重建疾病基因和关联网络,寻找目标基因

V. Turkina, P. Iarema, A. Mayorova, N. Orlova, E. Savina, Yuriy Orlov
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引用次数: 0

摘要

计算机重建基因网络--具有共同功能的相互作用大分子集--是生物信息学中的一项复杂任务。以图形结构呈现的基因网络提供了一种便捷的可视化手段,并对所研究的基因集合及其生物功能提供了质的新见解。我们将进一步探索与疾病相关的关联基因网络。这些网络不仅包括基因及其产物(蛋白质、代谢产物、非编码 RNA 和药物化合物),还包括常见疾病指标(症状、表型表现)。旨在重建基因网络图的软件工具正在全球范围内积极开发,并在生物医学中发挥着重要作用。我们对现有的基于基因列表和相应计算机分析管道进行基因网络重建的在线生物信息学工具进行了评估。我们深入分析了利用在线程序分析胶质瘤基因网络的实例。建议的方法可扩展到与疾病相关的其他功能基因组。仔细研究与疾病相关的基因网络结构,可以找出关键基因,作为治疗靶点。利用类似的生物信息学策略,我们探索了帕金森病、痴呆症、精神分裂症、乳腺癌和其他癌症等复杂疾病的基因网络。我们深入探讨了基因网络分析计算机程序的应用,并探讨了该领域的教学问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RECONSTRUCTION OF GENE AND ASSOCIATIVE NETWORKS OF DISEASES TO SEARCH FOR TARGET GENES
Computer reconstruction of gene networks—sets of interacting macromolecules with common functions – is a complex task in bioinformatics. Gene networks, presented as graphical structures, offer a convenient means of visualization and provide qualitatively new insights into the set of studied genes and their biological functions. Expanding further, we explore associative gene networks related to diseases. These networks encompass not only genes and their products (proteins, metabolites, non-coding RNA and drug compounds) but also common disease indicators (symptoms, phenotypic manifestations). Software tools aimed at reconstructing gene network graphs are undergoing active development worldwide and find significant utility in biomedicine. We evaluate available online bioinformatics tools for gene network reconstruction based on gene lists and corresponding computer analysis pipelines. We delve into examples showcasing the utilization of online programs for analyzing the glioma gene network. The proposed approach can be extended to other functional gene sets linked to diseases. Scrutinizing the structure of disease-associated gene networks enables the identification of pivotal genes, which can serve as therapeutic targets. Employing similar bioinformatics strategies, gene networks of intricate disorders such as Parkinson's disease, dementia, schizophrenia, breast cancer, and other cancers have been explored. We delve into the applications of computer programs for gene network analysis and address pedagogical aspects of the field.
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