Accurate and efficient gene function prediction using a multi-bacterial network
Accurate and efficient gene function prediction using a multi-bacterial network
复制标题
利用多细菌网络进行准确高效的基因功能预测
DOI：
10.1093/bioinformatics/btaa885
复制
发表时间：
2020
期刊：
影响因子：
5.8
通讯作者：
Murali, T M
中科院分区：
文献类型：
--
作者：
Law, Jeffrey N;Kale, Shiv D;Murali, T M
在测序的基因组中，近40%的基因没有实验或计算得出的功能注释。为了填补这一空白，我们寻求开发基于网络的基因功能预测方法，该方法可以将多个物种的异质数据与基于实验的功能注释相结合，并将其系统地转移到全基因组范围内的新测序生物中。然而，这样的网络的大尺寸的可扩展性提出了挑战，目前的methods.ResultsWe开发的标签传播算法称为FastSinkSource。通过形式上限制其进度，我们将运行时间减少了100倍，而不牺牲准确性。我们系统地评估了许多构建多物种细菌网络的方法，并将FastSinkSource和其他最先进的方法应用于这些网络。我们发现，最准确和有效的方法是预先计算注释分数的物种与实验注释，然后将它们转移到其他生物。以这种方式，FastSinkSource在3分钟内运行200种细菌。可用性和实施我们的框架的实施和本研究中使用的所有数据可在https://github.com/Murali-group/multi-species-GOA-prediction.Supplementary信息补充数据可在Bioinformatics在线。
MotivationNearly 40% of the genes in sequenced genomes have no experimentally or computationally derived functional annotations. To fill this gap, we seek to develop methods for network-based gene function prediction that can integrate heterogeneous data for multiple species with experimentally based functional annotations and systematically transfer them to newly sequenced organisms on a genome-wide scale. However, the large sizes of such networks pose a challenge for the scalability of current methods.ResultsWe develop a label propagation algorithm called FastSinkSource. By formally bounding its rate of progress, we decrease the running time by a factor of 100 without sacrificing accuracy. We systematically evaluate many approaches to construct multi-species bacterial networks and apply FastSinkSource and other state-of-the-art methods to these networks. We find that the most accurate and efficient approach is to pre-compute annotation scores for species with experimental annotations, and then to transfer them to other organisms. In this manner, FastSinkSource runs in under 3 min for 200 bacterial species.Availability and implementationAn implementation of our framework and all data used in this research are available at https://github.com/Murali-group/multi-species-GOA-prediction.Supplementary informationSupplementary data are available atBioinformaticsonline.