A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

Abstract

Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms

“…Recent review from Zolotareva et al . 13 described 14 up-to-date and available gene prioritization tools. From those, 7 tools were adapted to our problematic and 5 where fully operational: Phenolyzer 30 , Endeavour 17 , MaxLink 21 , ToppGene 18 , ToppNet 18 .…”

“…To help decipher the picture of the deregulated molecular networks and prioritize disease candidates, computational methods and tools have been proposed 13 . Some approaches prioritize candidates based on their similarity to the list of disease-modified genes 14 .…”

“…One of the main limitations hampering the use of IPA is that the software is proprietary and therefore its use cannot be broadly generalized to the biology community. Many other computational prioritization methods already exist 13 . Some are looking for candidates that directly interact with known disease genes, following the principle of “guilt-by-association” 14 , 25 .…”

Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes

“…Several approaches for prioritization of gene candidates have been described in the literature to narrow down likely candidate genes. In general, these methods used the topology of protein–protein interaction networks (PPINs) together with various other data types to retrieve a measure of the importance of different genes or gene products in the regulation of the disease of interest [ 19 ]. Several methods are cancer-specific, often require quantitative patient data as inputs [ 20 ], while other methods require manual setting of parameters [ 21 ].…”

“…Data integration is a challenge since disparate sources have variations in gene names, data quality, etc. A recent review [ 19 ] has summarised the approaches and tools developed in the field.…”