IQSeq

IQSeq quantifies and reconstructs gene isoform abundances from RNA sequencing (RNA-Seq) data to enable isoform-level expression analysis.

Key Features:

• Statistical Solution for Isoform Quantification: Implements a statistical framework to reconstruct isoform abundances from RNA-Seq reads based on theoretical results.
• Efficient Accuracy Estimation: Estimates the average variance of isoform abundance estimates using a fast algorithm based on the Fisher Information Matrix, achieving approximately 500-fold speedup over brute-force resampling.
• Transcriptome Complexity Measurement: Computes an information-theoretic measure of overall transcriptome complexity to summarize isoform abundance diversity across experiments.
• Experimental Design Optimization: Models integration of different sequencing technologies and associated costs to support RNA-Seq experimental design optimization.
• Integration of Multiple Sequencing Technologies: Provides a unified statistical formalism for analysis of samples generated from diverse sequencing platforms within a single framework.
• Modular Read-Generation Functionality: Includes a generalized statistical partial-sample-generation function to model the sequencing process and enable plugin-able read-generation functions.

Scientific Applications:

• Gene expression studies: Provides isoform-level abundance estimates for expression profiling and differential expression analysis.
• Transcriptome analysis: Quantifies isoform diversity and characterizes transcriptome complexity across experiments.
• Functional genomics: Supports investigation of isoform-specific regulation and functional annotation by supplying isoform abundance measurements.
• Experimental design and technology integration: Assists modeling of sequencing technology combinations and cost trade-offs for study design.

Methodology:

Uses a statistical framework to analyze RNA-Seq reads and reconstruct isoform abundances; computes average-variance estimates via the Fisher Information Matrix; calculates an information-theoretic transcriptome complexity measure; models integration of sequencing technologies; and implements a generalized partial-sample-generation function for read modeling.