Motif-based algorithms for detection of functional classes of long non-coding RNAs

非编码RNA（ncRNA），不编码蛋白质的转录物，在过去十年中开始受到很多关注，这要归功于几项高通量全基因组测序工作，这些工作表明，尽管不到2%的基因组编码蛋白质，但至少75%的基因组被积极转录为非编码RNA。NcRNA已经成为贯穿发育、分化和疾病的若干生物过程中的重要关键参与者，包括转录和转录后基因调控机制。它们的大小变化很大，从20到数十万个核苷酸的小RNA。在这个建议中感兴趣的是长ncRNA，转录长于200个核苷酸，已被估计为约20000在基因组中，并表现出不同的时空表达模式。尽管它们丰富，但只有少数具有特征性功能：通过高通量方法检测到的大多数lncRNA仍然没有功能分类。lncRNA序列进化非常迅速，因此公认的是，与考虑二级RNA结构的方法相比，仅关注序列保守性的分析方法不太适合，允许非常规基序，如假结、G-四链体和分子内RNA三链体，其已显示与lncRNA的功能方面相关。虽然用于阐明序列结构保守性的全局方法是有价值的，但是假设对于lncRNA，必须求助于局部保守的序列结构基序是相关的。在这个建议中，我们将解决和研究各种计算方面的重要搜索和聚类lncRNA共享本地序列结构基序。这种共有的基序可能表明lncRNA在功能上是相关的（例如，通过序列结构基序结合相同的RNA结合蛋白），因此将使我们能够对lncRNA进行第一次全面的功能分类。我们有两个主要目标，主要归因于这两个PI分别。1）我们想设计计算方法比对（pseudoknotted）RNA家族，然后从比对中推导出概率性的全局和局部基序，同时允许结合实验证据，最后开发出在大型基因组序列中快速灵敏地搜索基序的方法。2)我们希望扩展和应用的方法，根据第一个目标检索lncRNA的序列结构基序。我们希望能够根据序列结构基序和其他基因组特征将lncRNA分类为不同的类别，并最终发现和功能注释新的非编码RNA类别。

Non-coding RNAs (ncRNAs), transcripts that do not code for proteins, started receiving a lot of attention in the last decade, thanks to several high-throughput genome-wide sequencing efforts which showed that, whereas less than 2% of the genome encodes proteins, at least 75% is actively transcribed into non-coding RNAs. NcRNAs have emerged as important key players in several biological processes throughout development, differentiation and diseases, including mechanisms of transcriptional and post-transcriptional gene regulation. Their size varies extremely from small RNAs of size 20 to hundred thousands of nucleotides. Of interest in this proposal are long ncRNAs, transcripts longer than 200 nucleotides, which have been estimated to be about 20000 in the genome and exhibiting distinct patterns of spatio-temporal expression. Despite their abundance, only few of them have a characterized function: most of the lncRNAs detected by high-throughput methods remain without a functional classification. LncRNA sequences evolve very rapidly, hence it is accepted that analysis methods focusing solely on sequence conservation are less suited compared to methods that take the secondary RNA structure into account, allowing for non-conventional motifs, such as pseudoknots, G-quadruplexes and intramolecular RNA triplexes, which have been shown to be related to functional aspects of lncRNAs. While global approaches for elucidating sequence-structure conservation are valuable, it is relevant to assume that for lncRNAs one has to resort to locally conserved sequence-structure motifs. In this proposal we will address and research various computational aspects important for searching and clustering lncRNAs which share local sequence-structure motifs. Such shared motifs might indicate that the lncRNAs are functionally related (e.g. bind the same RNA Binding Protein via the sequence-structure motif) and therefore will enable us a first comprehensive functional classification of lncRNAs. We have two main goals attributed mainly to the two PIs respectively.1) We want to devise computational methods for aligning (pseudoknotted) RNA families, then derive probabilistic global and local motifs from the alignments while allowing to incorporate experimental evidence, and finally develop methods to search the motifs fast and sensitive in large genomic sequences. 2) We want to extend and apply the methodology derived under the first goal to retrieve sequence structure motifs in lncRNAs. We want to enable classification of lncRNAs into different classes based on sequence structure motifs and other genomic features, and finally find and functionally annotate new classes of non-coding RNAs.