Research Project
9905539
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 recent as 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 coming 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 alleviate the problems of finishing new repeat libraries: we aim to exploit the power of multi-species genome alignments, especially in revealing lineage-specific TEs, develop a web-based workbench based on our TE library finishing tools and strategies, and crowdsource the most laborious step through the use of gamification. In addition, we propose a new family-centric search strategy and an incremental annotation approach to provide a tractable solution to the re-analysis problem while also providing opportunities to improve the annotation quality.
