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Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schäffer, Jinghui Zhang, Zheng Zhang, Webb Miller and David J. Lipman Nucleic Acids Research, 1997, Vol. 25 No. 17 Oxford University Press. Index. Introduction

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Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

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  1. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schäffer, Jinghui Zhang, Zheng Zhang, Webb Miller and David J. Lipman Nucleic Acids Research, 1997, Vol. 25 No. 17 Oxford University Press

  2. Index • Introduction • BLAST 1.0 • BLAST 2.0 • Future works • Conclusions

  3. Index • Introduction • BLAST 1.0 • BLAST 2.0 • Future works • Conclusions

  4. Introduction • The most used tool for searching protein and DNA similarities. • Variants • BLASTP – TBLASP • BLASTN – TBLASN • BLASTX – TBLASTX • …

  5. Index • Introduction • BLAST 1.0 • BLAST 2.0 • Future works • Conclusions

  6. BLAST 1.0 • Features • One hit seeding • Ungapped extension • Pros • More specific searching • Cons • Computational and memory consumption

  7. Index • Introduction • BLAST 1.0 • BLAST 2.0 • Future works • Conclusions

  8. BLAST 2.0 • Features • Two hit seeding • Gapped extension • Pros • Reduced computational and memory consumption • More relevant results • Cons • Less specific searching

  9. BLAST 2.0 – Two Hit method A

  10. BLAST 2.0 – One Hit vs. Two Hit

  11. BLAST 2.0 – Gapped Extension • Run an ungapped extension (as BLAST 1.0) • If the HSP > Sg • Run a gapped extension • Apply Smith-Waterman algorithm

  12. BLAST 2.0 – What’s Sg?

  13. BLAST 2.0 – Gapped Extension

  14. BLAST 2.0 – Comparison

  15. PSI-BLAST • Features • Use BLAST 2.0 output for building position-specific scoring matrices (PSSM). • Run an iterated variant of BLAST 2.0 to process PSSM. • Different scoring method. • Pros • More sensitivity. • Cons • More computational consumption.

  16. PSI-BLAST

  17. Future Works • High-performance computing. • Medicine Specialized • DNA sequencers

  18. Conclusions • BLAST 2.0 is more optimized than BLAST 1.0, by returning less results but more relevant. • High-performance approaches can optimized seeding and extension stages. • New heuristic models that improve previous techniques.

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