Dataset Information

TAFFEL: Independent Enrichment Analysis of gene sets.

ABSTRACT:

Background

A major challenge in genomic research is identifying significant biological processes and generating new hypotheses from large gene sets. Gene sets often consist of multiple separate biological pathways, controlled by distinct regulatory mechanisms. Many of these pathways and the associated regulatory mechanisms might be obscured by a large number of other significant processes and thus not identified as significant by standard gene set enrichment analysis tools.

Results

We present a novel method called Independent Enrichment Analysis (IEA) and software TAFFEL that eases the task by clustering genes to subgroups using Gene Ontology categories and transcription regulators. IEA indicates transcriptional regulators putatively controlling biological functions in studied condition.

Conclusions

We demonstrate that the developed method and TAFFEL tool give new insight to the analysis of differentially expressed genes and can generate novel hypotheses. Our comparison to other popular methods showed that the IEA method implemented in TAFFEL can find important biological phenomena, which are not reported by other methods.

SUBMITTER: Kurki MI

PROVIDER: S-EPMC3120704 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Publications

TAFFEL: Independent Enrichment Analysis of gene sets.

Kurki Mitja I MI Paananen Jussi J Storvik Markus M Ylä-Herttuala Seppo S Jääskeläinen Juha E JE von Und Zu Fraunberg Mikael M Wong Garry G Pehkonen Petri P

BMC bioinformatics 20110519

<h4>Background</h4>A major challenge in genomic research is identifying significant biological processes and generating new hypotheses from large gene sets. Gene sets often consist of multiple separate biological pathways, controlled by distinct regulatory mechanisms. Many of these pathways and the associated regulatory mechanisms might be obscured by a large number of other significant processes and thus not identified as significant by standard gene set enrichment analysis tools.<h4>Results</h ...[more]

PMID: 21592412

Similar Datasets

Project description:BACKGROUND:The Gene Ontology (GO) consists of over 40,000 terms for biological processes, cell components and gene product activities linked into a graph structure by over 90,000 relationships. It has been used to annotate the functions and cellular locations of several million gene products. The graph structure is used by a variety of tools to group annotated genes into sets whose products share function or location. These gene sets are widely used to interpret the results of genomics experiments by assessing which sets are significantly over- or under-represented in results lists. F Hoffmann-La Roche Ltd. has developed a bespoke, manually maintained controlled vocabulary (RCV) for use in over-representation analysis. Many terms in this vocabulary group GO terms in novel ways that cannot easily be derived using the graph structure of the GO. For example, some RCV terms group GO terms by the cell, chemical or tissue type they refer to. Recent improvements in the content and formal structure of the GO make it possible to use logical queries in Web Ontology Language (OWL) to automatically map these cross-cutting classifications to sets of GO terms. We used this approach to automate mapping between RCV and GO, largely replacing the increasingly unsustainable manual mapping process. We then tested the utility of the resulting groupings for over-representation analysis. RESULTS:We successfully mapped 85% of RCV terms to logical OWL definitions and showed that these could be used to recapitulate and extend manual mappings between RCV terms and the sets of GO terms subsumed by them. We also show that gene sets derived from the resulting GO terms sets can be used to detect the signatures of cell and tissue types in whole genome expression data. CONCLUSIONS:The rich formal structure of the GO makes it possible to use reasoning to dynamically generate novel, biologically relevant groupings of GO terms. GO term groupings generated with this approach can be used in. over-representation analysis to detect cell and tissue type signatures in whole genome expression data.

Dataset Information

TAFFEL: Independent Enrichment Analysis of gene sets.

Background

Results

Conclusions

Publications

TAFFEL: Independent Enrichment Analysis of gene sets.

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