Interdisciplinary Sciences: Computational Life Sciences ( IF 3.9 ) Pub Date : 2021-05-18 , DOI: 10.1007/s12539-021-00440-9 Mustafa Özgür Cingiz 1 , Göksel Biricik 2 , Banu Diri 2
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Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene–gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA–target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs. Users can access the GNIAP R package via https://github.com/ozgurcingiz/GNIAP.
Graphic abstract
中文翻译：
使用不同选择标准的乳腺癌和前列腺癌基因共表达网络的性能比较
基因共表达网络 (GCN) 呈现基因之间的无向关系，以了解疾病（包括癌症）背后的分子结构。利用各种生物数据集和基因网络推理 (GNI) 算法可以揭示 GCN 有意义的基因-基因相互作用。本研究在 mRNA 基因表达、RNA-Seq 和 miRNA 靶基因数据集上应用三种 GNI 算法来推断乳腺癌和前列腺癌的 GCN。为了评估 GCN 的性能，我们通过文献数据、拓扑评估和基于基因本体的生物学评估利用重叠分析。结果强调了生物数据集的选择和 GNI 算法如何影响不同评估标准下的性能结果。微阵列基因表达数据上的 GCN 在重叠分析中略胜一筹。还，RNA-Seq 和基因表达数据集上的 GCN 遵循无标度拓扑。在所有生物数据集上，生物评估结果彼此接近。基于 C3NET 算法的 GCN 不包含任何生物评估模块；因此，它不是生物学评估的最佳选择。GNI 算法的选择并没有改变重叠分析和拓扑评估结果。我们的主要目标是比较基于不同评估标准的生物数据集和 GNI 算法的性能结果。为此，我们开发了 GNIAP R 包，使用户能够选择不同的 GNI 算法来推断 GCN。GNIAP R 包还提供基于文献的重叠分析，以及对 GCN 的拓扑和生物学分析。用户可以通过 https://github.com/ozgurcingiz/GNIAP 访问 GNIAP R 包。
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