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MCHelper Automatically Curates Transposable Element Libraries Across Species

GENOME RESEARCH(2024)

UPF | Univ Cambridge

Cited 3|Views5
Abstract
The number of species with high-quality genome sequences continues to increase, in part due to the scaling up of multiple large-scale biodiversity sequencing projects. While the need to annotate genic sequences in these genomes is widely acknowledged, the parallel need to annotate transposable element (TE) sequences that have been shown to alter genome architecture, rewire gene regulatory networks, and contribute to the evolution of host traits is becoming ever more evident. However, accurate genome-wide annotation of TE sequences is still technically challenging. Several de novo TE identification tools are now available, but manual curation of the libraries produced by these tools is needed to generate high-quality genome annotations. Manual curation is time-consuming, and thus impractical for large-scale genomic studies, and lacks reproducibility. In this work, we present the Manual Curator Helper tool MCHelper, which automates the TE library curation process. By leveraging MCHelper's fully automated mode with the outputs from three de novo TE identification tools, RepeatModeler2, EDTA, and REPET, in the fruit fly, rice, hooded crow, zebrafish, maize, and human, we show a substantial improvement in the quality of the TE libraries and genome annotations. MCHelper libraries are less redundant, with up to 65% reduction in the number of consensus sequences, have up to 11.4% fewer false positive sequences, and up to similar to 48% fewer "unclassified/unknown" TE consensus sequences. Genome-wide TE annotations are also improved, including larger unfragmented insertions. Moreover, MCHelper is an easy-to-install and easy-to-use tool.
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Key words
genome annotation,Transposable Elements,Repetitive Elements
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要点】:本文介绍了MCHelper工具,一种自动编辑物种转座子库的方法,提高了基因组注释的质量和效率,降低了冗余和错误率。

方法】:MCHelper通过整合两种转座子识别工具RepeatModeler2和REPET的输出结果,自动化完成转座子库的编辑过程。

实验】:在果蝇、水稻和斑马鱼中使用MCHelper,与手动编辑相比,生成的转座子库冗余减少了54%,假阳性序列减少了11.4%,未分类/未知转座子序列减少了约45%,并且提高了基因组范围内转座子注释的质量,包括更少的碎片插入。实验使用的数据集未在摘要中明确提及。