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

Abstract

BackgroundThe 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.ResultsBiofilter 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.ConclusionsBiofilter 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.

Pathway analysis by randomization incorporating structure—PARIS: an update

“…As part of the Biofilter 2.0 package, PARIS 2.4 now accesses updated knowledge sources, thus relaying the most pertinent biological information relevant to analysis. PARIS 2.4 now incorporates a more user-friendly interface, in addition to accessing the options available in Biofilter 2.0 (Pendergrass et al, 2013).…”

“…As a result of integrating PARIS into Biolfilter 2.0, analysis options are extended to include all of those available in the Biofilter program (Pendergrass et al, 2013).…”

“…Here we describe PARIS v2.4, a new Python implementation of PARIS, as part of the Biofilter 2.0 software suite (Pendergrass et al, 2013). Figure 1A outlines the updates to PARIS 2.4, where orange elements represent changes to the software tool.…”

Phenome-Wide Association Studies: Embracing Complexity for Discovery

“…Therefore, several studies have adopted prioritization strategies to investigate genegene interactions (G × G) through evaluating the association between specific SNP × SNP regression models and phenotypic outcome. For example, several studies including studies of cataracts [11] , HDL cholesterol [86] , and multiple sclerosis [87] have identified SNP × SNP interactions using a Biofilter [88,89] to biologically filter SNPs based on pathway information. All three studies found intriguing relationships between SNP interactions and phenotypes, highlighting potential interactions between gene products while also providing biological context for the interactions behind the identified associations.…”

“…Exploring the co-occurrence of phenotypic outcomes and genetic variation with additional biological information visualized as a network, such as the previously described 'Human Disease Network', may better represent the genetic architecture of complex traits. For example, we have used the software tool Biofilter [88,89] to annotate PheWAS results with biological knowledge, including information about genes PheWAS SNPs map within as well as known gene-gene relationships, such as pathway information from the Kyoto Encyclopedia of Genes (KEGG) [130] . Then, comprehensive PheWAS association results can be visualized as a network connecting SNPs with their associated phenotypes, using association p values as the edge weights where distance indicates the significance of associations using Cytoscape [131,132] .…”