Computational Functional Genomics-Based Approaches in Analgesic Drug Discovery and Repurposing

Abstract

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library 'dbtORA'.

A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

“…Bottom: The GO terms (biological processes) taken from the functional areas, shown above in the DAG , are shown with larger fonts for better readability. The figure was created using the R software package (version 3.3.2 for Linux; http://CRAN.R-project.org/; R Development Core Team, ) and our R package ‘dbt ORA ’ ( https://github.com/IME-TMP-FFM/dbtORA; Lippmann et al., ). The Venn diagram was drawn using the R library ‘Vennerable’ (Swinton J., https://r-forge.r-project.org/R/?group_id=474).…”

“…In this graph, the top‐down, branching polyhierarchy of GO terms starts with the most broadly defined terms and progresses towards the branches, representing GO terms with the narrowest definition (details). These calculations were made using our R package ‘dbtORA’ (https://github.com/IME-TMP-FFM/dbtORA; Lippmann et al., ), which has been designed for knowledge discovery in the GO.…”

Development of an AmpliSeqTM Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain

“…This implied a functional bias and therefore, the present set of d = 77 genes ( Figure 4 ) was analyzed whether this bias prevailed when comparing it with the alternative sets of human genes proposed to modulate persisting pain ( Mogil, 2012 ; Zorina-Lichtenwalter et al, 2016 ). As applied previously ( Lippmann et al, 2018 ), the biological roles of the set of d = 77 genes were queried from the Gene Ontology knowledgebase (GO) 11 ( Ashburner et al, 2000 ) where the knowledge about the biological processes, the molecular functions and the cellular components of genes is formulated using a controlled and clearly defined vocabulary of GO terms. Particular biological roles of the set of d = 77 genes, among all human genes, were analyzed by means of over-representation analysis (ORA).…”

“…In contrast to enrichment analysis, any quantitative criteria such as gene expression values are disregarded ( Backes et al, 2007 ). The analyses were performed using our R library “dbtORA” ( Lippmann et al, 2018 ) 12 on the R software environment (version 3.4.2 for Linux; R Development Core Team, 2008 ) 13 .…”

“…The graphical representation follows the standard of the GO knowledgebase, where GO terms are related to each other by “is-a,” “part-of,” and “regulates” relationships forming a polyhierarchy organized in a directed acyclic graph (DAG, Thulasiraman and Swamy, 1992 ). The figure has been created using our R library “dbtORA” ( https://github.com/IME-TMP-FFM/dbtORA , Lippmann et al, 2018 ) on the R software package (version 3.4.2 for Linux; http://CRAN.R-project.org/ , R Development Core Team, 2008 ) and the freely available graph visualization software GraphViz ( http://www.graphviz.org , Gansner and North, 2000 ).…”