Summary: The identification of protein complexes is a fundamental challenge in interpreting protein-protein interaction data. Crossspecies analysis allows coping with the high levels of noise that are typical to these data. The NetworkBLAST web-server provides a platform for identifying protein complexes in protein-protein interaction networks. It can analyze a single network or two networks from different species. In the latter case, NetworkBLAST outputs a set of putative complexes that are evolutionarily conserved across the two networks.
Abstract. Comparative analysis of protein networks has proven to be a powerful approach for elucidating network structure and predicting protein function and interaction. A fundamental challenge for the successful application of this approach is to devise an efficient multiple network alignment algorithm. Here we present a novel framework for the problem. At the heart of the framework is a novel representation of multiple networks that is only linear in their size as opposed to current exponential representations. Our alignment algorithm is very efficient, being capable of aligning 10 networks with tens of thousands of proteins each in minutes. We show that our algorithm outperforms a previous strategy for the problem that is based on progressive alignment, and produces results that are more in line with current biological knowledge.
Comparative analysis of protein networks has proven to be a powerful approach for elucidating network structure and predicting protein function and interaction. A fundamental challenge for the successful application of this approach is to devise an efficient multiple network alignment algorithm. Here we present a novel framework for the problem. At the heart of the framework is a novel representation of multiple networks that is only linear in their size as opposed to current exponential representations. Our alignment algorithm is very efficient, being capable of aligning 10 networks with tens of thousands of proteins each in minutes. We show that our algorithm outperforms previous approaches for the problem, and produces results that are more in line with current biological knowledge.
Higher microprocessor frequencies accentuate the performance cost of memory accesses. This is especially noticeable in the Intel's IA32 architecture where lack of registers results in increased number of memory accesses. This paper presents novel, non-speculative technique that partially hides the increasing loadto-use latency, by allowing the early issue of load instructions. Early load address resolution relies on register tracking to safely compute the addresses of memory references in the front-end part of the processor pipeline. Register tracking enables decode-time computation of register values by tracking simple operations of the form reg±immediate. Register tracking may be performed in any pipeline stage following instruction decode and prior to execution. Several tracking schemes are proposed in this paper: • Stack pointer tracking allows safe early resolution of stack references by keeping track of the value of the ESP register (the stack pointer). About 25% of all loads are stack loads and 95% of these loads may be resolved in the front-end. • Absolute address tracking allows the early resolution of constant-address loads. • Displacement-based tracking tackles all loads with addresses of the form reg±immediate by tracking the values of all general-purpose registers. This class corresponds to 82% of all loads, and about 65% of these loads can be safely resolved in the front-end pipeline. The paper describes the tracking schemes, analyzes their performance potential in a deeply pipelined processor and discusses the integration of tracking with memory disambiguation.
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