Literature DB >> 18434345

Annotation-Modules: a tool for finding significant combinations of multisource annotations for gene lists.

Michael Hackenberg1, Rune Matthiesen.   

Abstract

MOTIVATION: The ontological analysis of the gene lists obtained from DNA microarray experiments constitutes an important step in understanding the underlying biology of the analyzed system. Over the last years, many other high-throughput techniques emerged, covering now basically all 'omics' fields. However, for some of these techniques the generally used functional ontologies might not be sufficient to describe the biological system represented by the derived gene lists. For a more complete and correct interpretation of these experiments, it is important to extend substantially the number of annotations, adapting the ontological analysis to the new emerging techniques.
RESULTS: We developed Annotation-Modules, which offers an improvement over the current tools in two critical aspects. First, the underlying annotation database implements features from many different fields like gene regulation and expression, sequence properties, evolution and conservation, genomic localization and functional categories-resulting in about 60 different annotation features. Second, it examines not only single annotations but also all the combinations, which is important to gain insight into the interplay of different mechanisms in the analyzed biological system. AVAILABILITY: http://web.bioinformatics.cicbiogune.es/AM/AnnotationModules.php

Mesh:

Year:  2008        PMID: 18434345     DOI: 10.1093/bioinformatics/btn178

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  18 in total

Review 1.  Analysing and interpreting DNA methylation data.

Authors:  Christoph Bock
Journal:  Nat Rev Genet       Date:  2012-10       Impact factor: 53.242

2.  Analyzing large biological datasets with association networks.

Authors:  Tatiana V Karpinets; Byung H Park; Edward C Uberbacher
Journal:  Nucleic Acids Res       Date:  2012-05-25       Impact factor: 16.971

3.  WordCluster: detecting clusters of DNA words and genomic elements.

Authors:  Michael Hackenberg; Pedro Carpena; Pedro Bernaola-Galván; Guillermo Barturen; Angel M Alganza; José L Oliver
Journal:  Algorithms Mol Biol       Date:  2011-01-24       Impact factor: 1.405

4.  RuleGO: a logical rules-based tool for description of gene groups by means of Gene Ontology.

Authors:  Aleksandra Gruca; Marek Sikora; Andrzej Polanski
Journal:  Nucleic Acids Res       Date:  2011-07       Impact factor: 16.971

5.  miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments.

Authors:  Michael Hackenberg; Naiara Rodríguez-Ezpeleta; Ana M Aransay
Journal:  Nucleic Acids Res       Date:  2011-04-22       Impact factor: 16.971

6.  sRNAtoolbox: an integrated collection of small RNA research tools.

Authors:  Antonio Rueda; Guillermo Barturen; Ricardo Lebrón; Cristina Gómez-Martín; Ángel Alganza; José L Oliver; Michael Hackenberg
Journal:  Nucleic Acids Res       Date:  2015-05-27       Impact factor: 16.971

7.  Mining rare associations between biological ontologies.

Authors:  Fernando Benites; Svenja Simon; Elena Sapozhnikova
Journal:  PLoS One       Date:  2014-01-03       Impact factor: 3.240

8.  miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments.

Authors:  Michael Hackenberg; Martin Sturm; David Langenberger; Juan Manuel Falcón-Pérez; Ana M Aransay
Journal:  Nucleic Acids Res       Date:  2009-05-11       Impact factor: 16.971

9.  Profile analysis and prediction of tissue-specific CpG island methylation classes.

Authors:  Christopher Previti; Oscar Harari; Igor Zwir; Coral del Val
Journal:  BMC Bioinformatics       Date:  2009-04-21       Impact factor: 3.169

10.  ContDist: a tool for the analysis of quantitative gene and promoter properties.

Authors:  Michael Hackenberg; Gorka Lasso; Rune Matthiesen
Journal:  BMC Bioinformatics       Date:  2009-01-07       Impact factor: 3.169

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