Alignment and mapping methodology influence transcript abundance estimation.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Transcript quantification accuracy in RNA-sequencing (RNA-seq) is crucial for downstream analyses.
• While quantification models are studied, the impact of read alignment methods on accuracy is less understood.
• Alignment choices can influence RNA-seq data interpretation.

Purpose of the Study:

• To investigate how different RNA-seq read mapping and alignment strategies affect transcript quantification accuracy.
• To evaluate the influence of alignment on subsequent differential gene expression analysis.
• To introduce and validate a novel alignment methodology, selective alignment.

Main Methods:

• Comparison of various read alignment methods and parameters using simulated and experimental RNA-seq datasets.
• Assessment of quantification accuracy and differential expression analysis outcomes.
• Development and testing of a selective alignment approach.

Main Results:

• Alignment methodology significantly impacts transcript quantification and differential expression analysis, even with a fixed quantification model.
• Effects of alignment choices can be substantial and may be underestimated with simulated data alone.
• The proposed selective alignment method demonstrates improved performance over lightweight approaches.

Conclusions:

• Read alignment is a critical factor influencing RNA-seq quantification and differential expression results.
• Experimental data reveals significant variability in lightweight alignment method performance.
• Selective alignment offers a computationally efficient and accurate alternative for RNA-seq data processing.