Self-Assembly of a Triphenylene-Based Electron Donor Molecule on Graphene:Structural and Electronic Properties

In this study, we report on the self-assembly of the organic electron donor 2,3,6,7,10,11-hexamethoxytriphenylene (HAT) on graphene grown epitaxially on Ir(111). Using scanning tunneling microscopy and low-energy electron diffraction, we find that a monolayer of HAT assembles in a commensurate close-packed hexagonal network on graphene/Ir(111). X-ray and ultraviolet photoelectron spectroscopy measurements indicate that no charge transfer between the HAT molecules and the graphene/Ir(111) substrate takes place, while the work function decreases slightly. This demonstrates that the HAT/graphene interface is weakly interacting. The fact that the molecules nonetheless form a commensurate network deviates from what is established for adsorption of organic molecules on metallic substrates where commensurate overlayers are mainly observed for strongly interacting systems

CAGI, the critical assessment of genome interpretation, establishes progress and prospects for computational genetic variant interpretation methods

Background: The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors. Results: Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic. Conclusions: Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead