GEN

GEN provides integrated access to transcriptomic profiles across multiple species, aggregating bulk and single-cell RNA sequencing datasets to support analysis of transcriptional and post-transcriptional regulatory mechanisms.

Key Features:

• Comprehensive Dataset Integration: Curates 323 transcriptomic datasets (157 bulk RNA sequencing and 166 single-cell RNA sequencing) encompassing 50,500 samples and over 15 million cells from 30 species.
• Standardized Data Processing: Applies standardized data processing pipelines to ensure consistency across integrated datasets.
• Structured Curation Model: Categorizes datasets into six distinct biological contexts using a structured curation model.
• Transcriptomic Profiles Diversity: Provides expression levels, RNA editing events, and alternative splicing information, with post-transcriptional profiles available for 10 bulk datasets.
• Gene Annotations: Generates gene annotations derived from curated transcriptomic data to support interpretation of regulatory architectures.
• Data Analysis and Visualization: Provides data analysis and visualization functions for exploration and interpretation of transcriptomic datasets.

Scientific Applications:

• Genetic Regulatory Network Analysis: Enables study of genetic regulatory networks from tissues to individual cells using bulk and single-cell RNA-seq data.
• Functional Genomic Element Investigation: Supports investigation of functional genomic elements through transcriptional and post-transcriptional analyses.
• Comparative Transcriptomics: Facilitates comparative transcriptomic analyses across 30 species.
• Gene Expression Dynamics: Allows exploration of gene expression dynamics under diverse biological conditions using 50,500 samples and over 15 million cells.
• RNA Editing and Alternative Splicing Studies: Permits investigation of mechanisms underlying RNA editing and alternative splicing, including specific annotations in 10 bulk datasets.

Methodology:

Employs rigorous data curation and standardization, applies standardized data processing pipelines, uses a structured curation model to categorize datasets into six biological contexts, and integrates multiple transcriptomic profiles from bulk and single-cell RNA sequencing.