Quantifying transcript complexity via the condition number of gene-specific random matrix.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Accurate transcript quantification is crucial but challenging due to discrepancies between computational tools.
• This variability arises from transcript complexity, influenced by alternative splicing and read length.
• Existing methods lack a unified standard for assessing quantification uncertainty.

Purpose of the Study:

• To introduce a theoretical framework for quantifying transcript complexity using the Condition Number (CN).
• To establish the CN as a principled standard for assessing quantification uncertainty in RNA sequencing.
• To guide the optimization of sequencing strategies for improved transcript quantification.

Main Methods:

• Developed a theoretical framework based on the Condition Number (CN) of a gene-specific random matrix.
• Analyzed the impact of alternative splicing and RNA-seq read length distribution on CN.
• Evaluated the correlation between CN and inter-tool concordance in real RNA-seq data.
• Modeled hybrid sequencing (hybrid-seq) error rates and determined optimal mixing ratios.

Main Results:

• The Condition Number (CN) quantifies transcript complexity and sets a theoretical bound for quantification error.
• CN strongly correlates with inter-tool concordance, explaining discrepancies in expression levels.
• Increasing read length decreases CN, highlighting the advantage of long-read sequencing.
• Hybrid-seq offers error rates no worse than individual methods, with potential for improvement at optimal ratios.

Conclusions:

• The CN provides a principled metric for assessing transcript complexity and quantification uncertainty.
• This framework elucidates a fundamental source of variability in transcript expression analysis.
• Findings guide the selection of sequencing technologies and strategies for more accurate transcript quantification.