Ingenuity Variant Analysis is a web application that helps researchers studying human disease to identify causal variants from human resequencing data in just minutes. Ingenuity Variant Analysis combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based both upon published biological evidence and your own knowledge of disease biology. With Variant Analysis, you can interrogate your variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up.
Leverage a single application that accesses multiple sources of content to rapidly and comprehensively prioritize variants (including SNV, indels, structural, and CNVs)
Rapidly identify variants across the genome that are known to be deleterious based upon literature evidence (not just predictions)
Choose promising variants for follow-up based on detailed annotations and facts from the literature about implications of known variants and genes containing novel variants on disease biology
Use knowledge of causal regulation relationships to discover compelling hypotheses for the impact of variants on disease progression
Integrate your own data (such as RNA-Seq isoform expression data or epigenetic genome coordinate data) to refine your variant analysis
Leverage disease models that including symptoms, signaling pathways, cellular processes, established disease genes and efficacious drug targets to rapidly identify disease driver mutations
Integrate pharmaceutically relevant content (drug targets and variants implicated in drug response, metabolism, and toxicity) to more rapidly prioritize variants based on pharmacogenomics
Use hereditary relationships and inheritance patterns to identify variants that contribute to disease progression across multiple samples, ranging from a few to hundreds all at once
Interactive Filtering to Rapidly Prioritize Variants
Variant Analysis uses a series of filters that you can apply to quickly exclude common variants (based on 1000 Genomes project) and non-deleterious variants (those predicted to have no effect on protein function or expression), and then relate variants to relevant biology. You can easily ask biological questions to identify variants that impact symptoms, pathways, processes, or genes implicated in disease progression or drug response, based on your expertise and the Ingenuity Knowledge Base of millions of findings from the biomedical literature. This filtering allowing you to go from thousands of variants to a few compelling ones in just minutes, using a simple interface (see below) that works at a variety of biological levels (for example, symptoms or physiological information, mutations, pathways, etc.).
No data is ever lost; it is possible to view all variants included or excluded at each filter step, and change filter settings at any time - each adjustment immediately updates the variants that satisfy each step.
Integrated and accurate evidence for faster results
The biological filtering in Variant Analysis is made possible by the rich biological content in the Ingenuity® Knowledge Base, plus additional sources of variant-level content. Variant Analysis uses millions of accurate biomedical findings curated by experts from the literature and a commonly used 3rd party databases, plus additional content about the effects of mutations on human disease and abnormal phenotypes. This content is quality controlled for accurate results, and structured into a single, comprehensive database (the Ingenuity Knowledge Base) that leverages up-to-date information on pathways, biological processes, and disease models, plus findings about drugs, diseases, genes, and mutations.
Published findings about each variant of interest can be reviewed to assess the likely strength of its effect. Regulatory diagrams of how each variant may impact disease progression with literature supports are readily accessible. When a select number of variants have been identified for follow-up, Variant Analysis provides a report of putative causal variants with a customizable table of annotations about the compelling variants. Additionally, filtering strategies can be shared and reproduced to facilitate collaboration and publication.
Content Sources
Variant Analysis uses primary literature on a large inventory of human germ-line mutations found in patients with particular diseases or abnormal phenotypes with an emphasis on those relevant to cancer. It also incorporates content about somatic mutations found in human tumor samples from the COSMIC database. This includes the specific somatic mutation and the type of tumor and number of samples in which it was found. Variant Analysis also uses information from OMIM about hereditary mutations involved in a wide variety of human disease, plus additional information curated from the literature about the effect of genes and mutations on drug response.
These mutations are mapped to genome coordinates, so they can be compared to variants from resequencing datasets. Information about the frequency of each variant in the population and predictions as to the effect of the mutation on protein function can be used to help enrich for the most compelling variants. Information about the cellular process and signaling pathways associated with some common cancers are available, allowing you to identify the genes associated with these processes as candidates to be involved in disease progression.
Mouse knock-out phenotypes from Jackson Labs are also included to help understand the likely phenotypic consequences of loss-of-function mutations in genes of interest.
A wide variety of content types integrated into one place allows you to ask many kinds of questions:
Disease Models - What variants are associated with breast cancer?
Pathways - What pathways have most deleterious variants in tumor vs. normal samples?
Biomarkers - Which variants are associated with warfarin dosing?
Causal Networks- Which variants are expected to activate genes involved in bone morphogenesis?
Regulatory networks - Which variants would be expected to impact expression of predicted NF-kappaB targets?
Hereditary mutations - Is this variant associated with early onset breast cancer? What is the literature evidence?
Experimental observations - Which variants in BRAF lack kinase activity in HELA cells?
Somatic mutations - Which variants are observed in >10% of melanomas?
Mouse ortholog models - Which variants are deleterious in genes with tumorigenic mouse ortholog knockout phenotypes?
Associations - Which variants are associated with elevated CVD risk at P<10-5 in Framingham SHARe?
Pharmacogenetic and clinically validated - Which variants are associated with response of cystic fibrosis patients to VX-770 treatment?
