LocusMasterTE: integrating long-read RNA sequencing improves locus-specific quantification of transposable element...

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Transposable elements (TEs) are mobile DNA sequences that can impact genome integrity and contribute to human diseases.
• Accurate quantification of TE expression is crucial for understanding their role in disease but remains challenging due to their repetitive genomic sequences.
• Existing methods struggle to precisely quantify TE expression from RNA sequencing data.

Purpose of the Study:

• To develop a novel computational method for accurate quantification of transposable element (TE) expression.
• To improve the accuracy of short-read RNA sequencing-based TE quantification by integrating long-read data.
• To provide a tool that facilitates new insights into the functional roles of TEs in biological processes and diseases.

Main Methods:

• Developed LocusMasterTE, a method integrating long-read and short-read RNA sequencing data.
• Utilized fractional transcript per million (TPM) values from long-read data within an expectation-maximization algorithm.
• Implemented a read reassignment strategy for multi-mapped reads to enhance short-read quantification accuracy.

Main Results:

• LocusMasterTE demonstrated increased accuracy in quantifying transposable element expression.
• The method effectively reassigns multi-mapped reads, a key challenge in TE quantification.
• Validation using simulated and human datasets confirmed the method's performance.

Conclusions:

• LocusMasterTE offers a more precise approach to quantifying transposable element expression.
• This enhanced accuracy can provide deeper insights into the functional significance of TEs.
• The method has the potential to advance research into TE-mediated impacts on human health and disease.