Abstract
Biological pathways are complex networks able to provide a view on the interactions among bio-molecules inside the cell. They are represented as a network, where the nodes are the bio-molecules, and the edges represent the interactions between two biomolecules. Main online repositories of pathways information include KEGG that is a repository of metabolic pathways, SIGNOR that comprises primarily signaling pathways, and Reactome that contains information about metabolic and signal transduction pathways. Pathways enrichment analysis is employed to help the researchers to discriminate relevant proteins involved in the development of both simple and complex diseases, and is performed with several software tools. The main limitation of the current enrichment tools are: (i) each tool can use only a single pathway source to compute the enrichment; (ii) researchers have to repeat the enrichment analysis several times with different tools (able to get pathway data from different data sources); (iii) enrichment results have to be manually merged by the user, a tedious and error-prone task even for a computer scientist. To face this issues, we propose a parallel enrichment tool named Parallel Enrichment Analysis (PEA) ables to retrieve at the same time pathways information from KEGG, Reactome, and SIGNOR databases, with which to automatically perform pathway enrichment analysis, allowing to reduce the computational time of some order of magnitude, as well as the automatic merging of the results.
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Agapito, G., Cannataro, M. (2020). A Parallel Software Platform for Pathway Enrichment. In: Sergeyev, Y., Kvasov, D. (eds) Numerical Computations: Theory and Algorithms. NUMTA 2019. Lecture Notes in Computer Science(), vol 11973. Springer, Cham. https://doi.org/10.1007/978-3-030-39081-5_19
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DOI: https://doi.org/10.1007/978-3-030-39081-5_19
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