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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cerami, E.G., et al.: Pathway commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685–D690 (2010)
Demir, E., et al.: The BioPAX community standard for pathway data sharing. Nat. Biotechnol. 28(9), 935 (2010)
Fabregat, A., et al.: The reactome pathway knowledgebase. Nucleic Acids Res. 46(D1), D649–D655 (2017)
Fabregat, A., et al.: Reactome pathwayanalysis: a high-performance in-memory approach. BMC Bioinformatics 18(1), 142 (2017). https://doi.org/10.1186/s12859-017-1559-2
Gu, Z., Wang, J.: CePa: an R package for finding significant pathways weighted by multiple network centralities. Bioinformatics 29(5), 658–660 (2013)
Kanehisa, M., Goto, S.: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 27–30 (2000)
Liu, D., et al.: Pathway enrichment analysis approach based on topological structure and updated annotation of pathway. Briefings in Bioinf. 20(1), 168–177 (2017)
Perfetto, L., et al.: SIGNOR: a database of causal relationships between biological entities. Nucleic Acids Res. 44(D1), D548–D554 (2015)
Rahmati, S., Abovsky, M., Pastrello, C., Jurisica, I.: pathDIP: an annotated resource for known and predicted human gene-pathway associations and pathway enrichment analysis. Nucleic Acids Res. 45(D1), D419–D426 (2016)
Tarca, A.L., et al.: A novel signaling pathway impact analysis. Bioinformatics 25(1), 75–82 (2009). https://doi.org/10.1093/bioinformatics/btn577. https://www.ncbi.nlm.nih.gov/pubmed/18990722
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-39081-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39080-8
Online ISBN: 978-3-030-39081-5
eBook Packages: Computer ScienceComputer Science (R0)