A unified framework to analyze transposable element insertion polymorphisms using graph genomes

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Transposable elements (TEs) are mobile DNA sequences that drive genomic diversity through insertion polymorphisms.
• Analyzing polymorphic TEs is crucial for understanding genome evolution and variation.
• Existing methods may lack flexibility or ease of use for comprehensive TE insertion analysis.

Purpose of the Study:

• To introduce GraffiTE, a flexible computational pipeline for the identification and genotyping of polymorphic transposable element insertions.
• To enable non-expert users to perform detailed analyses of TE landscapes across diverse species and datasets.
• To leverage graph genomes and advanced structural variant detection for improved TE analysis.

Main Methods:

• Integration of state-of-the-art structural variant detection algorithms with graph genome approaches.
• Identification of polymorphic mobile element insertions from genomic assemblies or long-read sequencing data.
• Genotyping of TE variants using both short and long read sequencing datasets.

Main Results:

• Benchmarking on simulated and real datasets demonstrated high precision and recall rates for GraffiTE.
• GraffiTE successfully analyzed human, Drosophila melanogaster, maize, and Cannabis sativa pangenome data, showcasing its versatility.
• The pipeline revealed detailed landscapes of polymorphic TEs and their frequency variations across different individuals and cultivars.

Conclusions:

• GraffiTE provides a robust and user-friendly solution for analyzing polymorphic transposable element insertions.
• The pipeline is compatible with various sequencing technologies and adaptable to species with limited prior TE knowledge.
• GraffiTE facilitates comprehensive studies of genomic diversity driven by mobile DNA across a wide range of organisms.