Computational prediction and analysis of long non-coding RNAs

长非编码RNA的计算预测和分析

• 批准号: BB/J01589X/1
• 负责人: Anton Enright
• 金额: $ 45.59万
• 依托单位: European Bioinformatics Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FJ01589X%2F1
• 关键词: Computational; prediction; analysis; long; non

The sequencing of the Human genome has created a new era in biological research. Understanding our genome and how it is regulated is one of the great challenges for science, yet has the potential to help improve lives and our ability to treat diseases. The advent of this genomic age has heralded rapid changes in the field of biology. One surprise from the initial sequencing of the genome was the relative scarcity of genomic regions which can be read to produce proteins via RNA intermediates. Proteins are the building blocks of cells and many important molecular machines are composed of proteins. The non protein-coding part of the genome was previously dismissed in some circles as largely containing 'junk dna'. In the last ten years a number of breakthroughs in genome analysis and genome sequencing have shed-light on many hitherto unknown aspects of biology being carried out by these non protein coding regions.Novel technologies such as genome tiling arrays and high-throughput RNA sequencing has shown that although large portions of the genome may not be coding for protein sequences, they are still being read as RNA messages. The discovery of small RNA molecules such as small-interfering and microRNAs illustrated that many of these non-coding messages were being processed within cells and used to regulate other genes (both protein coding and non-coding). Within testes and oocytes (germline) another class of small RNAs called piwi-RNAs was discovered and shown to have an important role in protecting the genome as it passes from one generation to the next. Recently, attention is focusing on larger non-coding transcripts called long non-coding RNAs (lncRNAs). We know that the genome encodes many long RNA molecules which do not appear to encode proteins. A central dogma of biology has always been that DNA is read into RNA messages which subsequently encode proteins. This elegant view of molecular biology is still largely true, but the last ten years of research have revealed many hidden layers to this view of gene-regulation at the level of both DNA and RNA. Discovering how different classes of molecules work together is vital to our understanding of how our genome is regulated, how cells and organisms function and has tremendous implications for our understanding of development and disease.In this proposal we aim to build a computational system that will be able to detect candidate lncRNAs from RNA sequence data obtained from experimental samples. We aim to collect, score and characterise these molecules and to present them in a web-interface for further analysis. We will use computational biology to attempt to find cases where these molecules may interact with each other, protein-coding genes or the genome itself to control gene-regulation. Using computers allows us to work with a large quantity of data quickly and efficiently, however experiments are required in a laboratory to confirm and expand these results. We will work with a Mouse laboratory and a fruitfly laboratory (Drosophila melanogaster) to confirm our findings and to test the importance of these molecules by knocking them out. We will study what happens to these molecules as the embryo develops and as red-blood cells develop to see how their spatial and temporal expression is regulated. We will also attempt to discover what other molecules (such as proteins) may be binding to them.We believe that this project has the potential to greatly increase our understanding of these elusive molecules, the organisation of the genome and to assist ourselves and others in elucidating their roles in biology, health and disease.

人类基因组测序开创了生物学研究的新时代。了解我们的基因组及其调控方式是科学面临的巨大挑战之一，但也有可能帮助改善生活，提高我们治疗疾病的能力。这个基因组时代的到来预示着生物学领域的快速变化。基因组最初测序的一个惊喜是，可以通过RNA中间体读取产生蛋白质的基因组区域相对稀缺。蛋白质是细胞的基石，许多重要的分子机器都是由蛋白质组成的。基因组的非蛋白质编码部分此前被某些圈子认为主要含有“垃圾dna”而不予考虑。在过去的十年中，基因组分析和基因组测序的一些突破已经揭示了许多迄今为止未知的生物学方面是由这些非蛋白质编码区进行的。基因组平铺阵列和高通量RNA测序等新技术表明，尽管基因组的大部分可能不编码蛋白质序列，但它们仍然被解读为RNA信息。小RNA分子（如小干扰RNA和微RNA）的发现表明，许多这些非编码信息在细胞内被处理，并用于调节其他基因（包括蛋白质编码和非编码）。在睾丸和卵母细胞（种系）中发现了另一类被称为piwi- rna的小rna，并证明它们在保护基因组代代相传的过程中起着重要作用。近年来，人们将注意力集中在被称为长链非编码rna （long non-coding RNAs, lncRNAs）的大型非编码转录物上。我们知道，基因组编码许多长RNA分子，而这些分子似乎并不编码蛋白质。生物学的一个中心法则一直是DNA被解读为RNA信息，RNA信息随后编码蛋白质。这种优雅的分子生物学观点在很大程度上仍然是正确的，但最近十年的研究揭示了这种基因调控观点在DNA和RNA水平上的许多隐藏层面。发现不同种类的分子是如何协同工作的，这对于我们理解基因组是如何被调控的、细胞和生物体是如何运作的至关重要，对我们理解发育和疾病也有着巨大的意义。在本提案中，我们的目标是建立一个计算系统，能够从实验样品中获得的RNA序列数据中检测候选lncrna。我们的目标是收集、评分和描述这些分子，并将它们呈现在一个网络界面上，以供进一步分析。我们将使用计算生物学来试图找到这些分子可能相互作用的情况，蛋白质编码基因或基因组本身来控制基因调控。使用计算机使我们能够快速有效地处理大量数据，但是需要在实验室中进行实验来证实和扩展这些结果。我们将与小鼠实验室和果蝇实验室（Drosophila melanogaster）合作，以确认我们的发现，并通过敲除这些分子来测试它们的重要性。我们将研究这些分子在胚胎发育和红细胞发育过程中发生的变化，以了解它们的空间和时间表达是如何受到调节的。我们还将试图发现可能与它们结合的其他分子（如蛋白质）。我们相信这个项目有潜力极大地增加我们对这些难以捉摸的分子的理解，基因组的组织，并帮助我们自己和其他人阐明它们在生物学、健康和疾病中的作用。

项目成果

Non-coding RNA Expression, Function, and Variation during Drosophila Embryogenesis.

• DOI: 10.1016/j.cub.2018.09.026
• 发表时间: 2018-11-19
• 期刊: Current biology : CB
• 作者: Schor IE;Bussotti G;Maleš M;Forneris M;Viales RR;Enright AJ;Furlong EEM
• 通讯作者: Furlong EEM

spongeScan: A web for detecting microRNA binding elements in lncRNA sequences.

• DOI: 10.1093/nar/gkw443
• 发表时间: 2016-07-08
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Furió-Tarí P;Tarazona S;Gabaldón T;Enright AJ;Conesa A
• 通讯作者: Conesa A

Improved definition of the mouse transcriptome via targeted RNA sequencing.

通过靶向的RNA测序改善了小鼠转录组的定义。

• DOI: 10.1101/gr.199760.115
• 发表时间: 2016-05
• 期刊: Genome research
• 影响因子: 7
• 作者: Bussotti G;Leonardi T;Clark MB;Mercer TR;Crawford J;Malquori L;Notredame C;Dinger ME;Mattick JS;Enright AJ
• 通讯作者: Enright AJ

Large-scale analysis of microRNA expression, epi-transcriptomic features and biogenesis.

• DOI: 10.1093/nar/gkw1031
• 发表时间: 2017-02-17
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Vitsios DM;Davis MP;van Dongen S;Enright AJ
• 通讯作者: Enright AJ

Transposon-driven transcription is a conserved feature of vertebrate spermatogenesis and transcript evolution.

• DOI: 10.15252/embr.201744059
• 发表时间: 2017-07
• 期刊: EMBO reports
• 影响因子: 7.7
• 作者: Davis MP;Carrieri C;Saini HK;van Dongen S;Leonardi T;Bussotti G;Monahan JM;Auchynnikava T;Bitetti A;Rappsilber J;Allshire RC;Shkumatava A;O'Carroll D;Enright AJ
• 通讯作者: Enright AJ