Development and Maintenance of RepeatMasker and RepeatModeler

项目摘要 哺乳动物和大多数其他真核生物的基因组包含大量的散布重复序列(IR)，大多数 它们是处于不同衰变水平的转座元件(TES)的副本。他们的出现使许多事情复杂化 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 并发症。除了它们的普遍性之外，在过去的几十年里，研究界已经成为 对它们对基因组活动和进化的巨大影响广为人知。 每个物种都暴露在一套独特的、复杂的TE中，留下了可识别的副本 从3亿年前到现在。这些TES被发现并由De重建 Novo发现方法，通常通过我们的RepeatModeler工具，而它们的副本然后由我们的 RepeatMasker软件。从头开始的方法可以以合理的速度创建TE库，但产品远远不够 从手工精选所能达到的所需品质。随着最近序列化的爆炸性增长 物种，这些最后的步骤，也许永远不会完全自动化，现在形成了基因组的严重瓶颈 分析由于缺乏人力和专业知识，而结果，特别是在产生不同结果时 研究方法来自不同的研究小组，缺乏一致性和冗余性。此外， 已经为其创建了高质量文库的基因组注释跟不上文库 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泛化和改进TE对齐来重构RepeatMasker的计划 裁决，改用以家庭为中心的搜索策略，支持增量重新分析，改进 批注报告和支持群集环境。响应对改进方法的需求 自动生成TE库我们建议对RepeatModeler的核心发现进行重大更改 算法，开发了一种新的模型扩展工具。此外，我们将把我们的新方法扩展到 利用多物种排列和祖先重建，并利用它们建立一个全面的 哺乳动物TE文库。

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.