Identification of functional modules using network topology and high-throughput data

By Igor Ulitsky, Ron Shamir. BMC Systems Biology, 2007

329 citations recorded by the source corpus.

BackgroundWith the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data.ResultsWe describe a novel algorithmic framework for this challenge. We first transform the high-throughput data into similarity values, (e.g., by computing pairwise similarity of gene expression patterns from microarray data). Then, given a network of genes or proteins and similarity values between some of them, we seek connected sub-networks (or modules) that manifest high similarity. We develop algorithms for this problem and evaluate their performance on the osmotic shock response network in S. cerevisiae and on the human cell cycle network. We demonstrate that focused, biologically meaningful and relevant functional modules are obtained. In comparison with extant algorithms, our approach has higher sensitivity and higher specificity.ConclusionWe have demonstrated that our method can accurately identify functional modules. Hence, it carries the promise to be highly useful in analysis of high throughput data.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/822b24a57bbdd9b5839e279f54da43ad95192a49

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Citations in the corpus

Systems biology of stem cell fate and cellular reprogramming (2009, methodology): “Then, they used a graph theoretic algorithm known as MATISSE (module analysis via topology of interactions and similarity sets”
Computational solutions for omics data (2013, methodology): “For example, JACS allows for an arbitrary measure of similarity (for example, including, but not limited to, co-expression) between pairs of genes with the goal of uncovering connected subnetworks that exhibit high similarity 117 .”
Prediction of disease genes using tissue-specified gene-gene network (2014, unknown): “Ulitsky I and Shamir R [15] also combined interactions from published networks and yeast two-hybrid experiments to identify the associations.”
Protein Networks as Logic Functions in Development and Cancer (2011, unknown): “[17–30].”
Efficient community detection in large networks using content and links (2012, methodology): “Biologists have studied methods [13, 26] to find functional modules using network topology and gene expression data.”
Computational approaches for detecting protein complexes from protein interaction networks: a survey (2010, methodology): “[56] also proposed a method called MATISSE (Module Analysis via Topology of Interactions and Similarity SEts) to grow a functional module from a set of seed proteins.”
pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks (2019, methodology): “…algorithms have been proposed, such as greedy algorithms (Breitling et al., 2004; Sohler et al., 2004; Chuang et al., 2007; Nacu et al., 2007; Ulitsky and Shamir, 2007; Karni et al., 2009; Ulitsky and Shamir, 2009; Fortney et al., 2010; Doungpan et al., 2016), simulated annealing (Ideker et…”