​​​​​​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. Presentado por: 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.    

El nuevo panel de exoma de ARN de Twist utiliza una novedosa estrategia de diseño que tiene en cuenta los exones para dirigirse a isoformas codificantes de proteínas conocidas y propiciar el descubrimiento de nuevos genes de fusión. Hemos evaluado el rendimiento del panel y mostramos que las abundancias relativas de los transcritos se conservan tras la captura híbrida. También mostramos las mejoras en la eficiencia de la secuenciación y demostramos la capacidad de capturar nuevas variantes estructurales, como las fusiones de ARN comunes en los cánceres. Analizamos nuestro enfoque bioinformático para evaluar el rendimiento de la captura de ARN y los retos que plantea el análisis de experimentos de secuenciación de ARN.