Graph animals, subgraph sampling and motif search in large networks

Details Graph animals, subgraph sampling and motif search in large networks Graph animals, subgraph sampling and motif search in large networks

2007-02-13

arxiv.org/abs/q-bio/0702029v2

Biology

Quantitative Biology

Molecular Networks

14 pages, uncludes 16 figures (color); version 2: several minor changes; to be published in Phys. Rev. E

Scientific paper

10.1103/PhysRevE.76.036107

We generalize a sampling algorithm for lattice animals (connected clusters on a regular lattice) to a Monte Carlo algorithm for `graph animals', i.e. connected subgraphs in arbitrary networks. As with the algorithm in [N. Kashtan et al., Bioinformatics 20, 1746 (2004)], it provides a weighted sample, but the computation of the weights is much faster (linear in the size of subgraphs, instead of super-exponential). This allows subgraphs with up to ten or more nodes to be sampled with very high statistics, from arbitrarily large networks. Using this together with a heuristic algorithm for rapidly classifying isomorphic graphs, we present results for two protein interaction networks obtained using the TAP high throughput method: one of Escherichia coli with 230 nodes and 695 links, and one for yeast (Saccharomyces cerevisiae) with roughly ten times more nodes and links. We find in both cases that most connected subgraphs are strong motifs (Z-scores >10) or anti-motifs (Z-scores <-10) when the null model is the ensemble of networks with fixed degree sequence. Strong differences appear between the two networks, with dominant motifs in E. coli being (nearly) bipartite graphs and having many pairs of nodes which connect to the same neighbors, while dominant motifs in yeast tend towards completeness or contain large cliques. We also explore a number of methods that do not rely on measurements of Z-scores or comparisons with null models. For instance, we discuss the influence of specific complexes like the 26S proteasome in yeast, where a small number of complexes dominate the $k$-cores with large k and have a decisive effect on the strongest motifs with 6 to 8 nodes. We also present Zipf plots of counts versus rank. They show broad distributions that are not power laws, in contrast to the case when disconnected subgraphs are included.

Affiliated with

Also associated with

No associations

Rating

Graph animals, subgraph sampling and motif search in large networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Graph animals, subgraph sampling and motif search in large networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Graph animals, subgraph sampling and motif search in large networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-210161