Improved TGIRT-seq methods for comprehensive transcriptome profiling with decreased adapter dimer formation and bias...

Area of Science:

• Molecular Biology
• Genomics
• Biochemistry

Background:

• Thermostable Group II Intron Reverse Transcriptases (TGIRTs) are valuable tools for various RNA sequencing (RNA-seq) applications due to their high fidelity and processivity.
• Existing TGIRT-seq methods have been applied to profile diverse RNA types, including whole-cell, exosomal, and plasma RNAs, as well as for quantitative tRNA sequencing and mapping RNA modifications and structures.

Purpose of the Study:

• To improve TGIRT-seq methods for comprehensive transcriptome profiling.
• To rationally design RNA-seq adapters to minimize adapter dimer formation.
• To develop methods for remediating 5' and 3' end biases in RNA sequencing data.

Main Methods:

• Rational design of RNA-seq adapters to reduce adapter dimer formation.
• Development of biochemical and computational methods to correct for 5' and 3' end biases.
• Utilized a random forest regression model to analyze factors contributing to 3' end biases.

Main Results:

• Successfully minimized adapter dimer formation through improved adapter design.
• Developed effective strategies for remediating 5' and 3' end biases in RNA-seq data.
• Achieved increased efficiency in TGIRT-seq library construction and enhanced coverage of small RNAs, including microRNAs (miRNAs).

Conclusions:

• The enhanced TGIRT-seq methods improve library construction efficiency and small RNA coverage.
• The findings offer insights into the biochemical basis of RNA-seq biases and propose general solutions.
• Some improvements may be transferable to other RNA-seq methodologies.