Predicting Protein Function by Genomic Context: Quantitative Evaluation and Qualitative Inferences

Abstract

Various new methods have been proposed to predict functional interactions between proteins based on the genomic context of their genes. The types of genomic context that they use are Type I: the fusion of genes; Type II: the conservation of gene-order or co-occurrence of genes in potential operons; and Type III: the co-occurrence of genes across genomes (phylogenetic profiles). Here we compare these types for their coverage, their correlations with various types of functional interaction, and their overlap with homology-based function assignment. We apply the methods to Mycoplasma genitalium, the standard benchmarking genome in computational and experimental genomics. Quantitatively, conservation of gene order is the technique with the highest coverage, applying to 37% of the genes. By combining gene order conservation with gene fusion (6%), the co-occurrence of genes in operons in absence of gene order conservation (8%), and the co-occurrence of genes across genomes (11%), significant context information can be obtained for 50% of the genes (the categories overlap). Qualitatively, we observe that the functional interactions between genes are stronger as the requirements for physical neighborhood on the genome are more stringent, while the fraction of potential false positives decreases. Moreover, only in cases in which gene order is conserved in a substantial fraction of the genomes, in this case six out of twenty-five, does a single type of functional interaction (physical interaction) clearly dominate (>80%). In other cases, complementary function information from homology searches, which is available for most of the genes with significant genomic context, is essential to predict the type of interaction. Using a combination of genomic context and homology searches, new functional features can be predicted for 10% of M. genitalium genes.

Combining data from genomes, Y2H and 3D structure indicates that BolA is a reductase interacting with a glutaredoxin

“…[23] or genomic context data [17]. Here we combine 3D structure data with both genomic context data and functional genomics data.…”

“…For bolA, we have analyzed a number of socalled ''genomic context'' [17] types (gene fusion, gene-order conservation, and the co-occurrence of genes among sequenced genomes) using the genomic context server STRING [18]. Two types of genomic context indicate an interaction between BolA and a mono-thiol glutaredoxin/PICOT-homology domain [8]: Their conserved occurrence as neighbors in Bacterial genomes (Fig.…”

Predicting genetic modifier loci using functional gene networks

“…To discover functional associations between genes on the basis of the genomic context of orthologs of C. elegans genes, we used the phylogenetic profile method Huynen et al 2000;Wolf et al 2001) and the gene neighbors method (Dandekar et al 1998;Overbeek et al 1999;Bowers et al 2004). For both methods, we analyzed 424 bacterial genome sequences (31 archeaebacteria and 393 eubacteria, downloaded from NCBI in December 2006).…”

Mycobacterium tuberculosisFunctional Network Analysis by Global Subcellular Protein Profiling

“…Their hypothesis is based on the observation that individual genes in one organism that are fused in another organism into a single chain, the Rosetta sequence, probably have a functional relationship and may interact physically with each other. Huynen et al (2000) performed a statistical analysis on Mycoplasma genitalium to study the significance of functional links identified among genes through the Rosetta method. They concluded that the identification of genes in one organism that match a fused gene in a second organism provide an 80% statistically significant evidence of a similar function of the gene pairs, a presence in a protein complex by 70%, and a physical interaction by 60%.…”