Protein Interaction Verification and Functional Annotation by Integrated Analysis of Genome-Scale Data

348

263

The dissection of complex biological systems is a challenging task, made difficult by the size of the underlying molecular network and the heterogeneous nature of the control mechanisms involved. Novel high-throughput techniques are generating massive data sets on various aspects of such systems. Here, we perform analysis of a highly diverse collection of genomewide data sets, including gene expression, protein interactions, growth phenotype data, and transcription factor binding, to reveal the modular organization of the yeast system. By integrating experimental data of heterogeneous sources and types, we are able to perform analysis on a much broader scope than previous studies. At the core of our methodology is the ability to identify modules, namely, groups of genes with statistically significant correlated behavior across diverse data sources. Numerous biological processes are revealed through these modules, which also obey global hierarchical organization. We use the identified modules to study the yeast transcriptional network and predict the function of >800 uncharacterized genes. Our analysis framework, SAMBA (Statistical-Algorithmic Method for Bicluster Analysis), enables the processing of current and future sources of biological information and is readily extendable to experimental techniques and higher organisms.

Section: Resultsmentioning

confidence: 99%

Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data

Tanay

Sharan

Kupiec

et al. 2004

348

263

“…Although we used the best annotated data available at the time of this study, the problems of false-positive and false-negative (14,30,(46)(47)(48)(49)(50) data were not completely avoided. There is also the biased coverage toward conserved proteins (30).…”

Section: Discussionmentioning

confidence: 99%

Evolution of the yeast protein interaction network

Qin

et al. 2003

To study the evolution of the yeast protein interaction network, we first classified yeast proteins by their evolutionary histories into isotemporal categories, then analyzed the interaction tendencies within and between the categories, and finally reconstructed the main growth path. We found that two proteins tend to interact with each other if they are in the same or similar categories, but tended to avoid each other otherwise, and that network evolution mirrors the universal tree of life. These observations suggest synergistic selection during network evolution and provide insights into the hierarchical modularity of cellular networks.B iological networks are the basis of cellular functions (1, 2).Understanding network evolution may shed light on the hierarchical modularity, scale-free property, and various uses of the building blocks of biological networks (3-12). The yeast protein interaction network is one of the best annotated complex networks to date (13)(14)(15)(16)(17). Previous studies on the evolution of this network focused either on gene duplication and molecular evolution at the protein level (9, 10) or on the global statistical properties (12). Neither approach can delineate the network evolutionary path, and there is no other comparable protein interaction data for the system-level comparison approach (5). Therefore, uncovering the growth patterns and the evolutionary path of the protein interaction network is a serious challenge (3,4,6,7,9,12).Parts of the present yeast protein interaction network would have been inherited from the last common ancestor of the three domains of life: Eubacteria, Archaea, and Eukaryotes. Thus, an analysis of the evolution of the yeast protein interaction network may provide new insights into the origin of eukaryotic cells (18-21), which has been a controversial issue.A key question in the evolution of biological complexity (6, 7, 9, 12, 21, 22) is, how have integrated biological systems evolved? Darwinists (21, 23) proposed natural selection as the driving force of evolution. However, the striking similarities between biological and nonbiological complexities have led to the argument that a set of universal (or ahistorical) rules account for the formation of all complexities (22,24,25). The yeast protein interaction network is an example of a complex biological system and contributes to the complexity at the cellular level (26). By analyzing the growth pattern and reconstructing the evolutionary path of the yeast protein interaction network, we can address whether or not network growth is contingent on evolutionary history, which is the key disagreement between the Darwinian view and the universality view (22,23,27).In this article, we studied how the yeast protein interaction network has evolved. We used graph theory to model the yeast protein interaction network. Each yeast protein is a node in the graph. Each pairwise interaction is a link between two nodes. Evolution of the yeast protein interaction network can then be inferred by analyzing the growth pattern ...

“…Similar approaches have previously been used to infer relationships between coexpression and gene function (17)(18)(19)(20)(21). Although highly reproducible patterns of genetic coregulation have been reported in some cases (20,22), several approaches, such as cluster analyses, may cause distortions in coexpression patterns when data from different microarray platforms are included (23).…”

mentioning

confidence: 95%

Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets

Persson

Wei²,

Milne³

et al. 2005

535

586

Coexpression patterns of gene expression across many microarray data sets may reveal networks of genes involved in linked processes. To identify factors involved in cellulose biosynthesis, we used a regression method to analyze 408 publicly available Affymetrix Arabidopsis microarrays. Expression of genes previously implicated in cellulose synthesis, as well as several uncharacterized genes, was highly coregulated with expression of cellulose synthase (CESA) genes. Four candidate genes, which were coexpressed with CESA genes implicated in secondary cell wall synthesis, were investigated by mutant analysis. Two mutants exhibited irregular xylem phenotypes similar to those observed in mutants with defects in secondary cellulose synthesis and were designated irx8 and irx13. Thus, the general approach developed here is useful for identification of elements of multicomponent processes.Arabidopsis ͉ cell wall ͉ xylem ͉ coexpression