Abstract
Biochemical pathways are commonly used as a reference to conduct functional analysis on biomedical omics data sets, where experimental results are mapped to knowledgebases comprising known molecular interactions collected from the literature. Due to their central role, the content of the functional knowledgebases directly influences the outcome of pathway analyses. In this study, we investigate the structure of the current pathway knowledge, as exemplified by Reactome, discuss the consequences for biological interpretation, and outline possible improvements in the use of pathway knowledgebases. By providing a view of the underlying protein interaction network, we aim to help pathway analysis users manage their expectations and better identify possible artifacts in the results.
Highlights
In order to interpret the results of biomedical studies in a larger biological context, it is common to perform so-called pathway analysis
Summary Biochemical pathways are commonly used as a reference to conduct functional analysis on biomedical omics datasets, where experimental results are mapped to knowledgebases comprising known molecular interactions collected from the literature
Due to their central role, the content of the functional knowledgebases directly influences the outcome of pathway analyses
Summary
In order to interpret the results of biomedical studies in a larger biological context, it is common to perform so-called pathway analysis. This can provide additional insight into the interactions between the detected compounds and possibly uncover underlying disease mechanisms (Garcia-Campos et al, 2015; Khatri et al, 2012). Pathways are defined as chains of biochemical reactions that together form high-level biological processes. Biases and knowledge gaps in pathway databases directly influence the results (Wadi et al, 2016), and insight into where our knowledge is lacking can be used to guide future research
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