Low-cost, Low-bias and Low-input RNA-seq with High Experimental Verifiability based on Semiconductor Sequencing

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Low-input RNA sequencing (RNA-seq) is crucial for gene expression analysis with limited cellular material.
• Existing methods often introduce bias or require high throughput, limiting accessibility.
• There is a need for cost-effective, low-bias RNA-seq techniques for small cell populations.

Purpose of the Study:

• To develop a simple, low-cost, and low-bias RNA-seq method for low-input samples.
• To introduce a novel, highly accurate, and error-tolerant spliced mapping algorithm for single-ended reads.
• To provide a robust solution for gene expression profiling comparable to bulk RNA-seq.

Main Methods:

• Development of a low-input RNA-seq protocol utilizing ion torrent semiconductor sequencing (LIEA RNA-seq).
• Creation of FANSe2splice, a spliced mapping algorithm designed for accuracy and error tolerance with single-ended reads.
• Comparative analysis of LIEA RNA-seq with bulk mRNA-seq for quantification and splice junction detection.

Main Results:

• LIEA RNA-seq demonstrates comparable quantification accuracy to bulk mRNA-seq.
• The method reliably detects splice junctions with high experimental verifiability.
• Significantly minimized bias and reduced mappable reads compared to existing approaches.

Conclusions:

• LIEA RNA-seq offers a powerful, accessible, and accurate alternative for gene expression profiling from low-input samples.
• The combination of LIEA RNA-seq and FANSe2splice provides a robust and cost-effective solution for genomic studies.
• This approach enhances the reliability and reduces biases in low-input RNA sequencing applications.