Computational functional genomics-based reduction of disease-related gene sets to their key components

Abstract

Motivation The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction are applied to laboratory-derived genetic data, the analysis of topical sets of genes gathered from knowledge bases requires a modified approach as no quantitative information about gene expression is available. Results We propose a computational functional genomics-based approach at reducing sets of genes to the most relevant items based on the importance of the gene within the polyhierarchy of biological processes characterizing the disease. Knowledge bases about the biological roles of genes can provide a valid description of traits or diseases represented as a directed acyclic graph (DAG) picturing the polyhierarchy of disease relevant biological processes. The proposed method uses a gene importance score derived from the location of the gene-related biological processes in the DAG. It attempts to recreate the DAG and thereby, the roles of the original gene set, with the least number of genes in descending order of importance. This obtained precision and recall of over 70% to recreate the components of the DAG charactering the biological functions of n=540 genes relevant to pain with a subset of only the k=29 best-scoring genes. Conclusions A new method for reduction of gene sets is shown that is able to reproduce the biological processes in which the full gene set is involved by over 70%; however, by using only ∼5% of the original genes. Availability and implementation The necessary numerical parameters for the calculation of gene importance are implemented in the R package dbtORA at https://github.com/IME-TMP-FFM/dbtORA. Supplementary information Supplementary data are available at Bioinformatics online.

Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain

“…The d = 29 genes have been shown to cover 70% of the DAG emerging from 540 pain genes, I.e., can be regarded to represent pain completely to this extent [ 17 ]. However, as the present panel had been filled with further genes, the present analyses aimed to functionally characterize the set of d = 72 genes.…”

“…The primary subset of the present panel of pain-relevant genes represents key genes of pain that had emerged in a computational functional genomics-based analysis that considered the position of biological processes in which these genes were involved in the polyhierarchical presentation of pain [ 17 ]. In a previous analysis of the functional genomics of pain [ 8 ], the biological functions characterizing pain had been identified to comprise 12 different components.…”

“…As a basis for the association of human genotypes with pain and the risk for its persistence, we propose a set of 29 genes as key players among the currently known pain-relevant genes [ 17 ]. Specifically, the functional genomics-based architecture of pain has been presented as a polyhierarchy of biological processes [ 8 ] based on the organization of the Gene Ontology knowledge base that captures the current knowledge about the biological roles of all genes and their respective products [ 18 , 19 ].…”

“…Specifically, the functional genomics-based architecture of pain has been presented as a polyhierarchy of biological processes [ 8 ] based on the organization of the Gene Ontology knowledge base that captures the current knowledge about the biological roles of all genes and their respective products [ 18 , 19 ]. With the 29 genes, a respective representation created with 540 pain-relevant genes could be reconstructed by 70%, based on a bioinformatics analysis of the Gene Ontology knowledge base [ 17 ].…”

Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes

“…Its results successfully supported the association of the genetic information with the recognized pain phenotypes. Here, however, machine learning was stopped at that point because the genetic background of pain comprises many more genes—currently about 540 [ 54 , 55 ]—so it is unlikely that the present genes could provide a perfect assignment of subjects to the pain phenotype. The modest accuracy and receiver operator characteristic curve (ROC) area achieved for the assignment of phenotypes is probably not due to the poor selection or implementation of the AI, but rather reflects the truth about the small phenotypic effects of the ion channel genotypes.…”