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

非编码rna （ncRNAs）是一种不编码蛋白质的转录本，在过去十年中开始受到广泛关注，这要归功于几项高通量全基因组测序的努力，这些测序表明，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成非编码rna。ncrna在整个发育、分化和疾病的几个生物学过程中，包括转录和转录后基因调控机制，已成为重要的关键角色。它们的大小变化很大，从20个大小的小rna到数十万个核苷酸。这一提议感兴趣的是长ncrna，转录物长度超过200个核苷酸，估计在基因组中约有20000个，并表现出不同的时空表达模式。尽管它们数量丰富，但只有少数具有特征功能：高通量方法检测到的大多数lncrna仍然没有功能分类。LncRNA序列的进化非常迅速，因此人们普遍认为，与考虑二级RNA结构的方法相比，仅关注序列保守的分析方法不太合适，考虑到非常规的基序，如假结、g -四重结构和分子内RNA三重结构，这些基序已被证明与LncRNA的功能方面有关。虽然阐明序列结构保守的全局方法是有价值的，但假设lncrna必须求助于局部保守的序列结构基序是相关的。在本提案中，我们将讨论和研究对于搜索和聚类共享局部序列结构基序的lncrna很重要的各种计算方面。这种共享的基序可能表明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），因此将使我们能够首次对lncrna进行全面的功能分类。我们有两个主要目标，分别归于这两个pi。1)我们希望设计一种计算方法来对准（假结）RNA家族，然后在允许合并实验证据的情况下，从比对中获得概率全局和局部基序，最后开发一种方法来快速灵敏地搜索大基因组序列中的基序。2)我们希望扩展和应用第一个目标下导出的方法来检索lncrna中的序列结构基序。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.