1,044 research outputs found
Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes
Complexes of physically interacting proteins constitute fundamental
functional units responsible for driving biological processes within cells. A
faithful reconstruction of the entire set of complexes is therefore essential
to understand the functional organization of cells. In this review, we discuss
the key contributions of computational methods developed till date
(approximately between 2003 and 2015) for identifying complexes from the
network of interacting proteins (PPI network). We evaluate in depth the
performance of these methods on PPI datasets from yeast, and highlight
challenges faced by these methods, in particular detection of sparse and small
or sub- complexes and discerning of overlapping complexes. We describe methods
for integrating diverse information including expression profiles and 3D
structures of proteins with PPI networks to understand the dynamics of complex
formation, for instance, of time-based assembly of complex subunits and
formation of fuzzy complexes from intrinsically disordered proteins. Finally,
we discuss methods for identifying dysfunctional complexes in human diseases,
an application that is proving invaluable to understand disease mechanisms and
to discover novel therapeutic targets. We hope this review aptly commemorates a
decade of research on computational prediction of complexes and constitutes a
valuable reference for further advancements in this exciting area.Comment: 1 Tabl
Detecting highly overlapping community structure by greedy clique expansion
In complex networks it is common for each node to belong to several
communities, implying a highly overlapping community structure. Recent advances
in benchmarking indicate that existing community assignment algorithms that are
capable of detecting overlapping communities perform well only when the extent
of community overlap is kept to modest levels. To overcome this limitation, we
introduce a new community assignment algorithm called Greedy Clique Expansion
(GCE). The algorithm identifies distinct cliques as seeds and expands these
seeds by greedily optimizing a local fitness function. We perform extensive
benchmarks on synthetic data to demonstrate that GCE's good performance is
robust across diverse graph topologies. Significantly, GCE is the only
algorithm to perform well on these synthetic graphs, in which every node
belongs to multiple communities. Furthermore, when put to the task of
identifying functional modules in protein interaction data, and college dorm
assignments in Facebook friendship data, we find that GCE performs
competitively.Comment: 10 pages, 7 Figures. Implementation source and binaries available at
http://sites.google.com/site/greedycliqueexpansion
Link communities reveal multiscale complexity in networks
Networks have become a key approach to understanding systems of interacting
objects, unifying the study of diverse phenomena including biological organisms
and human society. One crucial step when studying the structure and dynamics of
networks is to identify communities: groups of related nodes that correspond to
functional subunits such as protein complexes or social spheres. Communities in
networks often overlap such that nodes simultaneously belong to several groups.
Meanwhile, many networks are known to possess hierarchical organization, where
communities are recursively grouped into a hierarchical structure. However, the
fact that many real networks have communities with pervasive overlap, where
each and every node belongs to more than one group, has the consequence that a
global hierarchy of nodes cannot capture the relationships between overlapping
groups. Here we reinvent communities as groups of links rather than nodes and
show that this unorthodox approach successfully reconciles the antagonistic
organizing principles of overlapping communities and hierarchy. In contrast to
the existing literature, which has entirely focused on grouping nodes, link
communities naturally incorporate overlap while revealing hierarchical
organization. We find relevant link communities in many networks, including
major biological networks such as protein-protein interaction and metabolic
networks, and show that a large social network contains hierarchically
organized community structures spanning inner-city to regional scales while
maintaining pervasive overlap. Our results imply that link communities are
fundamental building blocks that reveal overlap and hierarchical organization
in networks to be two aspects of the same phenomenon.Comment: Main text and supplementary informatio
Efficient and accurate greedy search methods for mining functional modules in protein interaction networks
<p>Abstract</p> <p>Background</p> <p>Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures.</p> <p>Methods</p> <p>In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules.</p> <p>Results</p> <p>The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms.</p> <p>Conclusions</p> <p>Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.</p
Enrichment and aggregation of topological motifs are independent organizational principles of integrated interaction networks
Topological network motifs represent functional relationships within and
between regulatory and protein-protein interaction networks. Enriched motifs
often aggregate into self-contained units forming functional modules.
Theoretical models for network evolution by duplication-divergence mechanisms
and for network topology by hierarchical scale-free networks have suggested a
one-to-one relation between network motif enrichment and aggregation, but this
relation has never been tested quantitatively in real biological interaction
networks. Here we introduce a novel method for assessing the statistical
significance of network motif aggregation and for identifying clusters of
overlapping network motifs. Using an integrated network of transcriptional,
posttranslational and protein-protein interactions in yeast we show that
network motif aggregation reflects a local modularity property which is
independent of network motif enrichment. In particular our method identified
novel functional network themes for a set of motifs which are not enriched yet
aggregate significantly and challenges the conventional view that network motif
enrichment is the most basic organizational principle of complex networks.Comment: 12 pages, 5 figure
Exploring hierarchical and overlapping modular structure in the yeast protein interaction network
<p>Abstract</p> <p>Background</p> <p>Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC) for clustering vertices of a protein interaction network using a novel subgraph density measurement.</p> <p>Results</p> <p>By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications.</p> <p>Conclusions</p> <p>Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.</p
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