Our research services group uses a set of advanced software tools designed for whole genome and exome interpretation. These tools are also available to our clients through our knomeBASE informatics service. In addition to various scripts, libraries, and conversion utilities, these tools include knomeVARIANTS and knomePATHWAYS.
knomeVARIANTS is a query kit that lets users search for candidate causal variants in studied genomes. It includes a query interface (see above), scripting libraries, and data conversion utilities.
Users select cases and controls, input a putative inheritance mode, and add sensible filter criteria (variant functional class, rarity/novelty, location in prior candidate regions, etc.) to automatically generate a sorted short-list of leading candidates. The application includes a SQL query interface to let users query the database as they wish, including by complex or novel sets of criteria.
In addition to querying, the application lets users export subsets of the database for viewing in MS Excel. Subsets can be output that target common research foci, including the following:
- Sites implicated in phenotypes, regardless of subject genotypes
- Sites where at least one studied genome mismatches the reference
- Sites where a particular set of one or more genomes, but no other genomes, show a novel variant
- Sites in phenotype-implicated genes
- Sites with nonsense, frameshift, splice-site, or read-through variants, relative to reference
- Sites where some but not all subject genome were called
knomePATHWAYS is a visualization tool that overlays variants found in each sample genome onto known gene interaction networks in order to help spot functional interactions between variants in distinct genes, and pathways enriched for variants in cases versus controls, differential drug responder groups, etc.
knomePATHWAYS integrates reference data from many sources, including GO, HPRD, and MsigDB (which includes KEGG and Reactome data). The application is particularly helpful in addressing higher-order questions, such as finding candidate genes and protein pathways, that are not readily addressed from tabular annotation data alone.