metaseqR2

metaseqR2 performs normalization, statistical testing, and meta-analysis of RNA-Seq gene expression data to identify differentially expressed genes while addressing biases such as gene length and molecule-specific detection issues.

Key Features:

• Normalization and Statistical Testing: Provides multiple options for normalizing RNA-Seq data and applying statistical tests to detect differential gene expression.
• Integration with Packages: Interfaces with several normalization and statistical testing packages to generate input results for downstream combination.
• PANDORA algorithm: Implements the PANDORA algorithm to combine results from multiple statistical tests for meta-analysis, optimizing the balance between precision and sensitivity and mitigating normalization-induced biases.
• Enhanced Detection of Transcript Types: Detects differential expression across various transcript types, including long non-coding RNAs.
• Diagnostic plots and annotation database building: Produces diagnostic plots and supports building annotation databases for downstream analyses.
• Performance improvements: Includes runtime optimizations yielding reported speedups (approximately 5–50×) relative to earlier versions.

Scientific Applications:

• Differential gene expression analysis: Identification of differentially expressed genes from RNA-Seq experiments.
• Meta-analysis of statistical results: Combining multiple statistical tests and normalization strategies to produce robust gene lists.
• Transcriptome profiling including lncRNAs: Detection and analysis of expression patterns across coding and non-coding transcript types, including long non-coding RNAs.
• Preparation for downstream pathway and annotation analyses: Generation of annotated result sets and diagnostic outputs for pathway-level interpretation.

Methodology:

Performs normalization and statistical testing on RNA-Seq data, combines test results via the PANDORA meta-analysis algorithm, addresses gene-length and molecule detection biases, generates diagnostic plots and annotation databases, and applies performance optimizations with reported 5–50× speed improvements.