Whole exome sequencing has been increasingly used in human disease studies. Prioritization based on appropriate functional annotations has been used as an indispensable step to select candidate variants. Here we present the latest updates to dbNSFP (version 4.1), a database designed to facilitate this step by providing deleteriousness prediction and functional annotation for all potential nonsynonymous and splice-site SNVs (a total of 84,013,093) in the human genome. The current version compiled 36 deleteriousness prediction scores, including 12 transcript-specific scores, and other variant and gene-level functional annotations. The database is available at http://database.liulab.science/dbNSFP with a downloadable version and a web-service.

microRNAs (miRNAs) are short non-coding RNAs that can repress the expression of protein coding messenger RNAs (mRNAs) by binding to the 3'UTR of the target. Genetic mutations such as single nucleotide variants (SNVs) in the 3'UTR of the mRNAs can disrupt this regulatory effect. In this study, we presented dbMTS, the database for miRNA target site (MTS) SNVs, which includes all potential MTS SNVs in the 3'UTR of human genome along with hundreds of functional annotations. This database can help studies easily identify putative SNVs that affect miRNA targeting and facilitate the prioritization of their functional importance.

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The 2-body correlation function (2-BCF) is a group of statistical measurements that found applications in many scientific domains. One type of 2-BCF named the Spatial Distance Histogram (SDH) is of vital importance in describing the physical features of natural systems. While a naïve way of computing SDH requires quadratical time, efficient algorithms based on resolving nodes in spatial trees have been developed. A key decision in the design of such algorithms is to choose a proper underlying data structure: our previous work utilizes quad-tree (oct-tree for 3-dimensional data) and in this paper we study a kd-tree-based solution. Although it is easy to see that both implementations have the same time complexity O N 2d−1 d , where d is the number of dimensions of the dataset, a thorough comparison of their actual running time under different scenarios is conducted. In particular, we present an analytical model to rigorously quantify the running time of dual-tree algorithms. Our analysis suggests that the kd-tree-based implementation outperforms the quad-/oct-tree solution under a wide range of data sizes and query parameters. Specifically, such performance advantage is shown as a speedup up to 1.23X over the quad-tree algorithm for 2D data, and 1.39X over the oct-tree for 3D data, respectively. Results of extensive experiments run on synthetic and real datasets confirm our findings.

Due to the high level of parallelism, there are unique challenges in developing system software on massively parallel hardware such as GPUs. One such challenge is designing a dynamic memory allocator whose task is to allocate memory chunks to requesting threads at runtime. State-of-the-art GPU memory allocators maintain a global data structure holding metadata to facilitate allocation/deallocation. However, the centralized data structure can easily become a bottleneck in a massively parallel system. In this paper, we present a novel approach for designing dynamic memory allocation without a centralized data structure. The core idea is to let threads follow a random search procedure to locate free pages. Then we further extend to more advanced designs and algorithms that can achieve an order of magnitude improvement over the basic idea. We present mathematical proofs to demonstrate that (1) the basic random search design achieves asymptotically lower latency than the traditional queue-based design and (2) the advanced designs achieve significant improvement over the basic idea. Extensive experiments show consistency to our mathematical models and demonstrate that our solutions can achieve up to two orders of magnitude improvement in latency over the best-known existing solutions.