The effects of incomplete protein interaction data on structural and evolutionary inferences
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The effects of incomplete protein interaction data on structural and evolutionary inferences. / de Silva, Eric; Thorne, Thomas; Ingram, Piers; Agrafioti, Ino; Swire, Jonathan; Wiuf, Carsten; Stumpf, Michael P H.
In: BMC Biology, Vol. 4, 39, 03.11.2006.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - The effects of incomplete protein interaction data on structural and evolutionary inferences
AU - de Silva, Eric
AU - Thorne, Thomas
AU - Ingram, Piers
AU - Agrafioti, Ino
AU - Swire, Jonathan
AU - Wiuf, Carsten
AU - Stumpf, Michael P H
PY - 2006/11/3
Y1 - 2006/11/3
N2 - 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. Conclusion: 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 system.
AB - 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. Conclusion: 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 system.
UR - http://www.scopus.com/inward/record.url?scp=33845262280&partnerID=8YFLogxK
U2 - 10.1186/1741-7007-4-39
DO - 10.1186/1741-7007-4-39
M3 - Journal article
C2 - 17081312
AN - SCOPUS:33845262280
VL - 4
JO - B M C Biology
JF - B M C Biology
SN - 1741-7007
M1 - 39
ER -
ID: 203900915