Biological context networks: a mosaic view of the interactome

Rachlin, John; Cohen, Dikla Dotan; Cantor, Charles R.; Kasif, Simon

doi:10.1038/msb4100103

Cited by 72 publications

(58 citation statements)

References 52 publications

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“…This methodology looks at absolute changes in gene expression levels, and treats each gene individually. However, genes and their protein products do not perform their functions in isolation, but in coordination [1], and the dynamic switch of a gene from one community to another always implies altered gene function [2,3]. Therefore, gene coexpression analysis was developed to explore gene interconnection at the expression level from a systems perspective [4][5][6][7][8][9][10], and 'differential coexpression analysis (DCEA)', as a complementary technique to the traditional 'differential expression analysis' (DEA) [11,12], was designed to investigate molecular mechanisms of phenotypic changes through identifying subtle changes in gene expression coordination [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

RSEM: Accurate Transcript Quantification from RNA-Seq Data With or Without a Reference Genome

2014

Bioinformatics

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Background: Differential coexpression analysis (DCEA) is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based strategy to estimate differential coexpression, which is characterized by comparing the numbers of gene neighbors in different coexpression networks. Although it simplifies the calculation, this strategy mixes up the identities of different coexpression neighbors of a gene, and fails to differentiate significant differential coexpression changes from those trivial ones. Especially, the correlation-reversal is easily missed although it probably indicates remarkable biological significance. Results: We developed two link-based quantitative methods, DCp and DCe, to identify differentially coexpressed genes and gene pairs (links). Bearing the uniqueness of exploiting the quantitative coexpression change of each gene pair in the coexpression networks, both methods proved to be superior to currently popular methods in simulation studies. Re-mining of a publicly available type 2 diabetes (T2D) expression dataset from the perspective of differential coexpression analysis led to additional discoveries than those from differential expression analysis. Conclusions: This work pointed out the critical weakness of current popular DCEA methods, and proposed two link-based DCEA algorithms that will make contribution to the development of DCEA and help extend it to a broader spectrum.

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Section: Introductionmentioning

confidence: 99%

RSEM: Accurate Transcript Quantification from RNA-Seq Data With or Without a Reference Genome

2014

Bioinformatics

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“…Such a set of networks may naturally occur in a cell due to the dependence of molecular interactions on time, space, and/or cellular contexts [58,97]. UCs derived from such networks may reveal new biological insights about condition-specific cellular processes.…”

Section: Discussionmentioning

confidence: 99%

Unstable Communities in Network Ensembles

Rahman

Jan

Kim³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Ensembles of graphs arise naturally in many applications, for example, the temporal evolution of social contacts or computer communications, tissue-specific protein interaction networks, annual citation or co-authorship networks in a field, or a family of high-likelihood Bayesian networks inferred from systems biology data. Several techniques have been developed to analyze such ensembles. A canonical problem is that of computing communities that are persistent across the ensemble. This problem is usually formulated as one of computing dense subgraphs (communities) that are frequent, i.e., appear in many graphs in the ensemble.In this thesis, we seek to find "unstable communities," which are the antithesis of frequent, dense subgraphs. Informally, an unstable community is a set of nodes that induces highly-varying subgraphs in the ensemble. In other words, the graphs in the ensemble disagree about the precise pairwise connections among these nodes. The primary contribution of this dissertation is to introduce the concept of unstable communities as a novel problem in the field of graph mining. Specifically, it presents three approaches to mathematically formulate the concept of unstable communities, devises algorithms for computing such communities in a given ensemble of networks, and shows the usefulness of this concept in a variety of settings.Our first definition of unstable community relies on two parameters: the first ensures that a node set induces several different subgraphs in the ensemble and the second guarantees that each of these subgraphs occurs in a large number of graphs in the ensemble. We present two algorithms to enumerate unstable communities that match this definition. The first approach, ClustMiner, is a heuristic that transforms the problem into one of computing dense subgraphs in a single graph that summarizes the ensemble. The second approach, UCMiner, is guaranteed to enumerate all maximal unstable communities correctly. We apply both approaches to systems biology datasets to demonstrate that UCMiner is superior to ClustMiner in the sense that ClustMiner's output contains node sets that are not unstable while also missing several communities computed by UCMiner. We find several node sets that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns.Our second and third definitions of unstable community rely on a novel concept of (scaled) subgraph divergence, a formulation that uses the concept of relative entropy to measure the instability of a community. We propose another algorithm, SDMiner, that can exactly enumerate all maximal unstable communities with small (scaled) subgraph divergence. We perform extensive experiments on social network datasets to show that we can discover UCs that capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets.Dedicated to the memory of my spiritual ...

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“…Indeed, in practice, an unambiguous functional interpretation of (genomic) databases is not straightforward. One hurdle is that functional activity depends on the context, as exemplified by genes or proteins having both stable and context-sensitive roles (Rachlin et al 2006). This demands context-sensitive databases, which are not readily available today.…”

Section: Data Mining As Such and Within The Iterative Research Cyclementioning

confidence: 99%

Ins and Outs of Systems Biology vis-à-vis Molecular Biology: Continuation or Clear Cut?

2009

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The comprehension of living organisms in all their complexity poses a major challenge to the biological sciences. Recently, systems biology has been proposed as a new candidate in the development of such a comprehension. The main objective of this paper is to address what systems biology is and how it is practised. To this end, the basic tools of a systems biological approach are explored and illustrated. In addition, it is questioned whether systems biology 'revolutionizes' molecular biology and 'transcends' its assumed reductionism. The strength of this claim appears to depend on how molecular and systems biology are characterised and on how reductionism is interpreted. Doing credit to molecular biology and to methodological reductionism, it is argued that the distinction between molecular and systems biology is gradual rather than sharp. As such, the classical challenge in biology to manage, interpret and integrate biological data into functional wholes is further intensified by systems biology's use of modelling and bioinformatics, and by its scale enlargement.

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Biological context networks: a mosaic view of the interactome

Cited by 72 publications

References 52 publications

RSEM: Accurate Transcript Quantification from RNA-Seq Data With or Without a Reference Genome

RSEM: Accurate Transcript Quantification from RNA-Seq Data With or Without a Reference Genome

Unstable Communities in Network Ensembles

Ins and Outs of Systems Biology vis-à-vis Molecular Biology: Continuation or Clear Cut?

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