The Sainsbury Laboratory, John Innes Centre, Colney Lane, Norwich NR47UH, UK.
BMC Bioinformatics. 2010 Feb 18;11:93. doi: 10.1186/1471-2105-11-93.
Next-generation sequencing technologies allow researchers to obtain millions of sequence reads in a single experiment. One important use of the technology is the sequencing of small non-coding regulatory RNAs and the identification of the genomic locales from which they originate. Currently, there is a paucity of methods for finding small RNA generative locales.
We describe and implement an algorithm that can determine small RNA generative locales from high-throughput sequencing data. The algorithm creates a network, or graph, of the small RNAs by creating links between them depending on their proximity on the target genome. For each of the sub-networks in the resulting graph the clustering coefficient, a measure of the interconnectedness of the subnetwork, is used to identify the generative locales. We test the algorithm over a wide range of parameters using RFAM sequences as positive controls and demonstrate that the algorithm has good sensitivity and specificity in a range of Arabidopsis and mouse small RNA sequence sets and that the locales it generates are robust to differences in the choice of parameters.
NiBLS is a fast, reliable and sensitive method for determining small RNA locales in high-throughput sequence data that is generally applicable to all classes of small RNA.
下一代测序技术使研究人员能够在单次实验中获得数百万个序列读取。该技术的一个重要用途是对小的非编码调控 RNA 进行测序，并确定它们的基因组来源位置。目前，寻找小 RNA 生成位置的方法很少。
我们描述并实现了一种可以从高通量测序数据中确定小 RNA 生成位置的算法。该算法通过在目标基因组上根据它们的接近程度在它们之间创建链接，为小 RNA 创建一个网络或图。对于生成图中的每个子网络，使用聚类系数（子网的连通性的度量）来识别生成位置。我们使用 RFAM 序列作为正对照在广泛的参数范围内测试了该算法，并证明该算法在一系列拟南芥和小鼠小 RNA 序列集中具有良好的灵敏度和特异性，并且它生成的位置对参数选择的差异具有鲁棒性。
NiBLS 是一种快速、可靠和敏感的方法，用于确定高通量序列数据中小 RNA 的位置，它通常适用于所有类型的小 RNA。
