Accurate Quantification of Functional Analogy among Close Homologs

Abstract

Correctly evaluating functional similarities among homologous proteins is necessary for accurate transfer of experimental knowledge from one organism to another, and is of particular importance for the development of animal models of human disease. While the fact that sequence similarity implies functional similarity is a fundamental paradigm of molecular biology, sequence comparison does not directly assess the extent to which two proteins participate in the same biological processes, and has limited utility for analyzing families with several parologous members. Nevertheless, we show that it is possible to provide a cross-organism functional similarity measure in an unbiased way through the exclusive use of high-throughput gene-expression data. Our methodology is based on probabilistic cross-species mapping of functionally analogous proteins based on Bayesian integrative analysis of gene expression compendia. We demonstrate that even among closely related genes, our method is able to predict functionally analogous homolog pairs better than relying on sequence comparison alone. We also demonstrate that the landscape of functional similarity is often complex and that definitive “functional orthologs” do not always exist. Even in these cases, our method and the online interface we provide are designed to allow detailed exploration of sources of inferred functional similarity that can be evaluated by the user.

Comparative Network Analysis Reveals That Tissue Specificity and Gene Function Are Important Factors Influencing the Mode of Expression Evolution in Arabidopsis and Rice

“…Green and purple nodes denote Arabidopsis and rice genes, respectively, whereas the orthologous seed genes are drawn as boxes (and indicated by the asterisk in the heat map shown in A). Node border thickness gives the tissue-specific expression measured with t. that also compare conserved expression patterns to identify functional analogy among homologous genes (Chikina and Troyanskaya, 2011;Mutwil et al, 2011), the presented ECC method includes different null models to reliably estimate the significance levels of conserved coexpression that control network properties, such as connectivity or tissue specificity. Overall, conserved coordinated expression occurred in 77% of the analyzed genes of Arabidopsis and rice.…”

Fusion methodologies for biomedical data

“…In terms of functional relationships, Chikina et al [63] explored the hypothesis that two functionally similar genes in different organisms should have similar 'meta-genes' lists associated with their functional neighborhoods. Bayesian integration was performed using a Bayesian classifier named BNCreator tool provided with their open-source Sleipnir library [3].…”

Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes

“…Briefly, we first integrated high and low-throughput experimental data such as gene expression data, protein-protein interaction data, protein domain and transcription factor binding motif information into functional networks for each of seven organisms ( Saccharomyces cerevisiae was also included as an annotation source). Next, we calculated a network-based functional similarity score as described in our prior work [25] but extended here to additional organisms and data sources, between all ortholog and paralog pairs in a Treefam [22] gene family to identify the targets for annotation transfer. Homologs with high functional similarity scores were determined to be functional analogs.…”

“…In our previous work [25], we developed a cross-organism gene functional similarity measure, which relied on the concept that functional genomics data can be used to resolve homologous relationships among closely related genes. The approach summarizes the compendium of genomics data in each organism into functional relationship networks to identify genes that do not simply share sequence similarity but also functional behavior in large collections of heterogeneous functional data, and are thus functionally analogous (referred to in text as functional analogs ).…”