Græmlin: General and robust alignment of multiple large interaction networks

Flannick, Jason; Novak, Antal F.; Srinivasan, Balaji; McAdams, Harley H.; Batzoglou, Serafim

doi:10.1101/gr.5235706

Cited by 292 publications

(240 citation statements)

References 72 publications

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“…Koyuturk et al (12) proposed another method, motivated by biological models of duplication and deletion. Recently, Flannick et al (7) proposed a new efficient approach, using modules of proteins to infer the alignment. Berg and Lassig (13) have proposed a Bayesian approach to this problem.…”

mentioning

confidence: 99%

Global alignment of multiple protein interaction networks with application to functional orthology detection

Singh¹,

Berger

2008

Proc. Natl. Acad. Sci. U.S.A.

574

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Protein-protein interactions (PPIs) and their networks play a central role in all biological processes. Akin to the complete sequencing of genomes and their comparative analysis, complete descriptions of interactomes and their comparative analysis is fundamental to a deeper understanding of biological processes. A first step in such an analysis is to align two or more PPI networks. Here, we introduce an algorithm, IsoRank, for global alignment of multiple PPI networks. The guiding intuition here is that a protein in one PPI network is a good match for a protein in another network if their respective sequences and neighborhood topologies are a good match. We encode this intuition as an eigenvalue problem in a manner analogous to Google's PageRank method. Using IsoRank, we compute a global alignment of the Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, and Homo sapiens PPI networks. We demonstrate that incorporating PPI data in ortholog prediction results in improvements over existing sequence-only approaches and over predictions from local alignments of the yeast and fly networks. Previous methods have been effective at identifying conserved, localized network patterns across pairs of networks. This work takes the further step of performing a global alignment of multiple PPI networks. It simultaneously uses sequence similarity and network data and, unlike previous approaches, explicitly models the tradeoff inherent in combining them. We expect IsoRank-with its simultaneous handling of node similarity and network similarity-to be applicable across many scientific domains.biological networks ͉ graph isomorphism ͉ network alignment ͉ protein-protein interactions ͉ functional coherence A fundamental goal of biology is to understand the cell as a system of interacting components. In particular, the discovery and understanding of interactions between proteins has received significant attention in recent years. Toward this goal, highthroughput experimental techniques [e.g., yeast two-hybrid (1, 2) and coimmunoprecipitation (3)] have been invented to discover protein-protein interactions (PPIs) . The data from these techniques, which are still being perfected, are being supplemented by high-confidence computational predictions and analyses of PPIs (4-6). A powerful way of representing and analyzing this vast corpus of data is the PPI network: A network where each node corresponds to a protein and an edge indicates a direct physical interaction between the proteins.As the size of PPI datasets for various species rapidly increases, comparative analysis of PPI networks across species is proving to be a valuable tool. Such analysis is similar in spirit to traditional sequence-based comparative genomic analyses; it also promises commensurate insights. As a phylogenetic tool, it offers a functionoriented perspective that complements traditional sequence-based methods. Comparative network analysis also enables us to identify conserved functional components across species (7) and perform...

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mentioning

confidence: 99%

Global alignment of multiple protein interaction networks with application to functional orthology detection

Singh¹,

Berger

2008

Proc. Natl. Acad. Sci. U.S.A.

574

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“…However, global methods are likely to create more false positive matches than local alignments, as many more interactions are aligned, even those with weak evidence. Still, the IsoRank, GRAAL, and Graemlin families of global methods produce alignments with significant levels of functional similarity between aligned proteins [113][114][115].…”

Section: Protein-protein Interaction Network Alignmentmentioning

confidence: 99%

Domain‐mediated protein interaction prediction: From genome to network

et al. 2012

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a b s t r a c tProtein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence-and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease.

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“…The main comparative approaches considered so far focus on homogeneous networks using networks alignment (see, among others, [10,11,18,20,5,2,21,12]). Such methods are powerful for detecting conserved modules across several networks of different species.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Aspects of Heterogeneous Biological Networks Comparison

Blin¹,

Fertin

Mohamed-Babou

et al. 2011

Combinatorial Optimization and Applications

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Abstract. Biological networks are commonly used to model molecular activity within the cell. Recent experimental studies have shown that the detection of conserved subnetworks across several networks, coming from different organisms, may allow the discovery of disease pathways and prediction of protein functions. There already exist automatic methods that allow to search for conserved subnetworks using networks alignment; unfortunately, these methods are limited to networks of same type, thus having the same graph representation. Towards overcoming this limitation, a unified framework for pairwise comparison and analysis of networks with different graph representations (in particular, a directed acyclic graph D and an undirected graph G over the same set of vertices) is presented in [4]. We consider here a related problem called k-DAGCC: given a directed graph D and an undirected graph G on the same set V of vertices, and an integer k, does there exist sets of vertices V1, V2, . . .is connected ? Two variants of k-DAGCC are of interest: (a) the Vis must form a partition of V , or (b) the Vis must form a cover of V . We study the computational complexity of both variants of k-DAGCC and, depending on the constraints imposed on the input, provide several polynomial-time algorithms, hardness and inapproximability results.

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Græmlin: General and robust alignment of multiple large interaction networks

Cited by 292 publications

References 72 publications

Global alignment of multiple protein interaction networks with application to functional orthology detection

Global alignment of multiple protein interaction networks with application to functional orthology detection

Domain‐mediated protein interaction prediction: From genome to network

Algorithmic Aspects of Heterogeneous Biological Networks Comparison

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