Measuring gene functional similarity based on group-wise comparison of GO terms

Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

Background

Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms.

Results

We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families.

Conclusions

Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited. Supplementary information and software are available at http://www.msu.edu/~jinchen/NETSIM.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0474-7) contains supplementary material, which is available to authorized users.

Integrating multiple networks for protein function prediction

Background

High throughput techniques produce multiple functional association networks. Integrating these networks can enhance the accuracy of protein function prediction. Many algorithms have been introduced to generate a composite network, which is obtained as a weighted sum of individual networks. The weight assigned to an individual network reflects its benefit towards the protein functional annotation inference. A classifier is then trained on the composite network for predicting protein functions. However, since these techniques model the optimization of the composite network and the prediction tasks as separate objectives, the resulting composite network is not necessarily optimal for the follow-up protein function prediction.

Results

We address this issue by modeling the optimization of the composite network and the prediction problems within a unified objective function. In particular, we use a kernel target alignment technique and the loss function of a network based classifier to jointly adjust the weights assigned to the individual networks. We show that the proposed method, called MNet, can achieve a performance that is superior (with respect to different evaluation criteria) to related techniques using the multiple networks of four example species (yeast, human, mouse, and fly) annotated with thousands (or hundreds) of GO terms.

Conclusion

MNet can effectively integrate multiple networks for protein function prediction and is robust to the input parameters. Supplementary data is available at https://sites.google.com/site/guoxian85/home/mnet. The Matlab code of MNet is available upon request.

An integrative approach for measuring semantic similarities using gene ontology

Background

Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications. However, the existing GO based similarity measurements have limited functions for only a subset of GO information is considered in each measure. An appropriate integration of the existing measures to take into account more information in GO is demanding.

Results

We propose a novel integrative measure called InteGO2 to automatically select appropriate seed measures and then to integrate them using a metaheuristic search method. The experiment results show that InteGO2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories.

Conclusions

InteGO2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness. The supplementary document and software are available at http://mlg.hit.edu.cn:8082/.

A weighted multipath measurement based on gene ontology for estimating gene products similarity

Many different methods have been proposed for calculating the semantic similarity of term pairs based on gene ontology (GO). Most existing methods are based on information content (IC), and the methods based on IC are used more commonly than those based on the structure of GO. However, most IC-based methods not only fail to handle identical annotations but also show a strong bias toward well-annotated proteins. We propose a new method called weighted multipath measurement (WMM) for estimating the semantic similarity of gene products based on the structure of the GO. We not only considered the contribution of every path between two GO terms but also took the depth of the lowest common ancestors into account. We assigned different weights for different kinds of edges in GO graph. The similarity values calculated by WMM can be reused because they are only relative to the characteristics of GO terms. Experimental results showed that the similarity values obtained by WMM have a higher accuracy. We compared the performance of WMM with that of other methods using GO data and gene annotation datasets for yeast and humans downloaded from the GO database. We found that WMM is more suited for prediction of gene function than most existing IC-based methods and that it can distinguish proteins with identical annotations (two proteins are annotated with the same terms) from each other.

Measure the Semantic Similarity of GO Terms Using Aggregate Information Content

The rapid development of gene ontology (GO) and huge amount of biomedical data annotated by GO terms necessitate computation of semantic similarity of GO terms and, in turn, measurement of functional similarity of genes based on their annotations. In this paper we propose a novel and efficient method to measure the semantic similarity of GO terms. The proposed method addresses the limitations in existing GO term similarity measurement techniques; it computes the semantic content of a GO term by considering the information content of all of its ancestor terms in the graph. The aggregate information content (AIC) of all ancestor terms of a GO term implicitly reflects the GO term's location in the GO graph and also represents how human beings use this GO term and all its ancestor terms to annotate genes. We show that semantic similarity of GO terms obtained by our method closely matches the human perception. Extensive experimental studies show that this novel method also outperforms all existing methods in terms of the correlation with gene expression data. We have developed web services for measuring semantic similarity of GO terms and functional similarity of genes using the proposed AIC method and other popular methods. These web services are available at http://bioinformatics.clemson.edu/G-SESAME.

Computational prediction of protein function based on weighted mapping of domains and GO terms

In this paper, we propose a novel method, SeekFun, to predict protein function based on weighted mapping of domains and GO terms. Firstly, a weighted mapping of domains and GO terms is constructed according to GO annotations and domain composition of the proteins. The association strength between domain and GO term is weighted by symmetrical conditional probability. Secondly, the mapping is extended along the true paths of the terms based on GO hierarchy. Finally, the terms associated with resident domains are transferred to host protein and real annotations of the host protein are determined by association strengths. Our careful comparisons demonstrate that SeekFun outperforms the concerned methods on most occasions. SeekFun provides a flexible and effective way for protein function prediction. It benefits from the well-constructed mapping of domains and GO terms, as well as the reasonable strategy for inferring annotations of protein from those of its domains.

Proteomic strategies for the discovery of novel diagnostic and therapeutic targets for infectious diseases

Viruses have developed numerous and elegant strategies to manipulate the host cell machinery to establish a productive infectious cycle. The interaction of viral proteins with host proteins plays an important role in infection and pathogenesis, often bypassing traditional host defenses such as the interferon response and apoptosis. Host–viral protein interactions can be studied using a variety of proteomic approaches ranging from genetic and biochemical to large‐scale high‐throughput technologies. Protein interactions between host and viral proteins are greatly influenced by host signal transduction pathways. In this review, we will focus on comparing proteomic information obtained through differing technologies and how their integration can be used to determine the functional aspect of the host response to infection. We will briefly review and evaluate techniques employed to elucidate viral–host interactions with a primary focus on Protein Microarrays (PMA) and Mass Spectrometry (MS) as potential tools in the discovery of novel therapeutic targets. As many potential molecular markers and targets are proteins, proteomic profiling is expected to yield both clearer and more direct answers to functional and pharmacologic questions.

Semantic particularity measure for functional characterization of gene sets using gene ontology

BACKGROUND

Genetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity.

RESULTS

We propose a new approach to compute gene set particularities based on the information conveyed by GO terms. A GO term informativeness can be computed using either its information content based on the term frequency in a corpus, or a function of the term's distance to the root. We defined the semantic particularity of a set of GO terms Sg1 compared to another set of GO terms Sg2. We combined our particularity measure with a similarity measure to compare gene sets. We demonstrated that the combination of semantic similarity and semantic particularity measures was able to identify genes with particular functions from among similar genes. This differentiation was not recognized using only a semantic similarity measure.

CONCLUSION

Semantic particularity should be used in conjunction with semantic similarity to perform functional analysis of GO-annotated gene sets. The principle is generalizable to other ontologies.

Towards integrative gene functional similarity measurement

Background

In Gene Ontology, the "Molecular Function" (MF) categorization is a widely used knowledge framework for gene function comparison and prediction. Its structure and annotation provide a convenient way to compare gene functional similarities at the molecular level. The existing gene similarity measures, however, solely rely on one or few aspects of MF without utilizing all the rich information available including structure, annotation, common terms, lowest common parents.

Results

We introduce a rank-based gene semantic similarity measure called InteGO by synergistically integrating the state-of-the-art gene-to-gene similarity measures. By integrating three GO based seed measures, InteGO significantly improves the performance by about two-fold in all the three species studied (yeast, Arabidopsis and human).

Conclusions

InteGO is a systematic and novel method to study gene functional associations. The software and description are available at http://www.msu.edu/~jinchen/InteGO.

Rebelling for a reason: protein structural "outliers"

Analysis of structural variation in domain superfamilies can reveal constraints in protein evolution which aids protein structure prediction and classification. Structure-based sequence alignment of distantly related proteins, organized in PASS2 database, provides clues about structurally conserved regions among different functional families. Some superfamily members show large structural differences which are functionally relevant. This paper analyses the impact of structural divergence on function for multi-member superfamilies, selected from the PASS2 superfamily alignment database. Functional annotations within superfamilies, with structural outliers or 'rebels', are discussed in the context of structural variations. Overall, these data reinforce the idea that functional similarities cannot be extrapolated from mere structural conservation. The implication for fold-function prediction is that the functional annotations can only be inherited with very careful consideration, especially at low sequence identities.

Improving the measurement of semantic similarity between gene ontology terms and gene products: insights from an edge- and IC-based hybrid method

BACKGROUND

Explicit comparisons based on the semantic similarity of Gene Ontology terms provide a quantitative way to measure the functional similarity between gene products and are widely applied in large-scale genomic research via integration with other models. Previously, we presented an edge-based method, Relative Specificity Similarity (RSS), which takes the global position of relevant terms into account. However, edge-based semantic similarity metrics are sensitive to the intrinsic structure of GO and simply consider terms at the same level in the ontology to be equally specific nodes, revealing the weaknesses that could be complemented using information content (IC).

RESULTS AND CONCLUSIONS

Here, we used the IC-based nodes to improve RSS and proposed a new method, Hybrid Relative Specificity Similarity (HRSS). HRSS outperformed other methods in distinguishing true protein-protein interactions from false. HRSS values were divided into four different levels of confidence for protein interactions. In addition, HRSS was statistically the best at obtaining the highest average functional similarity among human-mouse orthologs. Both HRSS and the groupwise measure, simGIC, are superior in correlation with sequence and Pfam similarities. Because different measures are best suited for different circumstances, we compared two pairwise strategies, the maximum and the best-match average, in the evaluation. The former was more effective at inferring physical protein-protein interactions, and the latter at estimating the functional conservation of orthologs and analyzing the CESSM datasets. In conclusion, HRSS can be applied to different biological problems by quantifying the functional similarity between gene products. The algorithm HRSS was implemented in the C programming language, which is freely available from http://cmb.bnu.edu.cn/hrss.