Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.
PLoS One
; 5(8): e12083, 2010 Aug 18.
Article
in En
| MEDLINE
| ID: mdl-20805870
ABSTRACT
We apply our recently developed information-theoretic measures for the characterisation and comparison of protein-protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large-scale analysis of protein-protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast-two-hybrid methods are sufficiently consistent to allow for intra-species comparisons (between different experiments) and inter-species comparisons, while data from affinity-purification mass-spectrometry methods show large differences even within intra-species comparisons.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Proteins
/
Computational Biology
Type of study:
Diagnostic_studies
Limits:
Animals
/
Humans
Language:
En
Journal:
PLoS One
Journal subject:
CIENCIA
/
MEDICINA
Year:
2010
Document type:
Article
Affiliation country:
United kingdom