Department of College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Sheng 150081, China.
Department of Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang Sheng 150001, China.
Bioinformatics. 2018 Jun 1;34(11):1953-1956. doi: 10.1093/bioinformatics/bty002.
SUMMARY: DincRNA aims to provide a comprehensive web-based bioinformatics toolkit to elucidate the entangled relationships among diseases and non-coding RNAs (ncRNAs) from the perspective of disease similarity. The quantitative way to illustrate relationships of pair-wise diseases always depends on their molecular mechanisms, and structures of the directed acyclic graph of Disease Ontology (DO). Corresponding methods for calculating similarity of pair-wise diseases involve Resnik's, Lin's, Wang's, PSB and SemFunSim methods. Recently, disease similarity was validated suitable for calculating functional similarities of ncRNAs and prioritizing ncRNA-disease pairs, and it has been widely applied for predicting the ncRNA function due to the limited biological knowledge from wet lab experiments of these RNAs. For this purpose, a large number of algorithms and priori knowledge need to be integrated. e.g. 'pair-wise best, pairs-average' (PBPA) and 'pair-wise all, pairs-maximum' (PAPM) methods for calculating functional similarities of ncRNAs, and random walk with restart (RWR) method for prioritizing ncRNA-disease pairs. To facilitate the exploration of disease associations and ncRNA function, DincRNA implemented all of the above eight algorithms based on DO and disease-related genes. Currently, it provides the function to query disease similarity scores, miRNA and lncRNA functional similarity scores, and the prioritization scores of lncRNA-disease and miRNA-disease pairs. AVAILABILITY AND IMPLEMENTATION: http://bio-annotation.cn:18080/DincRNAClient/. CONTACT: biofomeng@hotmail.com or qhjiang@hit.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
摘要：DincRNA 旨在提供一个全面的基于网络的生物信息学工具包，从疾病相似性的角度阐明疾病与非编码 RNA（ncRNA）之间复杂的关系。阐明两两疾病之间关系的定量方法始终取决于它们的分子机制和疾病本体论（DO）有向无环图的结构。计算两两疾病相似性的对应方法包括 Resnik 方法、Lin 方法、Wang 方法、PSB 方法和 SemFunSim 方法。最近，疾病相似性已被验证适合计算 ncRNA 的功能相似性和优先考虑 ncRNA-疾病对，由于这些 RNA 的湿实验中生物学知识有限，它已被广泛应用于预测 ncRNA 的功能。为此，需要整合大量的算法和先验知识。例如，用于计算 ncRNA 功能相似性的 '两两最佳，对平均'（PBPA）和 '两两全部，对最大'（PAPM）方法，以及用于优先考虑 ncRNA-疾病对的随机游走重启（RWR）方法。为了方便探索疾病关联和 ncRNA 功能，DincRNA 基于 DO 和与疾病相关的基因实现了上述所有 8 种算法。目前，它提供了查询疾病相似性评分、miRNA 和 lncRNA 功能相似性评分以及 lncRNA-疾病和 miRNA-疾病对优先排序评分的功能。
可用性和实现：http://bio-annotation.cn:18080/DincRNAClient/。
联系人：biofomeng@hotmail.com 或 qhjiang@hit.edu.cn。
补充信息：补充数据可在 Bioinformatics 在线获得。
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