1,034 research outputs found

    Seeing the Forest for the Trees: Using the Gene Ontology to Restructure Hierarchical Clustering

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    Motivation: There is a growing interest in improving the cluster analysis of expression data by incorporating into it prior knowledge, such as the Gene Ontology (GO) annotations of genes, in order to improve the biological relevance of the clusters that are subjected to subsequent scrutiny. The structure of the GO is another source of background knowledge that can be exploited through the use of semantic similarity. Results: We propose here a novel algorithm that integrates semantic similarities (derived from the ontology structure) into the procedure of deriving clusters from the dendrogram constructed during expression-based hierarchical clustering. Our approach can handle the multiple annotations, from different levels of the GO hierarchy, which most genes have. Moreover, it treats annotated and unannotated genes in a uniform manner. Consequently, the clusters obtained by our algorithm are characterized by significantly enriched annotations. In both cross-validation tests and when using an external index such as protein–protein interactions, our algorithm performs better than previous approaches. When applied to human cancer expression data, our algorithm identifies, among others, clusters of genes related to immune response and glucose metabolism. These clusters are also supported by protein–protein interaction data. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.Lynne and William Frankel Center for Computer Science; Paul Ivanier center for robotics research and production; National Institutes of Health (R01 HG003367-01A1

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

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    <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

    Metrics for GO based protein semantic similarity: a systematic evaluation

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    <p>Abstract</p> <p>Background</p> <p>Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic similarity in this context, since there is no conclusive evaluation of the various measures. Another issue, is whether electronic annotations should or not be used in semantic similarity calculations.</p> <p>Results</p> <p>We conducted a systematic evaluation of GO-based semantic similarity measures using the relationship with sequence similarity as a means to quantify their performance, and assessed the influence of electronic annotations by testing the measures in the presence and absence of these annotations. We verified that the relationship between semantic and sequence similarity is not linear, but can be well approximated by a rescaled Normal cumulative distribution function. Given that the majority of the semantic similarity measures capture an identical behaviour, but differ in resolution, we used the latter as the main criterion of evaluation.</p> <p>Conclusions</p> <p>This work has provided a basis for the comparison of several semantic similarity measures, and can aid researchers in choosing the most adequate measure for their work. We have found that the hybrid <it>simGIC</it> was the measure with the best overall performance, followed by Resnik's measure using a best-match average combination approach. We have also found that the average and maximum combination approaches are problematic since both are inherently influenced by the number of terms being combined. We suspect that there may be a direct influence of data circularity in the behaviour of the results including electronic annotations, as a result of functional inference from sequence similarity.</p

    Information content-based gene ontology functional similarity measures: which one to use for a given biological data type?

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    The current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration

    A Method for Integrating Heterogeneous Datasets based on GO Term Similarity

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    This thesis presents a method for integrating heterogeneous gene/protein datasets at the functional level based on Gene Ontology term similarity. Often biologists want to integrate heterogeneous data sets obtain from different biological samples. A major challenge in this process is how to link the heterogeneous datasets. Currently, the most common approach is to link them through common reference database identifiers which tend to result in small number of matching identifiers. This is due to lack of standard accession schemes. Due to this problem, biologists may not recognize the underlying biological phenomena revealed by a combination of the data but by each data set individually. We discuss an approach for integrating heterogeneous datasets by computing the similarity among them based on the similarity of their GO annotations. Then we group the genes and/or proteins with similar annotations by applying a hierarchical clustering algorithm. The results demonstrate a more comprehensive understanding of the biological processes involved

    Fuzzy measures on the Gene Ontology for gene product similarity

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    pre-printOne of the most important objects in bioinformatics is a gene product (protein or RNA). For many gene products, functional information is summarized in a set of Gene Ontology (GO) annotations. For these genes, it is reasonable to include similarity measures based on the terms found in the GO or other taxonomy. In this paper, we introduce several novel measures for computing the similarity of two gene products annotated with GO terms. The fuzzy measure similarity (FMS) has the advantage that it takes into consideration the context of both complete sets of annotation terms when computing the similarity between two gene products. When the two gene products are not annotated by common taxonomy terms, we propose a method that avoids a zero similarity result. To account for the variations in the annotation reliability, we propose a similarity measure based on the Choquet integral. These similarity measures provide extra tools for the biologist in search of functional information for gene products. The initial testing on a group of 194 sequences representing three proteins families shows a higher correlation of the FMS and Choquet similarities to the BLAST sequence similarities than the traditional similarity measures such as pairwise average or pairwise maximum

    Quantitative assessment of relationship between sequence similarity and function similarity

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    <p>Abstract</p> <p>Background</p> <p>Comparative sequence analysis is considered as the first step towards annotating new proteins in genome annotation. However, sequence comparison may lead to creation and propagation of function assignment errors. Thus, it is important to perform a thorough analysis for the quality of sequence-based function assignment using large-scale data in a systematic way.</p> <p>Results</p> <p>We present an analysis of the relationship between sequence similarity and function similarity for the proteins in four model organisms, i.e., <it>Arabidopsis thaliana, Saccharomyces cerevisiae, Caenorrhabditis elegans</it>, and <it>Drosophila melanogaster</it>. Using a measure of functional similarity based on the three categories of Gene Ontology (GO) classifications (biological process, molecular function, and cellular component), we quantified the correlation between functional similarity and sequence similarity measured by sequence identity or statistical significance of the alignment and compared such a correlation against randomly chosen protein pairs.</p> <p>Conclusion</p> <p>Various sequence-function relationships were identified from BLAST versus PSI-BLAST, sequence identity versus Expectation Value, GO indices versus semantic similarity approaches, and within genome versus between genome comparisons, for the three GO categories. Our study provides a benchmark to estimate the confidence in assignment of functions purely based on sequence similarity.</p

    Disjunctive shared information between ontology concepts: application to Gene Ontology

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    <p>Abstract</p> <p>Background</p> <p>The large-scale effort in developing, maintaining and making biomedical ontologies available motivates the application of similarity measures to compare ontology concepts or, by extension, the entities described therein. A common approach, known as semantic similarity, compares ontology concepts through the information content they share in the ontology. However, different disjunctive ancestors in the ontology are frequently neglected, or not properly explored, by semantic similarity measures.</p> <p>Results</p> <p>This paper proposes a novel method, dubbed DiShIn, that effectively exploits the multiple inheritance relationships present in many biomedical ontologies. DiShIn calculates the shared information content of two ontology concepts, based on the information content of the disjunctive common ancestors of the concepts being compared. DiShIn identifies these disjunctive ancestors through the number of distinct paths from the concepts to their common ancestors.</p> <p>Conclusions</p> <p>DiShIn was applied to Gene Ontology and its performance was evaluated against state-of-the-art measures using CESSM, a publicly available evaluation platform of protein similarity measures. By modifying the way traditional semantic similarity measures calculate the shared information content, DiShIn was able to obtain a statistically significant higher correlation between semantic and sequence similarity. Moreover, the incorporation of DiShIn in existing applications that exploit multiple inheritance would reduce their execution time.</p
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