MicroRNA prediction with a novel ranking algorithm based on random walks.
MicroRNA prediction with a novel ranking algorithm based on random walks.
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DOI：
10.1093/bioinformatics/btn175
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发表时间：
2008-07-01
期刊：
影响因子：
--
通讯作者：
Zhang W
中科院分区：
文献类型：
--
作者：
Xu Y;Zhou X;Zhang W
MicroRNA （mirna）在动植物转录后基因调控中发挥着重要作用。已经开发了几种现有的计算方法来补充实验方法，以发现在特定环境条件或细胞类型中限制性表达的mirna。这些计算方法需要足够数量的表征mirna作为训练样本，并依赖基因组注释来减少预测的假定mirna的数量。然而，大多数测序基因组都没有得到很好的注释，其中许多基因组只有很少的实验表征的mirna。因此，现有的方法对这些基因组中的mirna的鉴定是不有效的，甚至是不可行的。为了从已知miRNA较少和/或注释较少的基因组中识别miRNA，我们提出并开发了一种新的miRNA预测方法，miRank，基于我们新的基于随机行走的排序算法。我们首先在智人基因组上测试了我们的方法；使用极少数已知的人类mirna作为样本，我们的方法实现了大于95%的预测精度。然后，我们应用我们的方法预测了冈比亚按蚊（Anopheles gambiae）的200个mirna，冈比亚按蚊是非洲最重要的疟疾媒介。我们进一步的研究表明，在200个假定的miRNA前体中，有78个编码成熟的miRNA，这些miRNA在至少一种其他动物物种中是保守的。这些保守的推测mirna是进一步实验研究了解疟疾感染的良好候选者。可用性：MiRank是在Windows平台上用Matlab编程的。源代码可根据要求提供。联系:zhang@cse.wustl.edu
MicroRNA (miRNAs) play essential roles in post-transcriptional gene regulation in animals and plants. Several existing computational approaches have been developed to complement experimental methods in discovery of miRNAs that express restrictively in specific environmental conditions or cell types. These computational methods require a sufficient number of characterized miRNAs as training samples, and rely on genome annotation to reduce the number of predicted putative miRNAs. However, most sequenced genomes have not been well annotated and many of them have a very few experimentally characterized miRNAs. As a result, the existing methods are not effective or even feasible for identifying miRNAs in these genomes. Aiming at identifying miRNAs from genomes with a few known miRNA and/or little annotation, we propose and develop a novel miRNA prediction method, miRank, based on our new random walks- based ranking algorithm. We first tested our method on Homo sapiens genome; using a very few known human miRNAs as samples, our method achieved a prediction accuracy greater than 95%. We then applied our method to predict 200 miRNAs in Anopheles gambiae, which is the most important vector of malaria in Africa. Our further study showed that 78 out of the 200 putative miRNA precursors encode mature miRNAs that are conserved in at least one other animal species. These conserved putative miRNAs are good candidates for further experimental study to understand malaria infection. Availability: MiRank is programmed in Matlab on Windows platform. The source code is available upon request. Contact: zhang@cse.wustl.edu
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