Prostate Cancer Translational Research Hub
Empowering clinicians and researchers with cutting-edge bioinformatics tools that bridge the gap between bedside, bench, and bytes. Our comprehensive suite of open-access platforms accelerates discovery, enables precision medicine, and democratizes access to advanced cancer research capabilities—no coding required.
HuPSA-MoPSA: Single-Cell Atlas Explorer
Decode Human & Mouse Prostate Cancer at Single-Cell Resolution
Navigate the cellular landscape of prostate cancer with unprecedented detail through our comprehensive single-cell RNA sequencing atlas.
Discover Novel Biomarkers: Uncover hidden cellular populations like KRT7-high and SOX2/FOXA2+ progenitor-like cells linked to aggressive disease progression
Clinical Translation: Validate subtype markers across 50+ bulk transcriptome datasets from human clinical specimens
Interactive Visualization: Generate publication-ready figures with real-time gene expression analysis
Cross-Species Analysis: Compare molecular profiles between human and mouse models for translational insights
CTPC: Prostate Cancer Cell Line Encyclopedia
Precision Medicine Starts with Precise Preclinical Models
Optimize your research model selection with comprehensive molecular profiling of 2,000+ prostate cancer cell lines.
Golden-Standard Baselines: Access molecular profiles of established cell lines (LNCaP, PC3, DU145) with quality-controlled data
Treatment-Gene Networks: Identify drug-responsive pathways for mechanistic studies or drug repurposing
Biomarker Validation: Cross-reference datasets to prioritize targets with clinical translational potential
Data Export: Download normalized expression matrices and analysis results for downstream applications
LNCaP-ADT Multi-Omics Hub
Deciphering Androgen Deprivation Resistance Mechanisms
Explore the molecular evolution of treatment resistance through integrated multi-omics analysis of 500+ LNCaP samples during androgen deprivation therapy.
Multi-Omics Integration: Correlate transcriptomic, epigenetic, and transcription factor occupancy data
Dynamic Adaptation Maps: Track molecular changes during ADT at single-cell resolution
Resistance Mechanisms: Identify drivers of castration resistance and therapeutic vulnerabilities
Network Analysis: Export co-expression networks for experimental validation
PCTA: Pan-Cancer Cell Line Transcriptome Atlas
Expanding Horizons Beyond Prostate Cancer
Compare prostate cancer biology with 535+ cell lines across 114 cancer types to identify conserved mechanisms and unique therapeutic opportunities.
Cross-Cancer Insights: Comprehensive dataset spanning 24,965 genes across 84,385 samples from 5,677 studies
Biomarker Discovery: Validate prostate cancer-specific markers and identify cross-cancer therapeutic targets
Tissue-Specific Clustering: Visualize relationships between cancer types and identify shared pathways
Drug Repurposing: Leverage pan-cancer data to identify therapeutic opportunities from other oncology areas
IMPACT-sc: Integrated Single-Cell Analysis Pipeline
Modular Single-Cell RNA-seq Analysis Workflow
A comprehensive pipeline for single-cell transcriptomics analysis, integrating data processing, cell type annotation, differential expression, trajectory inference, and multi-omics integration.
Modular Architecture: 10+ analysis modules from QC to advanced downstream analyses with interactive configuration
AI-Powered Annotation: Integrates Cell2Sentence for semantic cell type prediction and SingleR for reference-based annotation
Advanced Analytics: Pathway analysis with DecoupleR, gene signature scoring with UCell, and pseudotime analysis with Palantir
Cross-Platform Integration: Seamless R/Python integration with automated environment management and dependency handling
Key Analysis Modules:
Data Processing: QC filtering, normalization, and batch correction with Harmony
Cell Type Annotation: Multi-method annotation combining Seurat clustering, SingleR, and Cell2Sentence
Differential Expression: Statistical analysis with Gene Set Enrichment Analysis (GSEA)
Pathway Analysis: Transcription factor activity inference and pathway scoring
SRA-LLM: Smart Research Assistant
AI-Powered Research Literature Analysis
An intelligent research assistant leveraging Large Language Models to accelerate literature review, hypothesis generation, and knowledge discovery in cancer research.
LLM Integration: Powered by state-of-the-art language models for intelligent literature analysis and synthesis
Literature Mining: Automated extraction and summarization of key findings from research publications
Hypothesis Generation: AI-assisted identification of research gaps and novel research directions
Knowledge Integration: Connects findings across studies to reveal hidden patterns and relationships
Research Applications:
Literature Review: Automated summarization and synthesis of research papers
Concept Discovery: Identify emerging trends and novel therapeutic targets
Experimental Design: AI-assisted methodology recommendations and protocol optimization
Data Interpretation: Contextual analysis of experimental results within existing literature
Choose Your Tool: Select the platform that best fits your research question or analytical needs
Explore Data: Use intuitive interfaces to search genes, browse datasets, or configure analysis pipelines
Generate Insights: Create publication-ready visualizations and export results for further analysis
Validate Findings: Cross-reference results across multiple tools and datasets for robust conclusions
No Coding Required: Intuitive web interfaces make advanced bioinformatics accessible to all researchers
Mobile Optimized: Analyze data anywhere, anytime—even on your smartphone or tablet
Open Science: All datasets are publicly available with peer-reviewed, reproducible methods
Real-Time Analysis: Instant results with interactive visualizations and customizable parameters
Clinical Translation: Bridge preclinical findings with clinical data for translational insights
Comprehensive Coverage: From single cells to populations, from discovery to validation