Query Search
Build powerful multi-strategy gene sets using Direct IDs, Pfam domains, Gene Symbols, Cellular Localisation or ML Predictions — then combine them with set operations and explore any section.
Tutorial & Examples
Example 1 – Simple Query
- Select Direct ID Search
- Enter
Ca_00001, Ca_00002 - Click Add Query
- Hit any Execute button to view results on that page
Example 2 – Union of two searches
- Add Query A: Pfam term
kinase - Add Query B: Localization Nuclear
- In Set Operations: Select A ∪ B
- Execute on the combined set
Example 3 – Intersection (multi-criteria filter)
- Add Query A: ML Prediction → Seed Tissue
- Add Query B: Gene Symbol →
ARF - Combine: A ∩ B (genes common to both)
- Save as Query C, then execute any section
Example 4 – Chain operations
- Build Queries A, B, C …
- Combine A ∪ B → Save as D
- Combine D ∩ C → Save as E
- Execute on final set E ✨
Set Operations:
- Union (A ∪ B) – all genes from both queries
- Intersection (A ∩ B) – genes common to both
- Difference (A − B) – genes in A but not B
- Difference (B − A) – genes in B but not A
- Symmetric Difference (A △ B) – genes in either but not both
Tips:
- 🔗 Chain: Save combined sets as new queries for further operations
- 📊 Multiple Execute: Click any section button to view those results
- 🎯 Strategy: Broad search → narrow with Intersections
- 💡 Pfam IDs: Use format like
PF03372or name likekinase
Step 1 — Add New Search Query
Search Method:
Separate multiple IDs with comma or space
Searches Pfam ID, Domain and Name columns (comma-separated)
Searches Gene Symbol, GO terms (molecular function / cellular component / biological process)
Searches genes by their most-likely cellular compartment
Searches genes by their ML-predicted tissue expression pattern
