Important information: ➡️According to the official release, the report addresses

Network depth affects inference of gene sets from bacterial transcriptomes using denoising autoencoders" 👩‍💻🧬 This paper explores how neural network architecture impacts gene set inference from bacterial expression data! 🔬 doi.org/10.1093/bioadv… #DeepLearning #Bioinformatics

What if there was an antibiotic that doesn't disrupt the gut microbiome? There is now. A discovery published nature today nature.com/articles/s4158… Paul Hergenrother and colleagues University of Illinois justsaysinmice

Bacteria possess unique traits with great potential for benefiting society. However, current genetic engineering methods to harness these advantages are limited to a small fraction of bacterial species. 👨‍🔬🦠 Researchers from #HIRI & NC State University have now addressed this issue. (1/3)

Timescales in Cell Biology

What geneticists fear the most: The Cambridge Plot.

Pseudomonas aeruginosa faces a fitness trade-off between mucosal colonization and antibiotic tolerance during airway infection | Nature Microbiology nature.com/articles/s4156…

Interested in predicting AMR in bacteria? We show ignoring phylogenetic structure in genome collections leads to overly optimistic evaluations of machine learning methods for AMR prediction. With Yanying Yu and Nicole Wheeler. biorxiv.org/content/10.110…