GeneTools – application for functional annotation and statistical hypothesis testing

Vidar Beisvag,Hallgeir Bergum,Clara-Cecilie Günther,Stian Lydersen,Mette Langaas,Frode KR Jünge,Heri Ramampiaro,Astrid Lægreid,Lars Jølsum,Arne K Sandvik

doi:10.1186/1471-2105-7-470

Vidar Beisvag, Hallgeir Bergum + Show 8 more

Open Access

https://doi.org/10.1186/1471-2105-7-470

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Abstract

BackgroundModern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions.ResultsGeneTools is a web-service providing access to a database that brings together information from a broad range of resources. The annotation data are updated weekly, guaranteeing that users get data most recently available. Data submitted by the user are stored in the database, where it can easily be updated, shared between users and exported in various formats. GeneTools provides three different tools: i) NMC Annotation Tool, which offers annotations from several databases like UniGene, Entrez Gene, SwissProt and GeneOntology, in both single- and batch search mode. ii) GO Annotator Tool, where users can add new gene ontology (GO) annotations to genes of interest. These user defined GO annotations can be used in further analysis or exported for public distribution. iii) eGOn, a tool for visualization and statistical hypothesis testing of GO category representation. As the first GO tool, eGOn supports hypothesis testing for three different situations (master-target situation, mutually exclusive target-target situation and intersecting target-target situation). An important additional function is an evidence-code filter that allows users, to select the GO annotations for the analysis.ConclusionGeneTools is the first "all in one" annotation tool, providing users with a rapid extraction of highly relevant gene annotation data for e.g. thousands of genes or clones at once. It allows a user to define and archive new GO annotations and it supports hypothesis testing related to GO category representations. GeneTools is freely available through www.genetools.no

Highlights

Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes
Microarray technology allows researchers to monitor transcript levels of thousands of genes in a single experiment [1]. It confronts the researcher with vast amounts of numerical data as a starting point from which to begin to investigate how molecular mechanisms are involved in a specific biological setting
Single search The database enables searching by gene symbols/names, GenBank accession numbers, UniGene cluster IDs, SwissProt entry names and several unique clone IDs (IMAGE clone IDs, University of Iowa clone IDs, Operon oligo IDs, TAIR IDs and a subset of selected Affymetrix and Agilent IDs)

Summary

Introduction

Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. Such tools often leave the investigator with large volumes of apparently unorganized information. Microarray technology allows researchers to monitor transcript levels of thousands of genes in a single experiment [1] It confronts the researcher with vast amounts of numerical data as a starting point from which to begin to investigate how molecular mechanisms are involved in a specific biological setting. Annotation of genes with GO terms creates a biological knowledge profile, in three layers dependent on the toplevel GO branch used (biological process, molecular function or cellular component)

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