Gene functional similarity search tool (GFSST)

BACKGROUND: With the completion of the genome sequences of human, mouse, and other species and the advent of high throughput functional genomic research technologies such as biomicroarray chips, more and more genes and their products have been discovered and their functions have begun to be understood. Increasing amounts of data about genes, gene products and their functions have been stored in databases. To facilitate selection of candidate genes for gene-disease research, genetic association studies, biomarker and drug target selection, and animal models of human diseases, it is essential to have search engines that can retrieve genes by their functions from proteome databases. In recent years, the development of Gene Ontology (GO) has established structured, controlled vocabularies describing gene functions, which makes it possible to develop novel tools to search genes by functional similarity. RESULTS: By using a statistical model to measure the functional similarity of genes based on the Gene Ontology directed acyclic graph, we developed a novel Gene Functional Similarity Search Tool (GFSST) to identify genes with related functions from annotated proteome databases. This search engine lets users design their search targets by gene functions. CONCLUSION: An implementation of GFSST which works on the UniProt (Universal Protein Resource) for the human and mouse proteomes is available at GFSST Web Server. GFSST provides functions not only for similar gene retrieval but also for gene search by one or more GO terms. This represents a powerful new approach for selecting similar genes and gene products from proteome databases according to their functions

FunSimMat: a comprehensive functional similarity database

Functional similarity based on Gene Ontology (GO) annotation is used in diverse applications like gene clustering, gene expression data analysis, protein interaction prediction and evaluation. However, there exists no comprehensive resource of functional similarity values although such a database would facilitate the use of functional similarity measures in different applications. Here, we describe FunSimMat (Functional Similarity Matrix, http://funsimmat.bioinf.mpi-inf.mpg.de/), a large new database that provides several different semantic similarity measures for GO terms. It offers various precomputed functional similarity values for proteins contained in UniProtKB and for protein families in Pfam and SMART. The web interface allows users to efficiently perform both semantic similarity searches with GO terms and functional similarity searches with proteins or protein families. All results can be downloaded in tab-delimited files for use with other tools. An additional XML–RPC interface gives automatic online access to FunSimMat for programs and remote services

GOTax: investigating biological processes and biochemical activities along the taxonomic tree

GOTax, a novel web-based platform that integrates protein annotation with protein family classification and taxonomy, allows for an extensive assessment of functional similarity between proteins and for comparing and analyzing the distribution of protein families and protein functions over different taxonomic groups

Candidate gene mapping: approach, methods and significance

Abstract Candidate gene is a gene with known or assumed function that may affect genetic control of a trait and thus, can be considered a 'candidate gene' for this trait. The Candidate gene associates a gene to its phenotypic trait. These quantitative traits responsible may be biomedical, economical, and even evolutionary important studies. The traditional candidate gene identification is tedious due to limited information of molecular marker and, also lack of computational tools and software. However, digital candidate gene approach makes candidate gene identification reliable and rapid due to available literature database and gene ontology database. The Candidate gene mapping is successfully conducted with the identification of molecular marker, linkage map construction and Quantitative trait locus mapping. The candidate gene approach is important for determination of associated genetic variant with phenotype

Assessing protein similarity with Gene Ontology and its use in subnuclear localization prediction

BACKGROUND: The accomplishment of the various genome sequencing projects resulted in accumulation of massive amount of gene sequence information. This calls for a large-scale computational method for predicting protein localization from sequence. The protein localization can provide valuable information about its molecular function, as well as the biological pathway in which it participates. The prediction of localization of a protein at subnuclear level is a challenging task. In our previous work we proposed an SVM-based system using protein sequence information for this prediction task. In this work, we assess protein similarity with Gene Ontology (GO) and then improve the performance of the system by adding a module of nearest neighbor classifier using a similarity measure derived from the GO annotation terms for protein sequences. RESULTS: The performance of the new system proposed here was compared with our previous system using a set of proteins resided within 6 localizations collected from the Nuclear Protein Database (NPD). The overall MCC (accuracy) is elevated from 0.284 (50.0%) to 0.519 (66.5%) for single-localization proteins in leave-one-out cross-validation; and from 0.420 (65.2%) to 0.541 (65.2%) for an independent set of multi-localization proteins. The new system is available at . CONCLUSION: The prediction of protein subnuclear localizations can be largely influenced by various definitions of similarity for a pair of proteins based on different similarity measures of GO terms. Using the sum of similarity scores over the matched GO term pairs for two proteins as the similarity definition produced the best predictive outcome. Substantial improvement in predicting protein subnuclear localizations has been achieved by combining Gene Ontology with sequence information

Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data

<p>Abstract</p> <p>Background</p> <p>Researchers interested in analysing the expression patterns of functionally related genes usually hope to improve the accuracy of their results beyond the boundaries of currently available experimental data. Gene ontology (GO) data provides a novel way to measure the functional relationship between gene products. Many approaches have been reported for calculating the similarities between two GO terms, known as semantic similarities. However, biologists are more interested in the relationship between gene products than in the scores linking the GO terms. To highlight the relationships among genes, recent studies have focused on functional similarities.</p> <p>Results</p> <p>In this study, we evaluated five functional similarity methods using both protein-protein interaction (PPI) and expression data of <it>S. cerevisiae</it>. The receiver operating characteristics (ROC) and correlation coefficient analysis of these methods showed that the maximum method outperformed the other methods. Statistical comparison of multiple- and single-term annotated proteins in biological process ontology indicated that genes with multiple GO terms may be more reliable for separating true positives from noise.</p> <p>Conclusion</p> <p>This study demonstrated the reliability of current approaches that elevate the similarity of GO terms to the similarity of proteins. Suggestions for further improvements in functional similarity analysis are also provided.</p

Gene–disease relationship discovery based on model-driven data integration and database view definition

Motivation: Computational methods are widely used to discover gene–disease relationships hidden in vast masses of available genomic and post-genomic data. In most current methods, a similarity measure is calculated between gene annotations and known disease genes or disease descriptions. However, more explicit gene–disease relationships are required for better insights into the molecular bases of diseases, especially for complex multi-gene diseases