We demonstrate the usefulness of our approach by testing OVA on multiple sets of candidate disease genes and exomes, encompassing known and novel disease genes.
In recent work, we used qRT PCR analysis to quantify inter-tumour differences in the expression of multiple candidate genes associated with disease progression and chemotherapy response in ovarian tumours (Smith et al, 2012).
Multiple candidate genes associated with cancer and mitochondrial diseases, including un-annotated and poorly studied genes, were identified.
Here we present a case study in which we used EnRICH to integrate and filter multiple candidate gene lists in order to identify potential retinal disease genes.
Although a number of computational approaches have been developed to integrate data from multiple sources for the purpose of predicting or prioritizing candidate disease genes, relatively few of them focus on identifying or ranking drug targets.
The purpose of this RFA is to establish an infrastructure for research teams to validate protein panels and to measure multiple candidate markers accurately, for heart, lung, blood, and sleep diseases.
In the above two PPI network-based approaches, those unknown genes directly interact with one or multiple confirmed disease genes are likely to be predicted as candidate disease genes.
Currently, the user may supply candidate disease genes in the following formats: a single or multiple genomic region(s), specified by chromosome and interval boundaries; a pre-filtered VCF file containing potentially deleterious variants (enforced by file size limit); a candidate gene list in a delimited format, accepting several commonly used delimiter types.
This means that additional analysis of multiple candidate genes or other procedures would often be needed to identify the disease gene following exome sequencing of single families with a Mendelian disorder.
Sometimes, multiple candidate norms vie for dominance in a population.
