RNASEQR

RNASEQR performs automated two-group RNA-Seq analysis to quantify gene expression, detect alternative exon usage, identify novel transcriptional isoforms, and detect single nucleotide variants (SNVs) from next-generation sequencing (NGS) RNA-seq data.

Key Features:

• Automated six-step workflow: Implements a six-step automated workflow comprising RNASeqRParam S4 object creation, environment setup, quality assessment, reads alignment and quantification, gene-level differential analyses, and functional analyses.
• RNASeqRParam S4 Object Creation: Initializes analysis parameters using an RNASeqRParam S4 object.
• Environment Setup: Prepares the computational environment required for subsequent analysis steps.
• Quality Assessment: Evaluates the quality of raw sequencing data.
• Reads Alignment & Quantification: Aligns reads to a reference genome and quantifies gene expression levels, providing initial genome localization and alignment information.
• Gene-level Differential Analyses: Identifies differentially expressed genes between two groups (case-control).
• Functional Analyses: Performs downstream functional analyses on identified genes.
• Accurate Mapping and Analysis: Engineered to accurately map millions of RNA-seq sequences ensuring high-quality initial genome localization and alignment information.
• Performance and Validation: Systematically compared with four widely used RNA-Seq analysis tools using a simulated dataset from the Consensus CDS project and two experimental RNA-seq datasets from a human glioblastoma patient, demonstrating superior performance in estimating gene expression levels, identifying complete gene structures, discovering new transcript isoforms, and detecting SNVs.
• Compatibility: Produces results compatible with a wide variety of specialized downstream analyses.

Scientific Applications:

• Case-control differential expression: Suited for automated two-group RNA-Seq studies to identify differentially expressed genes in case-control designs.
• Cancer transcriptomics: Applicable to cancer studies to explore gene expression patterns, transcript isoforms, and SNVs, exemplified by analyses of human glioblastoma RNA-seq datasets.
• Transcriptome characterization: Enables analysis of alternative exon usage, novel transcriptional isoforms, and genomic sequence variations from RNA-seq data.

Methodology:

Computational workflow steps: RNASeqRParam S4 object creation; environment setup; quality assessment of raw reads; reads alignment and quantification to a reference genome; gene-level differential analyses; and functional analyses.