SUMMARY: Understanding the regulatory roles of non-coding genetic variants has become a central goal for interpreting results of genome-wide association studies. The regulatory significance of the variants may be interrogated by assessing their influence on transcription factor binding. We have developed atSNP Search, a comprehensive web database for evaluating motif matches to the human genome with both reference and variant alleles and assessing the overall significance of the variant alterations on the motif matches. Convenient search features, comprehensive search outputs and a useful help menu are key components of atSNP Search. atSNP Search enables convenient interpretation of regulatory variants by statistical significance testing and composite logo plots, which are graphical representations of motif matches with the reference and variant alleles. Existing motif-based regulatory variant discovery tools only consider a limited pool of variants due to storage or other limitations. In contrast, atSNP Search users can test more than 37 billion variant-motif pairs with marginal significance in motif matches or match alteration. Computational evidence from atSNP Search, when combined with experimental validation, may help with the discovery of underlying disease mechanisms. AVAILABILITY AND IMPLEMENTATION: atSNP Search is freely available at http://atsnp.biostat.wisc.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cloud computing has emerged in recent years as a major segment of the IT industry; however, security concerns remain the primary impediment to full-scale adoption. Leveraging properties of virtualization, virtual machine introspection (VMI) has yielded promising research for cloud security yet adoption of these approaches in production environments remains minimal due to a semantic gap: the extraction of high-level knowledge of the guest operating system's state from low-level artifacts collected out-of-VM. Within the field of forensic memory analysis (FMA), a similar semantic gap exists from the reconstruction of physical memory dumps. We implement a production oriented prototype utilizing designs that combines and narrows these semantic gaps in a modular framework to function as an intrusion detection system (IDS) detecting and defeating post-exploitation activity.