​​​​​​In the era of precision medicine, how can the power of AI be utilised to turn biological studies into the best possible targets? How can AI be applied to systems biology to bring the most effective targets to light? And how can we use these insights in the drug discovery process? In the webinar, 4 different experts will discuss the areas in which AI is bringing biological insights to the forefront, and Aaron Sato, Twist's CSO, will explain how Twist is enabling  the key players to write the Future of Biologics with Synthetic DNA and Machine Learning. 発表者：Gary An, Prof. & Vice Chair of Surgical Research, University of Vermont Aaron K. Sato, CSO, Twist Bioscience Enkelejda Miho, Prof. of Digital Life Sciences, The FHNW University of Applied Sciences and Arts Northwestern Switzerland Philip Kim, Prof., The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto

In the future, it is expected that the DNA methylation information of each individual will be used for early prevention of diseases and as a surrogate marker to improve lifestyle. However, the capture sequencing method is expensive and requires a large amount of DNA, which has been a challenge. At this time, with the cooperation of Twist Bioscience, they created a capture probe set to measure CpG at approximately 1,000 locations and evaluated it, especially for the amount of DNA required. As a result, it was confirmed that capture sequencing was possible with less than 200 ng of DNA compared to other reagents. In this talk, Dr. Shimizu presents their epigenome research at IMM, their recently published work on epigenetic age, and the results of the use of the Twist Bioscience Methylation detection system.    

Twist の新しい RNA Exome パネルは、タンパク質をコードしている既知のアイソフォームをターゲットとし、新規融合遺伝子の発見を可能にする、エクソンを考慮した新しい設計戦略を採用しています。パネルの性能を評価し、ハイブリッドキャプチャ後も相対的に豊富な転写産物量が維持されていることを示しました。また、シーケンスの効率も向上し、がんによく見られるRNA融合体など、新規の構造変異を捉える能力も実証しています。RNAのキャプチャ性能を評価するためのバイオインフォマティックなアプローチと、RNAシーケンス実験の解析における課題について説明します。