Ribosome profiling is emerging as a powerful technique that enables genome-wide investigation of in vivo translation at sub-codon resolution. The increasing application of ribosome profiling in recent years has achieved remarkable progress toward understanding the composition, regulation and mechanism of translation. This benefits from not only the awesome power of ribosome profiling but also an extensive range of computational resources available for ribosome profiling. At present, however, a comprehensive review on these resources is still lacking. Here, we survey the recent computational advances guided by ribosome profiling, with a focus on databases, Web servers and software tools for storing, visualizing and analyzing ribosome profiling data. This review is intended to provide experimental and computational biologists with a reference to make appropriate choices among existing resources for the question at hand.

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. A model-driven approach to multidisciplinary collaborative simulation for virtual product development. Advanced Engineering Informatics, Elsevier, 2010, 24 (2) Abstract:The design and development of complex artifacts and systems is shifting towards a distributed and collaborative paradigm. The simulation environments for such a paradigm, therefore, need to take into account the cooperation between design teams, i.e. supporting multidisciplinary simulation in a distributed environment. However, current simulation tools cannot fulfil this requirement as they have been developed to solve the specific problems from different disciplines. Although it's already possible to per-form multidisciplinary simulations by using several tools together, it is very difficult to implement it when these tools are distributed on the Internet. A solution which can support the integration of distributed simulation models at run-time is presented, involving a computational infrastructure and a high-level modelling approach. Specifically, the infrastructure is constructed by a novel combination of two distributed computing techniques to implement the synchronization of distributed models, as well as to ensure the interoperability at run-time. In addition, a model-driven approach is developed to bridge the high-level model of a simulation system and the infrastructure which implements this model. The solution is evaluated by making a comparison with other approaches, as well as by developing a prototype tool. It's shown in the evaluation that (1) it is viable to develop multidisciplinary simulations in a distributed environment using this solution; (2) the model-driven approach allows designers to focus only on the high-level structure of a design without getting concerned with the details of the infrastructure.

Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. While many different methods have been proposed, there is a lack of a unifying framework that would lead to state-of-the-art results. Here we develop PathCon, a knowledge graph completion method that harnesses four novel insights to outperform existing methods. PathCon predicts relations between a pair of entities by: (1) Considering the Relational Context of each entity by capturing the relation types adjacent to the entity and modeled through a novel edge-based message passing scheme; (2) Considering the Relational Paths capturing all paths between the two entities; And, (3) adaptively integrating the Relational Context and Relational Path through a learnable attention mechanism. Importantly, (4) in contrast to conventional node-based representations, PathCon represents context and paths only using the relation types, which makes it applicable in an inductive setting. Experimental results on knowledge graph benchmarks as well as our newly proposed dataset show that PathCon outperforms stateof-the-art knowledge graph completion methods by a large margin. Finally, PathCon is able to provide interpretable explanations by identifying relations that provide the context and paths that are important for a given predicted relation.

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