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

Sarah A. Pendergrass, Alex Frase, John Wallace, Daniel Wolfe, Neerja Katiyar, Carrie Moore, Marylyn D. Ritchie

Research output: Contribution to journalArticle

36 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number25
JournalBioData Mining
Volume6
Issue number1
DOIs
StatePublished - Dec 30 2013

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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    Pendergrass, S. A., Frase, A., Wallace, J., Wolfe, D., Katiyar, N., Moore, C., & Ritchie, M. D. (2013). Genomic analyses with biofilter 2.0: Knowledge driven filtering, annotation, and model development. BioData Mining, 6(1), [25]. https://doi.org/10.1186/1756-0381-6-25