We are analyzing https://link.springer.com/article/10.1186/1471-2105-8-41.

Title:
FUNC: a package for detecting significant associations between gene sets and ontological annotations | BMC Bioinformatics
Description:
Background Genome-wide expression, sequence and association studies typically yield large sets of gene candidates, which must then be further analysed and interpreted. Information about these genes is increasingly being captured and organized in ontologies, such as the Gene Ontology. Relationships between the gene sets identified by experimental methods and biological knowledge can be made explicit and used in the interpretation of results. However, it is often difficult to assess the statistical significance of such analyses since many inter-dependent categories are tested simultaneously. Results We developed the program package FUNC that includes and expands on currently available methods to identify significant associations between gene sets and ontological annotations. Implemented are several tests in particular well suited for genome wide sequence comparisons, estimates of the family-wise error rate, the false discovery rate, a sensitive estimator of the global significance of the results and an algorithm to reduce the complexity of the results. Conclusion FUNC is a versatile and useful tool for the analysis of genome-wide data. It is freely available under the GPL license and also accessible via a web service.
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Keywords {🔍}

categories, test, gene, article, genes, pubmed, data, category, google, scholar, significant, func, tests, fdr, cas, analysis, pvalue, sets, ontology, functional, bioinformatics, methods, significance, null, expression, false, acid, random, distribution, fwer, table, variables, primates, results, implemented, rate, set, amino, hypothesis, annotations, global, annotation, raw, geneassociated, testing, central, number, tested, multiple, estimate,

Topics {✒️}

pgc-1alpha-responsive genes involved article download pdf unix/gnu linux platforms high-throughput gominer family-wise error rate large-scale genomic experiments affiliations max-planck-institute false discovery rate genemerge--post-genomic analysis resampling-based multiple testing kolmogorov-smirnov-type test full size image fewer raw p-values privacy choices/manage cookies related subjects mootha vk genome-wide data genome-wide measurement mcdonald-kreitman type full access expression profiling studies biggest size effect authors’ original file gene expression studies comparing amino acid dna sequence data ben-shaul max planck society article prüfer biomed central gene ontology terms refinement algorithm starts tricarboxylic acid cycle gnu gpl v2 identifying subtle interrelated gene expression data multiple-microarray experiments gene ontology hierarchies gene ontology database ontology molecular function kolmogorov-smirnov test amino acid substitutions global functional profiling analyzing ontological annotations web-based platform global test statistic wilcoxon-rank test wilcoxon rank test sixth framework programme author information authors

Schema {🗺️}

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         description:Genome-wide expression, sequence and association studies typically yield large sets of gene candidates, which must then be further analysed and interpreted. Information about these genes is increasingly being captured and organized in ontologies, such as the Gene Ontology. Relationships between the gene sets identified by experimental methods and biological knowledge can be made explicit and used in the interpretation of results. However, it is often difficult to assess the statistical significance of such analyses since many inter-dependent categories are tested simultaneously. We developed the program package FUNC that includes and expands on currently available methods to identify significant associations between gene sets and ontological annotations. Implemented are several tests in particular well suited for genome wide sequence comparisons, estimates of the family-wise error rate, the false discovery rate, a sensitive estimator of the global significance of the results and an algorithm to reduce the complexity of the results. FUNC is a versatile and useful tool for the analysis of genome-wide data. It is freely available under the GPL license and also accessible via a web service.
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            False Discovery Rate
            Amino Acid Change
            Binomial Test
            Refinement Algorithm
            Bioinformatics
            Microarrays
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            Computer Appl. in Life Sciences
            Algorithms
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      headline:FUNC: a package for detecting significant associations between gene sets and ontological annotations
      description:Genome-wide expression, sequence and association studies typically yield large sets of gene candidates, which must then be further analysed and interpreted. Information about these genes is increasingly being captured and organized in ontologies, such as the Gene Ontology. Relationships between the gene sets identified by experimental methods and biological knowledge can be made explicit and used in the interpretation of results. However, it is often difficult to assess the statistical significance of such analyses since many inter-dependent categories are tested simultaneously. We developed the program package FUNC that includes and expands on currently available methods to identify significant associations between gene sets and ontological annotations. Implemented are several tests in particular well suited for genome wide sequence comparisons, estimates of the family-wise error rate, the false discovery rate, a sensitive estimator of the global significance of the results and an algorithm to reduce the complexity of the results. FUNC is a versatile and useful tool for the analysis of genome-wide data. It is freely available under the GPL license and also accessible via a web service.
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         False Discovery Rate
         Amino Acid Change
         Binomial Test
         Refinement Algorithm
         Bioinformatics
         Microarrays
         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Algorithms
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