The effects of incomplete protein interaction data on structural and evolutionary inferences.
BACKGROUND
Present protein interaction network data sets include only interactions among subsets of the proteins in an organism. Previously this has been ignored, but in principle any global network analysis that only looks at partial data may be biased. Here we demonstrate the need to consider network sampling properties explicitly and from the outset in any analysis.


RESULTS
Here we study how properties of the yeast protein interaction network are affected by random and non-random sampling schemes using a range of different network statistics. Effects are shown to be independent of the inherent noise in protein interaction data. The effects of the incomplete nature of network data become very noticeable, especially for so-called network motifs. We also consider the effect of incomplete network data on functional and evolutionary inferences.


CONCLUSIONS
Crucially, when only small, partial network data sets are considered, bias is virtually inevitable. Given the scope of effects considered here, previous analyses may have to be carefully reassessed: ignoring the fact that present network data are incomplete will severely affect our ability to understand biological systems.
de Silva
Eric
E
Theoretical Genomics Group, Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London, UK. e.desilva@imperial.ac.uk <e.desilva@imperial.ac.uk>
Thorne
Thomas
T
Ingram
Piers
P
Agrafioti
Ino
I
Swire
Jonathan
J
Wiuf
Carsten
C
Stumpf
Michael P H
MP
D019143:Evolution, Molecular; D053263:Gene Regulatory Networks / Q000235:genetics*; D008954:Models, Biological; D025941:Protein Interaction Mapping; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*
D029701:Saccharomyces cerevisiae Proteins