TJ先生との共著、最新作が出版されました!
ドンコ属のeDNA+次世代系統地理
Tsuji et al. (2026) High-Resolution Insights Into the Geographic Differentiation and Hybridisation of Odontobutis Gobies Through Integrated eDNA and SNP Analyses. Freshw Biol
https://t.co/KmgURw0HjU
Introducing a limited preview of GPT-5.6 Sol, our next generation frontier model, as well as GPT-5.6 Terra, a balanced model for efficient, everyday work, and GPT-5.6 Luna, a fast and affordable model for high-volume work.
https://t.co/OoM83SyISN
Since the 1960s, the genetic code has been used to predict protein sequences from DNA and mRNA sequences. Our @Nature article demonstrates that these predictions miss thousands of protein sequences present in human tissues.
Across >1,000 human samples, we identified numerous abundant proteins whose amino acid sequences differ from those predicted by the genetic code.
These proteins are not rare translation byproducts. They accumulate to thousands of copies per cell. Some are more abundant than the proteins predicted by the genetic code from the same transcripts.
Their abundance reflects a combination of alternate RNA decoding mechanisms — including codon-anticodon mismatches, tRNA abundance, and RNA modifications — and selective stabilization of the resulting proteins. The last factor – protein stability – emerges as a major determinant of protein abundance across proteins, proteoforms and cell types: https://t.co/IzOfAZKnxT
Alternate RNA decoding is pervasive across functional groups of proteins, healthy and diseased tissues. It affects proteins playing key roles in neurodegeneration, and some alternately decoded proteins show strong enrichment in tumors compared to their surrounding tissues.
This discovery has been a long and exhilarating journey with Shira Tsour and the @slavovLab team. It started in 2019 and proceeded through many challenges and thrilling highs. A journey that has opened new perspectives that we long to explore!
1/
Our perspective paper about the potential of methylation signal detection in eDNA research has finally been published! Epi-eDNA is still technically challenging, but I think it has huge potential: https://t.co/7k1TqAtOiw