samExploreR

samExploreR explores the effects of sequencing depth on RNA-seq data analysis by subsampling aligned short reads from Sequence Alignment/Map (SAM) files to assess reproducibility, robustness, and impacts on differential gene expression and annotation.

Key Features:

• Subsampling Capability: Implements an "m out of n" bootstrapping subsampling procedure that draws aligned short reads from SAM files to simulate reduced sequencing depth.
• Sequencing Depth Exploration: Simulates varying sequencing depths to evaluate how depth influences downstream analysis outcomes and experimental design trade-offs.
• Reproducibility and Robustness Assessment: Quantifies the reproducibility and robustness of RNA-seq analysis results under varied sequencing depths.
• Differential Gene Expression Analysis: Evaluates how sequencing depth and annotation affect the identification of differentially expressed genes.

Scientific Applications:

• RNA-seq Experimental Design Optimization: Inform sequencing depth decisions for RNA sequencing experiments by simulating reduced-depth datasets.
• Reproducibility Studies: Assess robustness and reproducibility of RNA-seq analyses across different sequencing depths and datasets.
• Annotation Impact Evaluation: Evaluate the influence of genome or transcriptome annotation on detection of differentially expressed genes.

Methodology:

Subsampling of aligned short reads from SAM files using an "m out of n" bootstrapping procedure to generate simulated reduced-depth datasets for downstream analysis.