Development and Maintenance of RepeatMasker and RepeatModeler

RepeatMasker和RepeatModeler的开发和维护

基本信息

批准号：
10563214
负责人：
Robert MacDonald Hubley
金额：
$ 58.82万
依托单位：
INSTITUTE FOR SYSTEMS BIOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-04 至 2027-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10563214
关键词：
Age Algorithms Biology Calibration Classification Code Communities Complex Computer software Consensus Sequence DNA Insertion Elements DNA Transposable Elements DNA Transposons Data Set Databases Detection Development Distant Elements Environment Event Evolution Exposure to Family Funding Agency Generations Genome Growth Hand Human Human Resources Knowledge Libraries Light Maintenance Mammals Methods Modeling Mutation Organism Phylogenetic Analysis Phylogeny Process Reporting Research Resolution Selfish DNA Sensitivity and Specificity Sequence Alignment Sequence Analysis Shapes Source Speed Tandem Repeat Sequences Trees Update Vertebrates Work adjudication cluster computing conflict resolution genome analysis genome annotation improved mammalian genome markov model novel programs reconstruction search engine tool trustworthiness usability vertebrate genome

项目摘要

Project Summary Mammalian and most other eukaryotic genomes contain a large number of interspersed repeats (IRs), most of which are copies of transposable elements (TEs) at varying levels of decay. Their presence complicates many genome sequence analyses, but their accurate identification in an early analysis stage can reduce these complications. In addition to their pervasiveness, over the last decades the research community has become widely familiar with their enormous impact on genome activity and evolution. Every species has been exposed to a unique, complex set of TEs leaving recognizable copies from as long ago as 300 million years to as recently as the present day. These TEs are uncovered and reconstructed by de novo discovery methods, often by our RepeatModeler tool, while their copies are then annotated by our RepeatMasker software. De novo methods can create TE libraries at a reasonable pace, but the product is far from the desired quality that can be reached by hand curation. With the recent explosive growth in sequenced species, these finishing steps, perhaps never fully automatable, now form a severe bottleneck in genome analyses due to a lack of manpower and expertise, while the results, especially when produced by different methods from different research groups, lack consistency and suffer from redundancy. Furthermore, the annotation of genomes for which high-quality libraries have been created is not keeping up with library improvements due to the computational burden of re-analysis. In this proposal, we describe a plan to refactor RepeatMasker by generalizing and improving TE alignment adjudication, switching to a family-centric search strategy with support for incremental re-analysis, improving annotation reporting and supporting cluster environments. Responding to the need for improved methods for automated TE library generation we propose making significant changes to RepeatModeler’s core discovery algorithms, develop a novel model extension tool, and. In addition, we will extend our novel methods for exploiting multi-species alignments and ancestral reconstructions and utilize them to build a comprehensive mammalian TE library.

项目概要哺乳动物和大多数其他真核生物基因组包含大量散在重复序列（IR），大多数它们是不同衰变程度的转座因子（TE）的副本，它们的存在使许多事情变得复杂。基因组序列分析，但在早期分析阶段准确识别可以减少这些除了其普遍性之外，在过去的几十年里，研究界也变得越来越复杂。广泛熟悉它们对基因组活动和进化的巨大影响。每个物种都接触过一组独特、复杂的 TE，从很长一段时间以来都留下了可识别的副本 de 发现并重建了从 3 亿年前到今天的 TE。 novo 发现方法，通常是通过我们的 RepeatModeler 工具，而它们的副本然后由我们的注释 RepeatMasker 软件。 De novo 方法可以以合理的速度创建 TE 库，但产品还很遥远。随着最近测序技术的爆炸性增长，可以通过手工管理达到所需的质量。对于物种来说，这些整理步骤也许永远无法完全自动化，现在形成了基因组的严重瓶颈由于缺乏人力和专业知识而进行分析，而结果，特别是当由不同的人产生时不同研究小组的方法缺乏一致性并且存在冗余。已创建高质量文库的基因组注释未能跟上文库的步伐由于重新分析的计算负担而有所改进。在这个提案中，我们描述了一个通过推广和改进 TE 对齐来重构 RepeatMasker 的计划裁决，转向以家庭为中心的搜索策略，支持增量重新分析，改进注释报告和支持集群环境的需求。自动化 TE 库生成我们建议对 RepeatModeler 的核心发现进行重大更改算法，开发一种新颖的模型扩展工具，此外，我们还将扩展我们的新颖方法。利用多物种排列和祖先重建，并利用它们建立全面的哺乳动物 TE 库。