Rfam: The community resource for RNA families

Rfam：RNA 家族的社区资源

• 批准号: BB/S020462/1
• 负责人: Alex Bateman
• 金额: $ 64.88万
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FS020462%2F1
• 关键词: Rfam; community; resource; RNA; families

DNA encodes the genetic information that is transferred from parents to their offspring. When required, DNA is first transcribed into RNA, which is then translated into proteins that do useful work inside the cells. But many RNAs do much more than merely act as messengers between genes and proteins. These non-coding RNAs (ncRNAs; because they do not "code" for proteins) can be found in all living things, many of which are essential for survival. There are many types of ncRNAs, for example ncRNA is at the heart of a ribosome, the molecular machine that synthesises all proteins in our bodies.Importantly, when scientists encounter an RNA sequence, they need a reliable tool to identify this RNA and its function. Moreover, it is necessary to find the constituent RNA parts whenever a new genome is sequenced. The Rfam database was thus created, which is an online resource that groups together related ncRNAs into families, each represented by a statistical model that allows the detection of other members of the same family. Since its inception in 2002, Rfam has expanded from ~100 families to nearly 3,000 families today, reflecting the growth of the ncRNA field. Rfam has been used world over in thousands of studies spanning many biology disciplines, e.g. Rfam was used to find ncRNAs in important crops like rice and sugar beet when their genomes were first sequenced. However, it is important to keep Rfam up-to-date because new RNAs are being constantly discovered and additional information is gleaned about already known ncRNAs. We will collaborate with the RNA community to accomplish the following objectives:(1) We will focus on updating some of the most important RNA families for which at least one 3D structure has been found. The 3D structure can show us which parts of a long RNA sequence are close to each other in 3D space. With this knowledge, we can predict how the sequence may change, yet forming the same 3D shape. While Rfam has some of this information, it is not as accurate as what is known from 3D structures. By integrating 3D data into Rfam, scientists will be able to write new computer programs that can predict RNA 3D structure from sequence. (2) We will create a complete collection of ncRNA type called microRNAs, which are short RNA sequences that control the amounts of different proteins in the body. Since problems with microRNAs are linked to cancer, it is important to be able to discover these in genomes and identify which ones are related. We will collaborate with the miRBase developers at the University of Manchester to synchonise microRNA families contained within the two databases. Although miRBase is complete, it does not have the tools to maintain the families while the opposite holds true for Rfam. By working together, we will create a single, complete collection of microRNA families so as to facilitate the discovery of microRNAs in new genomes using Rfam.(3) We will create more families based on RNAs found in viruses. Many viruses use RNA structures to infect, reproduce, or avoid the host immune response. Rfam has a small number of viral families, mostly dating from a decade ago. We will update them by working with the virologists from the European Viral Bioinformatics Center who have compiled a set of conserved viral RNA structures. Scientists will then be able to use Rfam to detect viruses in sequences and study their RNA structures.We will also regularly update the Rfam website, respond to user queries, and attend conferences to meet colleagues and share resource developments. Collectively, this work will further enhance the functionality and utility of a powerful resource and cement Rfam's central status in the field of RNA research worldwide.

DNA编码从父母转移到其后代的遗传信息。在需要时，首先将DNA转录为RNA，然后将其翻译成细胞内部有用的蛋白质。但是，许多RNA的作用远不止是基因和蛋白质之间的使者。这些非编码RNA（NCRNA；因为它们不是“蛋白质代码”）可以在所有生物中找到，其中许多对于生存至关重要。 NCRNA有多种类型，例如NCRNA是核糖体的核心，核糖体是分子机，可以合成我们体内所有蛋白质。此外，每当对新基因组进行测序时，都必须找到构成RNA零件。因此，创建了RFAM数据库，这是一种在线资源，将将NCRNA相关的ncrnas组合在一起，每个资源都以统计模型为代表，该模型允许检测同一家族的其他成员。自2002年成立以来，RFAM已从约100个家庭扩展到了近3,000个家庭，这反映了NCRNA领域的增长。 RFAM已在跨越许多生物学学科的数千项研究中被用于全世界。首先对其基因组进行了测序时，使用RFAM在大米和甜菜等重要农作物中找到NCRNA。但是，重要的是要保持RFAM的最新状态，因为不断发现新的RNA，并且有关已知的NCRNA的其他信息。我们将与RNA社区合作实现以下目标：（1）我们将专注于更新一些至少一个3D结构的最重要的RNA系列。 3D结构可以向我们展示长RNA序列的哪些部分在3D空间中彼此接近。有了这些知识，我们可以预测序列如何改变，但形成相同的3D形状。尽管RFAM具有其中的一些信息，但它并不像3D结构所知的那样准确。通过将3D数据集成到RFAM中，科学家将能够编写可以从序列预测RNA 3D结构的新计算机程序。 （2）我们将创建一个称为MicroRNA的NCRNA类型的完整集合，该类型是控制体内不同蛋白质量的短RNA序列。由于microRNA的问题与癌症有关，因此能够在基因组中发现这些问题并确定哪些相关的问题很重要。我们将与曼彻斯特大学的Mirbase开发人员合作，同步两个数据库中包含的MicroRNA家庭。尽管Mirbase已完成，但它没有维护家庭的工具，而RFAM则相反。通过共同努力，我们将创建一个完整的microRNA家族集合，以促进使用RFAM在新基因组中发现microRNA。（3）我们将根据病毒中发现的RNA创建更多的家庭。许多病毒使用RNA结构感染，繁殖或避免宿主免疫反应。 RFAM有少数病毒式家庭，主要是十年前的历史。我们将通过与欧洲病毒生物信息学中心的病毒学家合作来对其进行更新，后者已经编译了一组保守的病毒RNA结构。然后，科学家将能够使用RFAM在序列中检测病毒并研究其RNA结构。我们还将定期更新RFAM网站，响应用户查询，并参加会议以结识同事并分享资源发展。总的来说，这项工作将进一步增强强大资源和水泥RFAM在全球RNA研究领域的中心地位的功能和实用性。

项目成果

Rfam 14: expanded coverage of metagenomic, viral and microRNA families.

• DOI: 10.1093/nar/gkaa1047
• 发表时间: 2021-01-08
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Kalvari I;Nawrocki EP;Ontiveros-Palacios N;Argasinska J;Lamkiewicz K;Marz M;Griffiths-Jones S;Toffano-Nioche C;Gautheret D;Weinberg Z;Rivas E;Eddy SR;Finn RD;Bateman A;Petrov AI
• 通讯作者: Petrov AI

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

• DOI: 10.1093/bib/bbaa232
• 发表时间: 2021-03-22
• 期刊: Briefings in bioinformatics
• 影响因子: 9.5
• 作者: Hufsky F;Lamkiewicz K;Almeida A;Aouacheria A;Arighi C;Bateman A;Baumbach J;Beerenwinkel N;Brandt C;Cacciabue M;Chuguransky S;Drechsel O;Finn RD;Fritz A;Fuchs S;Hattab G;Hauschild AC;Heider D;Hoffmann M;Hölzer M;Hoops S;Kaderali L;Kalvari I;von Kleist M;Kmiecinski R;Kühnert D;Lasso G;Libin P;List M;Löchel HF;Martin MJ;Martin R;Matschinske J;McHardy AC;Mendes P;Mistry J;Navratil V;Nawrocki EP;O'Toole ÁN;Ontiveros-Palacios N;Petrov AI;Rangel-Pineros G;Redaschi N;Reimering S;Reinert K;Reyes A;Richardson L;Robertson DL;Sadegh S;Singer JB;Theys K;Upton C;Welzel M;Williams L;Marz M
• 通讯作者: Marz M