CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome.

Autor: Nguyen H; Department of Computer Science and Software Engineering, Auburn University, AL 36849, USA., Pham VD; Department of Computer Science and Software Engineering, Auburn University, AL 36849, USA., Nguyen H; Department of Computer Science and Software Engineering, Auburn University, AL 36849, USA., Tran B; Department of Computer Science, California State University, Sacramento, CA 95819, USA., Petereit J; Nevada Bioinformatics Center, University of Nevada, Reno, NV 89557, USA., Nguyen T; Department of Computer Science and Software Engineering, Auburn University, AL 36849, USA.
Jazyk: angličtina
Zdroj: Briefings in bioinformatics [Brief Bioinform] 2024 Jul 23; Vol. 25 (Supplement_1).
DOI: 10.1093/bib/bbae222
Abstrakt: This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.
(© The Author(s) 2024. Published by Oxford University Press.)
Databáze: MEDLINE
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