Algorithms to Detect Multiprotein Modularity Conserved during Evolution

Authors:
Luqman Hodgkinson;Richard M. Karp
Affiliations:
University of California-Berkeley, Berkeley;University of California-Berkeley, Berkeley
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2012

Citing 5
Cited 1

Algorithm 447: efficient algorithms for graph manipulation

Communications of the ACM
Ontologizer 2.0—a multifunctional tool for GO term enrichment analysis and data exploration

Bioinformatics
Time bounds for selection

Journal of Computer and System Sciences
A Fast Hierarchical Clustering Algorithm for Functional Modules Discovery in Protein Interaction Networks

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Algorithms to detect multiprotein modularity conserved during evolution

ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications

Parallel software architecture for experimental workflows in computational biology on clouds

PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Detecting essential multiprotein modules that change infrequently during evolution is a challenging algorithmic task that is important for understanding the structure, function, and evolution of the biological cell. In this paper, we define a measure of modularity for interactomes and present a linear-time algorithm, Produles, for detecting multiprotein modularity conserved during evolution that improves on the running time of previous algorithms for related problems and offers desirable theoretical guarantees. We present a biologically motivated graph theoretic set of evaluation measures complementary to previous evaluation measures, demonstrate that Produles exhibits good performance by all measures, and describe certain recurrent anomalies in the performance of previous algorithms that are not detected by previous measures. Consideration of the newly defined measures and algorithm performance on these measures leads to useful insights on the nature of interactomics data and the goals of previous and current algorithms. Through randomization experiments, we demonstrate that conserved modularity is a defining characteristic of interactomes. Computational experiments on current experimentally derived interactomes for Homo sapiens and Drosophila melanogaster, combining results across algorithms, show that nearly 10 percent of current interactome proteins participate in multiprotein modules with good evidence in the protein interaction data of being conserved between human and Drosophila.