Jason Liu

TL;DR: A custom annotation within ENCODE for cancer is presented, highlighting a workflow that can help prioritise key elements in oncogenesis and targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the E NCODE resource.

TL;DR: A custom annotation for cancer-associated cell types is developed by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types to prioritize key elements and variants, in addition to regulators.

TL;DR: A negative binomial regression framework for uniformly processing STARR-seq data is developed, called STARRPeaker, which is applied to comprehensively and unbiasedly call enhancers in the HepG2 and K562 human cell lines.

TL;DR: A method called TopicNet is proposed that applies latent Dirichlet allocation to extract functional topics for a collection of genes regulated by a given TF, and a rewiring score is defined to quantify regulatory-network changes in terms of the topic changes for this TF.

TL;DR: The round table participants perceived the quality assurance standard to be important, but identified some practical process challenges that included the broad scope and complexity of the device universe, the need for a multiphase testing pathway, and the associated fees for product evaluation.