In recent decades, the development of next-generation sequencing (NGS)
technologies has made it possible to understand molecular mechanisms at the basis of
various genetic diseases. The huge amount of data obtained from these experiments
must be carefully analyzed. One of the most sensitive steps consists of gene
prioritization, already performed by several widely used computational tools such as
Endeavour, ToppGene, and Candid, to obtain only the genes that are most probably
associated with the disease of interest. Furthermore, among these genes, it is important
to choose those that show the highest statistical significance, to obtain a more reliable
result. This represents one of the major limitations for many researchers. In this work,
we propose an innovative method that could help researchers reduce a large amount of
data by applying filters before the prioritization process that is carried out by
Toppgene, today considered the most powerful tool. We performed prioritization of
candidate genes obtained by whole-exome sequencing (WES) on a patient affected by
an orphan form of retinitis pigmentosa. We obtained new mutations and polymorphic
variants in known associated/causative and yet unrelated genes. The upstream
application of different filters allowed us to work with a smaller number of genes and
therefore, to produce a lower statistical bias. Furthermore, Toppgene has proven to be a
complete, reliable tool for carrying out the prioritization process.
Keywords: Bioinformatics, Biostatistics, Candid, Conditional Formatting, CLC
Genomics Workbench, Disease Ontology, DNA, DNASTAR Lasergene Suite,
Excel, Gene Prioritization, GO, Human Phenotype, Mouse Phenotype, Molecular
Function, NGS, Pipeline, ToppGene, RP, WGS, WES.
