Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks

A novel context-aware recommendation algorithm with two-level SVD in social networks

With the rapid development of Internet applications and social networks, we have entered an era of big data, and people are hard to effectively find the information they want. Therefore, lots of recommendation algorithms have been proposed to help users select useful and beneficial information, and save their time. Moreover, context-aware recommendation methods are becoming more and more popular since they could provide more accurate or personalized recommendation information, compared with traditional recommendation methods. Singular value decomposition (SVD) has been successfully integrated with some traditional recommendation algorithms. However, the basic SVD can only extra

Integrated Approach to Identify Heparan Sulfate Ligand Requirements of Robo1

An integrated methodology is described to establish ligand requirements for heparan sulfate (HS) binding proteins based on a workflow in which HS octasaccharides are produced by partial enzymatic degradation of natural HS followed by size exclusion purification, affinity enrichment using an immobilized HS-binding protein of interest, putative structure determination of isolated compounds by a hydrophilic interaction chromatography–high-resolution mass spectrometry platform, and chemical synthesis of well-defined HS oligosaccharides for structure–activity relationship studies. The methodology was used to establish the ligand requirements of human Roundabout receptor 1 (Robo1), which is involved in a number of developmental processes. Mass spectrometric analysis of the starting octasaccharide mixture and the Robo1-bound fraction indicated that Robo1 has a preference for a specific set of structures. Further analysis was performed by sequential permethylation, desulfation, and pertrideuteroacetylation followed by online separation and structural analysis by MS/MS. Sequences of tetrasaccharides could be deduced from the data, and by combining the compositional and sequence data, a putative octasaccharide ligand could be proposed (GlA-GlcNS6S-IdoA-GlcNS-IdoA2S-GlcNS6S-IdoA-GlcNAc6S). A modular synthetic approach was employed to prepare the target compound, and binding studies by surface plasmon resonance (SPR) confirmed it to be a high affinity ligand for Robo1. Further studies with a number of tetrasaccharides confirmed that sulfate esters at C-6 are critical for binding, whereas such functionalities at C-2 substantially reduce binding. High affinity ligands were able to reverse a reduction in endothelial cell migration induced by Slit2-Robo1 signaling

Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.

How lifespan and body weight vary as a function of diet and genetic differences is not well understood. Here we quantify the impact of differences in diet on lifespan in a genetically diverse family of female mice, split into matched isogenic cohorts fed a low-fat chow diet (CD, n = 663) or a high-fat diet (HFD, n = 685). We further generate key metabolic data in a parallel cohort euthanized at four time points. HFD feeding shortens lifespan by 12%: equivalent to a decade in humans. Initial body weight and early weight gains account for longevity differences of roughly 4-6 days per gram. At 500 days, animals on a HFD typically gain four times as much weight as control, but variation in weight gain does not correlate with lifespan. Classic serum metabolites, often regarded as health biomarkers, are not necessarily strong predictors of longevity. Our data indicate that responses to a HFD are substantially modulated by gene-by-environment interactions, highlighting the importance of genetic variation in making accurate individualized dietary recommendations