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Identifies repetitive sequences and reconstructs separated sections to provide full-length repeats and, for long-terminal repeat (LTR) retrotransposons, calculates age since insertion based on LTR divergence. TEnest is a repeat identification and display tool made specifically for highly repetitive genomes. It provides a chronological insertion display to give an accurate visual representation of transposable element (TE) integration history showing timeline, location, and families of each TE identified, thus creating a framework from which evolutionary comparisons can be made among various regions of the genome.
RepARK / Repetitive motif detection by Assembly of Repetitive K-mers
A de novo repeat assembly method that avoids potential biases by using abundant k-mers of NGS WGS reads without requiring a reference genome. For validation, repeat consensuses derived from simulated and real Drosophila melanogaster NGS WGS reads were compared to repeat libraries generated by four established methods. RepARK is orders of magnitude faster than the other methods and generates libraries that are: (i) composed almost entirely of repetitive motifs, (ii) more comprehensive and (iii) almost completely annotated by TEclass. Additionally, we show that the RepARK method is applicable to complex genomes like human and can even serve as a diagnostic tool to identify repetitive sequences contaminating NGS datasets.
A command line application to annotate transposable elements from paired-end whole genome shotgun data. There are many tools to estimate the mathematical nature of repeats from short sequence reads. There are also a number of tools for analyzing repeats directly from a genome assembly. This tool allows you to infer the abundance of repeat types in the genome without a reference genome sequence. The output files make it easy to quickly summarize genomic abundance by transposable element class, superfamily, family, or any other level of the repeat taxonomy.
Assembles repeat sequences directly from raw shotgun sequencing data. REPdenovo can construct various types of repeats that are highly repetitive and have low sequence divergence within copies. We show that REPdenovo is substantially better than existing methods both in terms of the number and the completeness of the repeat sequences that it recovers. The key advantage of REPdenovo is that it can reconstruct long repeats from sequence reads. REPdenovo is a powerful computational tool for annotating genomes and for addressing questions regarding the evolution of repeat families.
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