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基因凝视者:一个整合两条管道用于个性化分析和生物标志物识别的工具包。

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

作者信息

Luo Ji-Dung, Chang Yu-Jia, Chang Chung-Ming, You Jeng-Fu, Wei Po-Li, Chiou Chiuan-Chian

机构信息

Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C.

Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, R.O.C. Division of General Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei, Taiwan, R.O.C. Department of Surgery, College of Medicine, Taipei Medical University, Taipei, Taiwan, R.O.C. Cancer Research Center, Taipei Medical University Hospital, Taipei, Taiwan, R.O.C.

出版信息

Cancer Genomics Proteomics. 2016 Mar-Apr;13(2):141-50.

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.

摘要

背景

下一代测序可提供有关基因突变、基因表达、表观遗传修饰、微小RNA表达及拷贝数变异的有用信息。越来越多的计算工具已被开发用于分析此类大量信息。然而,对于几乎没有生物信息学培训的用户而言,测试并找到合适的分析工具以及整合其结果既繁琐又具有挑战性。在本研究中,我们将计算工具整合到一个便捷的工具包中,以简化生物信息学工具间数据的分析与整合。

材料与方法

该工具包GeneGazer由两部分组成:第一部分名为Gaze_Profiler,旨在根据配对样本的下一代测序数据进行个性化分子分析;另一部分名为Gaze_BioSigner,旨在从队列研究的表达谱和突变谱中发现疾病相关的生物标志物。

结果

为展示Gaze_Profiler的功能,我们分析了从序列读取存档数据库下载的一名患者的一对(结肠癌和相邻正常组织)RNA测序数据,并用于分析体细胞突变和数字基因表达。在此例中,鉴定出RAS/RAF/MEK/ERK信号通路(由KRAS G13D突变激活)和经典WNT信号通路(由截短的APC激活)的改变;未发现EGFR突变或过表达。这些数据提示西妥昔单抗对此患者疗效有限。为展示Gazer_BioSigner的能力,我们分析了从癌症基因组图谱下载的192个癌组织的基因表达数据,发现cAMP/PKA信号通路、OCT-3/4和SRF的激活与结肠癌进展相关,且可能是潜在的治疗靶点。

结论

GeneGazer是用于分析高通量平台数据的可靠且强大的工具包,具有临床应用和生物医学研究的潜力。

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