SNPtoGO: Characterizing SNPs by enriched GO terms

Daniel F. Schwarz; Oliver Hädicke; Jeanette Erdmann; Andreas Ziegler; Daniel Bayer; Steffen Möller

doi:10.1093/bioinformatics/btm551

SNPtoGO: Characterizing SNPs by enriched GO terms

Daniel F. Schwarz^*, Oliver Hädicke, Jeanette Erdmann, Andreas Ziegler, Daniel Bayer, Steffen Möller

^*Corresponding author for this work

11 Citations (Scopus)

Abstract

For the analysis of complex polygenic diseases, one does not expect all patients to share the same disease-associated alleles. Not even will disease-causing variations be assigned to the identical sets of genes between patients. However, one does expect overlaps in the sets of genes that are involved and even more so in their assigned molecular processes. Furthermore, the assignment of single nucleotide polymorphisms (SNPs) to genes is highly ambiguous for intergenic SNPs. The tool presented here hence adds external information, i.e. GeneOntology (GO) terms (Gene Ontology Consortium), to the analysis of SNP data.

Original language	English
Journal	Bioinformatics
Volume	24
Issue number	1
Pages (from-to)	146-148
Number of pages	3
ISSN	1367-4803
DOIs	https://doi.org/10.1093/bioinformatics/btm551
Publication status	Published - 01.01.2008

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title = "SNPtoGO: Characterizing SNPs by enriched GO terms",

abstract = "For the analysis of complex polygenic diseases, one does not expect all patients to share the same disease-associated alleles. Not even will disease-causing variations be assigned to the identical sets of genes between patients. However, one does expect overlaps in the sets of genes that are involved and even more so in their assigned molecular processes. Furthermore, the assignment of single nucleotide polymorphisms (SNPs) to genes is highly ambiguous for intergenic SNPs. The tool presented here hence adds external information, i.e. GeneOntology (GO) terms (Gene Ontology Consortium), to the analysis of SNP data.",

author = "Schwarz, {Daniel F.} and Oliver H{\"a}dicke and Jeanette Erdmann and Andreas Ziegler and Daniel Bayer and Steffen M{\"o}ller",

note = "Funding Information: This work was funded by the DFG (K02250/3–1) and the EU projects KnowARC (032691) and Cardiogenics (037593). The authors thank Thomas Martinetz, Silke Szymczak and the anonymous reviewers for their support and critical reading of the manuscript.",

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T1 - SNPtoGO: Characterizing SNPs by enriched GO terms

AU - Schwarz, Daniel F.

AU - Hädicke, Oliver

AU - Erdmann, Jeanette

AU - Ziegler, Andreas

AU - Bayer, Daniel

AU - Möller, Steffen

N1 - Funding Information: This work was funded by the DFG (K02250/3–1) and the EU projects KnowARC (032691) and Cardiogenics (037593). The authors thank Thomas Martinetz, Silke Szymczak and the anonymous reviewers for their support and critical reading of the manuscript.

PY - 2008/1/1

Y1 - 2008/1/1

N2 - For the analysis of complex polygenic diseases, one does not expect all patients to share the same disease-associated alleles. Not even will disease-causing variations be assigned to the identical sets of genes between patients. However, one does expect overlaps in the sets of genes that are involved and even more so in their assigned molecular processes. Furthermore, the assignment of single nucleotide polymorphisms (SNPs) to genes is highly ambiguous for intergenic SNPs. The tool presented here hence adds external information, i.e. GeneOntology (GO) terms (Gene Ontology Consortium), to the analysis of SNP data.

AB - For the analysis of complex polygenic diseases, one does not expect all patients to share the same disease-associated alleles. Not even will disease-causing variations be assigned to the identical sets of genes between patients. However, one does expect overlaps in the sets of genes that are involved and even more so in their assigned molecular processes. Furthermore, the assignment of single nucleotide polymorphisms (SNPs) to genes is highly ambiguous for intergenic SNPs. The tool presented here hence adds external information, i.e. GeneOntology (GO) terms (Gene Ontology Consortium), to the analysis of SNP data.

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SNPtoGO: Characterizing SNPs by enriched GO terms

Abstract

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