gep2pep: a Bioconductor package for the creation and analysis of pathway-based expression profiles.

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Type
Article
Authors
Napolitano, Farancesco cc
Carrella, Diego
Gao, Xin cc
di Bernardo, Diego
KAUST Department
Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
Computer Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2019-10-25
Permanent link to this record
http://hdl.handle.net/10754/660056

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Abstract
SUMMARY:Pathway-based expression profiles allow for high-level interpretation of transcriptomic data and systematic comparison of dysregulated cellular programs. We have previously demonstrated the efficacy of pathway-based approaches with two different applications: the Drug Set Enrichment Analysis and the Gene2drug analysis. Here we present a software tool that allows to easily convert gene-based profiles to pathway-based profiles and analyze them within the popular R framework. We also provide pre-computed profiles derived from the original Connectivity Map and its next generation release, i.e. the LINCS database. AVAILABILITY AND IMPLEMENTATION:the tool is implemented as the R/Bioconductor package gep2pep and can be freely downloaded from https://bioconductor.org/packages/gep2pep. SUPPLEMENTARY INFORMATION:Supplementary data are available at http://dsea.tigem.it/lincs.
Citation
Napolitano, F., Carrella, D., Gao, X., & di Bernardo, D. (2019). gep2pep: a Bioconductor package for the creation and analysis of pathway-based expression profiles. Bioinformatics. doi:10.1093/bioinformatics/btz803
Sponsors
Funding: This work has been supported by Fondazione Telethon and Fondazione Veronesi.
Publisher
Oxford University Press (OUP)
Journal
Bioinformatics
DOI
10.1093/bioinformatics/btz803
Additional Links
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz803/5606711
Collections
Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
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Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.