WINNER: A network biology tool for biomolecular characterization and prioritization.
WINNER: A network biology tool for biomolecular characterization and prioritization.
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DOI：
10.3389/fdata.2022.1016606
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发表时间：
2022
影响因子：
3.1
通讯作者：
Chen, Jake Y.
中科院分区：
文献类型：
--
作者：
Thanh Nguyen;Yue, Zongliang;Slominski, Radomir;Welner, Robert;Zhang, Jianyi;Chen, Jake Y.
关键词：
在网络生物学中，分子功能可以通过基于网络的推理或“关联罪”来表征。类似PageRank的工具已应用于生物分子相互作用网络的研究中，以进一步获得网络中所有分子的相对重要性。然而，在可广泛获取的基因与基因关联或蛋白质与蛋白质相互作用的数据集中存在大量固有噪音。如何开发强大的测试来扩展、过滤和排序特定疾病网络中的分子实体仍然是一个临时的数据分析过程。我们描述了一种新的生物分子表征和优先级工具，称为加权网络内节点扩展和排名（WINNER）。它接受任何分子相互作用网络数据的输入，并生成一个可选的扩展网络，其中所有节点根据它们在网络中彼此的相关性进行排名。为了帮助用户评估结果的稳健性，WINNER 提供了两种不同类型的统计数据。第一类是节点扩展 p 值，有助于评估将“非种子”分子添加到由“种子”分子和分子相互作用组成的原始生物分子相互作用网络中的统计显着性。第二种类型是节点排名 p 值，有助于评估每个节点对整个网络架构的贡献的相对统计显着性。我们在几个网络排列实验中通过尖峰噪声验证了 WINNER 在排名顶级分子方面的稳健性。我们发现基因网络的节点度保留随机化产生了正态分布的排名分数，其性能优于其他基因网络随机化技术所获得的分数。此外，我们还验证了与 PageRank 等现有方法相比，获胜者排名基因中与疾病生物学相关的比例更大。我们通过一些案例研究证明了 WINNER 的性能，包括阿尔茨海默病、乳腺癌、心肌梗塞和三阴性乳腺癌 (TNBC)。在所有这些案例研究中，WINNER 识别的扩展和排名靠前的基因比其他基因优先软件工具（包括 Ingenuity Pathway Analysis (IPA) 和 DiAMOND）识别的基因更能显着地揭示疾病生物学。当网络覆盖足够的节点和边缘信息时，WINNER 排名与其他排名方法密切相关，表明网络质量较高。只要有可用的基因/蛋白质/代谢网络信息，WINNER 用户就可以使用这个新工具来稳健地评估高通量生物学实验产生的候选基因、蛋白质或代谢物列表。
In network biology, molecular functions can be characterized by network-based inference, or “guilt-by-associations.” PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process. We describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding “non-seed” molecules to the original biomolecular interaction network consisting of “seed” molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree–preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND. WINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
14.9
作者：
Chatr-Aryamontri A;Breitkreutz BJ;Heinicke S;Boucher L;Winter A;Stark C;Nixon J;Ramage L;Kolas N;O'Donnell L;Reguly T;Breitkreutz A;Sellam A;Chen D;Chang C;Rust J;Livstone M;Oughtred R;Dolinski K;Tyers M
通讯作者：
Tyers M
影响因子：
4.3
作者：
Bromberg Y
通讯作者：
Bromberg Y
影响因子：
14.9
作者：
Chen J;Bardes EE;Aronow BJ;Jegga AG
通讯作者：
Jegga AG
影响因子：
25
作者：
D'Amelio, Marcello;Cavallucci, Virve;Cecconi, Francesco
通讯作者：
Cecconi, Francesco