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Using WoLF PSORT for Protein Subcellular Localization

  • Prediction, Interpretation & Genome-Wide Classification
  • 👩‍🔬 Muhammad Shafiq | 📅 Date
  • https://youtu.be/Gq3UPEBWepE

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Introduction to Subcellular Localization

  • • Determines where a protein functions inside the cell
  • • Key locations: Cytoplasm, Nucleus, Chloroplast, Mitochondria, Plasma Membrane, Vacuole
  • • Important for functional annotation & genome studies

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What is WoLF PSORT?

  • • Word-based Protein Subcellular Localization Tool
  • • Combines PSORT + Machine Learning (kNN)
  • • Supports plant, animal, fungal proteins
  • • URL: https://wolfpsort.hgc.jp

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Input Data Requirements

  • • FASTA format protein sequences
  • • Full-length sequence recommended
  • • Proper gene/protein IDs
  • • Batch submission supported

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Step-by-Step Workflow

  • 1. Collect protein FASTA sequences
  • 2. Open WoLF PSORT webserver
  • 3. Select organism type
  • 4. Upload FASTA or paste sequences
  • 5. Run prediction
  • 6. Download results

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Example Run

  • • Plant model used
  • • Mango genome protein sequences
  • • Ranked localization predictions with scores
  • • Example: Gene001 chloroplast:14 cytosol:3

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Understanding the Output

  • • Gene ID + Predicted location + Score
  • • Highest score = most likely localization
  • • Multiple locations = dual targeting or low confidence

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Classification for Genome-Wide Analysis

  • • Run genome-wide FASTA
  • • Export results in text/Excel
  • • Classify: Nucleus, Chloroplast, Mitochondria, Cytoplasm, Membrane, Extracellular
  • • Summarize with Python/R

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Post-Processing with Python

  • import pandas as pd
  • df = pd.read_csv('wolfpsort_results.txt', sep='\t', header=None)
  • df.columns = ['GeneID','Prediction']
  • df['Main_location'] = df['Prediction'].str.extract(r'(\w+):\d+')
  • counts = df['Main_location'].value_counts()
  • print(counts)

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Data Visualization & Insights

  • • Use bar/pie charts for % distribution
  • • Biological relevance:
  • - TFs in nucleus
  • - Photosynthesis genes in chloroplast
  • - Metabolic enzymes in cytoplasm
  • • Functional classification & network modeling

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Integration with Other Tools

  • • Combine WoLF PSORT with:
  • - Gene Ontology
  • - Protein-protein interactions
  • - KEGG pathways
  • • For gene family studies & stress response

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Best Practices

  • • Use full-length protein sequences
  • • Verify uncertain predictions with TargetP or SignalP
  • • Batch analyze for whole genomes

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Conclusion

  • • WoLF PSORT = powerful localization predictor
  • • Enables large-scale functional classification
  • • Supports functional annotation & biological pathway understanding

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Useful Links & References

  • • https://wolfpsort.hgc.jp
  • • Horton P. et al. Nucleic Acids Res (2007)
  • • TargetP & SignalP
  • • Python Pandas for result analysis