Help Center

Comprehensive guide to the Animal Hormone Database and Gene Insights Hub platform

Platform Overview

This platform integrates two powerful bioinformatics analysis systems:

🧬 Animal Hormone Database

Comprehensive hormone data repository with BLAST alignment, transcriptome analysis, DNA methylation analysis, GNN prediction, and phylogenetic analysis tools.

📊 Gene Insights Hub

Multi-dimensional gene set analysis platform featuring Venn diagrams, UpSet plots, GO/KEGG enrichment, single-cell analysis, and Sankey visualization.

Feature Modules
Module Description Access
BLAST Alignment Sequence similarity search against hormone-related gene database. Supports nucleotide and protein BLAST with E-value filtering and visualization. Start →
Single Cell Analysis Explore gene expression across 94,836 cells from Tabula Sapiens dataset. UMAP/t-SNE visualization with cell type and tissue annotations. Start →
DNA Methylation Analyze DNA methylation patterns associated with hormone regulation. CpG island analysis and methylation heatmaps. Start →
Transcriptome Analysis Differential expression analysis, gene set enrichment, and pathway visualization for hormone-related studies. Start →
GNN Prediction Graph Neural Network-based prediction of hormone-receptor interactions with confidence scoring (High/Medium/Low). Start →
Phylogenetic Analysis Cross-species comparison and evolutionary analysis of hormone-related genes with phylogenetic tree visualization. Start →
KEGG Pathway Browse and analyze KEGG pathways related to hormone signaling and metabolism. Start →
Gene Prediction Predict hormone-related gene functions using Smith-Waterman alignment algorithm. Start →
AI Assistant Intelligent Q&A assistant for bioinformatics questions. Ask about GO/KEGG analysis, single-cell analysis, platform usage, and more. Start →
Gene Insights Hub Guide

Complete workflow for gene set analysis:

  1. 1
    Input Gene Lists Enter gene names in List 1, List 2 text boxes (one gene per line), or upload TXT/CSV files, or search from database.
  2. 2
    Run Analysis Click "Analyze" button to calculate intersections. View results in UpSet Plot (matrix view) or Classic Venn diagram.
  3. 3
    Select Genes Click bars in UpSet plot to select intersection genes. Selected genes appear in the panel below.
  4. 4
    Enrichment Analysis Click "GO" for Gene Ontology enrichment or "KEGG" for pathway enrichment. View results as bar/bubble/pie charts and tables.
  5. 5
    Validation Analysis Use Single-Cell Map to view gene expression patterns, Sankey-Bubble Plot for gene-pathway relationships, or STRING for protein interactions.
  6. 6
    Download Results Export charts (PNG/JPG/SVG) and data tables (CSV) for further analysis and publication.
FAQ

Q: How many gene lists can I analyze?

A: At least 2 lists are required. You can add more lists (3-6 recommended for clear visualization).

Q: What gene name format should I use?

A: Use official gene symbols (e.g., TP53, BRCA1). Case-insensitive. One gene per line.

Q: How to interpret enrichment results?

A: Focus on Adjusted P-value < 0.05 results. Higher gene counts and lower P-values indicate more significant enrichment.

Q: Can I use non-human genes?

A: The platform is optimized for human genes. Other species may have limited database coverage.

Q: How to get help if I encounter problems?

A: Click the AI Assistant button (purple circle) at the bottom right corner of any page to ask questions.

Contact Us

For technical support, feature suggestions, or collaboration inquiries:

  • Inner Mongolia University of Science and Technology
  • School of Life Science and Technology
  • Contact: 18879312606