Inferring noncoding RNA families and classes by means of genome-scale structure-based clustering.
Inferring noncoding RNA families and classes by means of genome-scale structure-based clustering.
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DOI：
10.1371/journal.pcbi.0030065
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发表时间：
2007-04-13
影响因子：
4.3
通讯作者：
Backofen R
中科院分区：
文献类型：
--
作者：
Will S;Reiche K;Hofacker IL;Stadler PF;Backofen R
RFAM数据库通过足以建立同源性的序列相似性来定义ncRNA家族。在某些情况下，如microRNA和box H/ACA snoRNA，功能共性定义了以结构相似性为特征的RNA类别，并且通常由多个RNA家族组成。高通量转录组学和比较基因组学的最新进展产生了非常大的推定非编码RNA和调节RNA信号集。对于他们中的许多人，证据稳定的选择作用于他们的二级结构已被推导出来，并至少近似模型的结构已被计算。这些假设的RNA中的绝大多数不能被分配到已建立的家族或类别。我们在这里提出了一个基于结构的聚类方法，能够提取推定的RNA类结构化RNA的全基因组调查。LocARNA（RNA的局部比对）工具实现了Sankoff算法的一种新变体，该算法速度足够快，可以处理数千个候选序列。该方法对于假阳性预测也是鲁棒的，即，输入数据被非结构化或非保守序列污染。我们已经成功地测试了基于LocARNA的聚类方法的RFAM种子比对的序列。此外，我们已经将其应用于先前公开的一组在玻璃海鞘基因组中的3，332个预测的结构化元件（Missal K，Rose D，Stadler PF（2005）Noncoding RNAs in Ciona lactobacillus. Bioinformatics 21（Supplement 2）：i77-i78）。除了恢复，例如，tRNA作为一个基于结构的类别，该方法鉴定了几个RNA家族，包括microRNA和snoRNA候选者，并提出了几种新的ncRNA类别，迄今为止还没有实验表征。长期以来，人们认为生物体中的过程控制几乎只由蛋白质执行。直到最近，科学家们才了解到另一类分子，即特殊的RNA，在细胞控制中起着重要作用。因此，在过去几年中，对此类RNA的研究越来越受到关注。这些RNA被称为非编码RNA（ncRNA），因为与大多数其他RNA不同，这些分子不编码蛋白质。由于最近的研究成功，人们可以通过比较相关生物的基因组来预测许多潜在的新ncRNA。从技术上讲，比较这样的RNA是具有挑战性和计算成本高的，因为相关的ncRNA通常只显示出序列水平上的弱相似性，但具有相似的结构。在本文中，我们提出了新的方法LocARNA的快速和准确的比较RNA的序列和结构。使用这种方法，我们定义了基于序列和结构的ncRNA对之间的距离度量。然后将其用于将RNA组合成一个簇，以识别大型无组织RNA组中的相似RNA组。这种比较的最终目的是鉴定新的ncRNA类别。我们将我们的聚类程序应用于先前发表的C.染色体组。除了重新发现已知的RNA类型，例如，该方法预测了microRNA候选者，并提出了几种新的，实验上未表征的ncRNA类别。为了验证，我们对RFAM的大约4，000个RNA进行了聚类，RFAM是一个大型数据库，其中包含已知家族分类的RNA。我们的研究结果表明，所提出的基于结构的聚类方法具有良好的性能。
The RFAM database defines families of ncRNAs by means of sequence similarities that are sufficient to establish homology. In some cases, such as microRNAs and box H/ACA snoRNAs, functional commonalities define classes of RNAs that are characterized by structural similarities, and typically consist of multiple RNA families. Recent advances in high-throughput transcriptomics and comparative genomics have produced very large sets of putative noncoding RNAs and regulatory RNA signals. For many of them, evidence for stabilizing selection acting on their secondary structures has been derived, and at least approximate models of their structures have been computed. The overwhelming majority of these hypothetical RNAs cannot be assigned to established families or classes. We present here a structure-based clustering approach that is capable of extracting putative RNA classes from genome-wide surveys for structured RNAs. The LocARNA (local alignment of RNA) tool implements a novel variant of the Sankoff algorithm that is sufficiently fast to deal with several thousand candidate sequences. The method is also robust against false positive predictions, i.e., a contamination of the input data with unstructured or nonconserved sequences. We have successfully tested the LocARNA-based clustering approach on the sequences of the RFAM-seed alignments. Furthermore, we have applied it to a previously published set of 3,332 predicted structured elements in the Ciona intestinalis genome (Missal K, Rose D, Stadler PF (2005) Noncoding RNAs in Ciona intestinalis. Bioinformatics 21 (Supplement 2): i77–i78). In addition to recovering, e.g., tRNAs as a structure-based class, the method identifies several RNA families, including microRNA and snoRNA candidates, and suggests several novel classes of ncRNAs for which to date no representative has been experimentally characterized. For a long time, it was believed that the control of processes in living organisms is almost only performed by proteins. Only recently, scientists learned that a further class of molecules, namely special RNAs, plays an important role in cell control. In consequence, research on such RNAs enjoys increasing attention over the last few years. These RNAs were called noncoding RNAs (ncRNA), because, unlike most other RNAs, these molecules do not code for proteins. Due to recent research successes, one can predict a lot of potential new ncRNAs by comparing the genomes of related organisms. Technically, comparing such RNAs is challenging and computationally expensive, since related ncRNAs often show only weak similarity on the sequence level, but share similar structures. In the paper, we present the new method LocARNA for fast and accurate comparison of RNAs with respect to their sequence and structure. Using this method, we define a distance measure between pairs of ncRNAs based on sequence and structure. This is then used for combining RNAs into a cluster for identifying groups of similar RNAs in large unorganized sets of RNA. The final aim of such a comparison is to identify new classes of ncRNAs. We applied our clustering procedure to a previously published set of 3,332 predicted ncRNAs in the C. intestinalis genomes. In addition to rediscovering known classes of RNAs, e.g., tRNAs, the method predicts microRNA candidates, and suggests several novel, experimentally uncharacterized classes of ncRNAs. For verification, we clustered about 4,000 RNAs of RFAM, which is a large database that contains RNAs with an already known classification into families. Our results show good performance of the presented structure-based clustering approach.
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