Literature DB >> 25549900

uGPA: unified Gene Pathway Analyzer package for high-throughput genome-wide screening data provides mechanistic overview on human diseases.

Daniel W H Ho1, Irene O L Ng2.   

Abstract

BACKGROUND: Gene set or pathway analysis (GPA) can provide a comprehensive mechanistic overview in delineating molecular pathoetiology of human diseases. Existing tools that can handle for GPA on both mutational and gene expressional aspects are limited. This leads to the development of uGPA (unified Gene Pathway Analyzer).
METHODS: uGPA package provides a unified solution to knowledge base-driven competitive GPA that can analyze genome-wide screening data derived from multiple platforms (next generation sequencing or microarray) and sample sources (genomic or transcriptomic) on both mutational and gene expressional perspectives. It allows for a quick assessment of perspective outline of gene sets on the studied disease through enrichment test modeled by cumulative hypergeometric distribution and reports the gene components detected with events (mutation or differential gene expression) in the gene sets.
RESULTS: uGPA package has been successfully verified to be applicable on input data generated from NGS-based platforms (transcriptome sequencing and whole-exome sequencing). Genome-wide screening data from other platforms should also apply.
CONCLUSIONS: uGPA package delivers a simple, flexible and platform-independent functionality to multiple aspects of GPA. It equips clinicians and scientists with a useful tool in studying molecular mechanism of human diseases using modern high-throughput genome-wide screening strategy, which assists personalized therapy.
Copyright © 2015. Published by Elsevier B.V.

Entities:  

Keywords:  Differential gene expression; Gene set enrichment; Genome-wide screening; High-throughput; Mutation; Pathway analysis

Mesh:

Year:  2014        PMID: 25549900     DOI: 10.1016/j.cca.2014.12.028

Source DB:  PubMed          Journal:  Clin Chim Acta        ISSN: 0009-8981            Impact factor:   3.786


  2 in total

1.  TCGA whole-transcriptome sequencing data reveals significantly dysregulated genes and signaling pathways in hepatocellular carcinoma.

Authors:  Daniel Wai-Hung Ho; Alan Ka-Lun Kai; Irene Oi-Lin Ng
Journal:  Front Med       Date:  2015-08-14       Impact factor: 4.592

2.  Investigation of Functional Synergism of CENPF and FOXM1 Identifies POLD1 as Downstream Target in Hepatocellular Carcinoma.

Authors:  Daniel Wai-Hung Ho; Wai-Ling Macrina Lam; Lo-Kong Chan; Irene Oi-Lin Ng
Journal:  Front Med (Lausanne)       Date:  2022-07-05
  2 in total

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