Related Experiment Video
Updated: Oct 6, 2025
12:33
Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing
Published on: July 28, 2017
13.0K
Finding and Characterizing Repeats in Plant Genomes
Jacques Nicolas1, Sébastien Tempel2, Anna-Sophie Fiston-Lavier3,4
1Univ Rennes, Inria, CNRS, IRISA, Rennes, France. jacques.nicolas@inria.fr.
Methods in Molecular Biology (Clifton, N.J.)
|January 17, 2022
Summary
This chapter guides biologists through software for detecting repeat structures in plant genomes, focusing on transposable elements (TEs). It covers indexing, mapping, and advanced methods like Machine Learning and linguistic approaches for repeat family modeling.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Plant genomes are characterized by a high abundance of repetitive DNA sequences.
- Transposable elements (TEs) are a significant source of these repeats, necessitating specialized analytical tools.
Purpose of the Study:
- To provide a comprehensive overview of available software for automated detection of repeat structures in plant sequence data.
- To introduce foundational concepts and advanced methodologies for analyzing and modeling repeat families.
Main Methods:
- Review and selection of existing bioinformatics software for repeat scanning in genomic and read data.
- Explanation of sequence indexing, mapping, and querying techniques.
- Introduction to Machine Learning and linguistic approaches for building repeat family models.
Main Results:
- A curated selection of software tools for identifying various types of repeats, with a focus on transposable elements.
- Foundational principles for efficient sequence searching and pattern identification.
- An overview of Machine Learning and linguistic methods for de novo repeat family modeling.
Conclusions:
- Efficient detection and characterization of repeat structures in plant genomes require appropriate software and methodologies.
- Advanced computational approaches, including Machine Learning and linguistic models, offer powerful strategies for understanding complex repeat families.
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