GeneGazer: A Toolkit Integrating Two Pipelines for Personalized Profiling and Biosignature Identification.

Abstract

BACKGROUND:

Next-generation sequencing provides useful information about gene mutations, gene expression, epigenetic modification, microRNA expression, and copy number variations. More and more computing tools have been developed to analyze this large quantity of information. However, to test and find suitable analytical tools and integrate their results is tedious and challenging for users with little bioinformatics training. In the present study, we assembled the computing tools into a convenient toolkit to simplify the analysis and integration of data between bioinformatics tools.

MATERIALS AND METHODS:

The toolkit, GeneGazer, comprises of two parts: the first, named Gaze_Profiler, was designed for personalized molecular profiling from next-generation sequencing data of paired samples; the other, named Gaze_BioSigner, was designed for the discovery of disease-associated biosignatures from expressional and mutational profiles of a cohort study.

RESULTS:

To demonstrate the capabilities of Gaze_Profiler, we analyzed a pair (colon cancer and adjacent normal tissues) of RNA-sequencing data from one patient downloaded from the Sequencing Read Archive database and used them to profile somatic mutations and digital gene expression. In this case, alterations in the RAS/RAF/MEK/ERK signaling pathway (activated by KRAS G13D mutation) and canonical WNT signaling pathway (activated by truncated APC) were identified; no EGFR mutation or overexpression was found. These data suggested a limited efficacy of cetuximab in the patient. To demonstrate the ability of Gazer_BioSigner, we analyzed gene-expression data from 192 cancer tissues downloaded from The Cancer Genome Atlas and found that the activation of cAMP/PKA signaling, OCT-3/4 and SRF were associated with colon cancer progression and could be potential therapeutic targets.

CONCLUSION:

GeneGazer is a reliable and robust toolkit for the analysis of data from high-throughput platforms and has potential for clinical application and biomedical research.

• Authors: Ji-Dung Luo, Yu-Jia Chang, Chung-Ming Chang, Jeng-Fu You, Po-Li Wei, Chiuan-Chian Chiou
• Journal: Cancer genomics & proteomics (Cancer Genomics Proteomics) 2016 Mar-Apr Vol. 13 Issue 2 Pg. 141-50 ISSN: 1790-6245 [Electronic] Greece
• PMID: 26912804 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright© 2016, International Institute of Anticancer Research (Dr. John G. Delinasios), All rights reserved.

Chemical References

• Transcription Factors

Topics

• Gene Expression Profiling (methods)
• High-Throughput Nucleotide Sequencing (methods)
• Humans
• Signal Transduction (genetics)
• Software
• Transcription Factors (metabolism)