Glioblastoma gene network reconstruction and ontology analysis by online bioinformatics tools.

IF 1.5 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Integrative Bioinformatics Pub Date : 2021-11-16 DOI:10.1515/jib-2021-0031
Natalya V Gubanova, Nina G Orlova, Arthur I Dergilev, Nina Y Oparina, Yuriy L Orlov
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引用次数: 4

Abstract

Glioblastoma is the most aggressive type of brain tumors resistant to a number of antitumor drugs. The problem of therapy and drug treatment course is complicated by extremely high heterogeneity in the benign cell populations, the random arrangement of tumor cells, and polymorphism of their nuclei. The pathogenesis of gliomas needs to be studied using modern cellular technologies, genome- and transcriptome-wide technologies of high-throughput sequencing, analysis of gene expression on microarrays, and methods of modern bioinformatics to find new therapy targets. Functional annotation of genes related to the disease could be retrieved based on genetic databases and cross-validated by integrating complementary experimental data. Gene network reconstruction for a set of genes (proteins) proved to be effective approach to study mechanisms underlying disease progression. We used online bioinformatics tools for annotation of gene list for glioma, reconstruction of gene network and comparative analysis of gene ontology categories. The available tools and the databases for glioblastoma gene analysis are discussed together with the recent progress in this field.

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利用在线生物信息学工具重建胶质母细胞瘤基因网络及本体分析。
胶质母细胞瘤是最具侵袭性的脑肿瘤类型,对许多抗肿瘤药物具有耐药性。良性细胞群异质性极高,肿瘤细胞排列随机,细胞核多态,使治疗和药物疗程问题复杂化。胶质瘤的发病机制需要利用现代细胞技术、全基因组和转录组高通量测序技术、微阵列基因表达分析以及现代生物信息学方法来研究,以寻找新的治疗靶点。基于遗传数据库检索疾病相关基因的功能注释,并通过整合互补实验数据进行交叉验证。一组基因(蛋白质)的基因网络重构被证明是研究疾病进展机制的有效方法。利用在线生物信息学工具对胶质瘤基因表进行标注、基因网络重构和基因本体分类的比较分析。本文讨论了胶质母细胞瘤基因分析的现有工具和数据库,以及该领域的最新进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
期刊最新文献
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