Large-scale analysis of structural, sequence and thermodynamic characteristics of A-to-I RNA editing sites in human Alu repeats

<p>Abstract</p> <p>Background</p> <p>Alu repeats in the human transcriptome undergo massive adenosine to inosine RNA editing. This process is selective, as editing efficiency varies greatly among different adenosines. Several studies have identified weak sequence motifs characterizing the editing sites, but these alone do not account for the large diversity observed.</p> <p>Results</p> <p>Here we build a dataset of 29,971 editing sites and use it to characterize editing preferences. We focus on structural aspects, studying the double-stranded RNA structure of the Alu repeats, and show the editing frequency of a given site to depend strongly on the micro-structure it resides in. Surprisingly, we find that interior loops, and especially the nucleotides at their edges, are more likely to be edited than helices. In addition, the sequence motifs characterizing editing sites vary with the micro-structure. Finally, we show that thermodynamic stability of the site is important for its editing.</p> <p>Conclusions</p> <p>Analysis of a large dataset of editing events reveals more information on sequence and structural motifs characterizing the A-to-I editing process</p

AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures

As AI systems' sophistication and proliferation have increased, awareness of the risks has grown proportionally (Sorkin et al. 2023). In response, calls have grown for stronger emphasis on disclosure and transparency in the AI industry (NTIA 2023; OpenAI 2023b), with proposals ranging from standardizing use of technical disclosures, like model cards (Mitchell et al. 2019), to yet-unspecified licensing regimes (Sindhu 2023). Since the AI value chain is complicated, with actors representing various expertise, perspectives, and values, it is crucial that consumers of a transparency disclosure be able to understand the risks of the AI system the disclosure concerns. In this paper we propose a risk profiling standard which can guide downstream decision-making, including triaging further risk assessment, informing procurement and deployment, and directing regulatory frameworks. The standard is built on our proposed taxonomy of AI risks, which reflects a high-level categorization of the wide variety of risks proposed in the literature. We outline the myriad data sources needed to construct informative Risk Profiles and propose a template-based methodology for collating risk information into a standard, yet flexible, structure. We apply this methodology to a number of prominent AI systems using publicly available information. To conclude, we discuss design decisions for the profiles and future work