Differential Analysis Reveals Isoform Switching Following Pneumococcal Vaccination

The preprint operates within the current paradigm of bioinformatics and transcriptomics, utilizing established RNA-seq technologies and analysis methods. It specifically focuses on the well-established concepts of differential gene expression and isoform switching.

This preprint represents a clear example of normal science. It operates within the accepted paradigm of transcriptomics and seeks to refine existing methods for analyzing time-series RNA-seq data. The study applies spline regression at the isoform level, a technique that builds upon existing statistical methods and bioinformatics tools like sleuth and Kallisto. The authors aim to improve the sensitivity and biological relevance of differential gene expression analysis, particularly in the context of isoform switching. There is no challenge to the existing paradigm or proposal of a radically new framework. Instead, the research focuses on improving the precision and comprehensiveness of analysis within the current paradigm. The authors highlight the advantages of their method compared to traditional pairwise comparisons and gene-level analyses, demonstrating a focus on puzzle-solving within the established framework. The use of established datasets and benchmarking against existing methods (e.g., edgeR-limma) further reinforces the classification of this work as normal science.