Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development

BioData Mining

Sarah A Pendergrass¹  Alex Frase¹  John Wallace¹  Daniel Wolfe¹  Neerja Katiyar¹  Carrie Moore¹  Marylyn D Ritchie¹ 

[1] Center for Systems Genomics, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, Eberly College of Science, The Huck Institutes of the Life Sciences, University Park, Pennsylvania, PA, USA

关键词: Epistasis;    Pathway analyses;    Modeling;    Expert knowledge;    Bioinformatics;    Data mining;   

Others  :  795178
DOI  :  10.1186/1756-0381-6-25

 received in 2013-08-05, accepted in 2013-12-19,  发布年份 2013

【 摘 要 】

Background

The ever-growing wealth of biological information available through multiple comprehensive database repositories can be leveraged for advanced analysis of data. We have now extensively revised and updated the multi-purpose software tool Biofilter that allows researchers to annotate and/or filter data as well as generate gene-gene interaction models based on existing biological knowledge. Biofilter now has the Library of Knowledge Integration (LOKI), for accessing and integrating existing comprehensive database information, including more flexibility for how ambiguity of gene identifiers are handled. We have also updated the way importance scores for interaction models are generated. In addition, Biofilter 2.0 now works with a range of types and formats of data, including single nucleotide polymorphism (SNP) identifiers, rare variant identifiers, base pair positions, gene symbols, genetic regions, and copy number variant (CNV) location information.

Results

Biofilter provides a convenient single interface for accessing multiple publicly available human genetic data sources that have been compiled in the supporting database of LOKI. Information within LOKI includes genomic locations of SNPs and genes, as well as known relationships among genes and proteins such as interaction pairs, pathways and ontological categories.

Via Biofilter 2.0 researchers can:

• Annotate genomic location or region based data, such as results from association studies, or CNV analyses, with relevant biological knowledge for deeper interpretation

• Filter genomic location or region based data on biological criteria, such as filtering a series SNPs to retain only SNPs present in specific genes within specific pathways of interest

• Generate Predictive Models for gene-gene, SNP-SNP, or CNV-CNV interactions based on biological information, with priority for models to be tested based on biological relevance, thus narrowing the search space and reducing multiple hypothesis-testing.

Conclusions

Biofilter is a software tool that provides a flexible way to use the ever-expanding expert biological knowledge that exists to direct filtering, annotation, and complex predictive model development for elucidating the etiology of complex phenotypic outcomes.

【 授权许可】

   
2013 Pendergrass et al.; licensee BioMed Central Ltd.

【 图 表 】

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