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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

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
Index
  • Introduction
  • BLAST 1.0
  • BLAST 2.0
  • Future works
  • Conclusions
index1
Index
  • Introduction
  • BLAST 1.0
  • BLAST 2.0
  • Future works
  • Conclusions
introduction
Introduction
  • The most used tool for searching protein and DNA similarities.
  • Variants
    • BLASTP – TBLASP
    • BLASTN – TBLASN
    • BLASTX – TBLASTX
index2
Index
  • Introduction
  • BLAST 1.0
  • BLAST 2.0
  • Future works
  • Conclusions
blast 1 0
BLAST 1.0
  • Features
    • One hit seeding
    • Ungapped extension
  • Pros
    • More specific searching
  • Cons
    • Computational and memory consumption
index3
Index
  • Introduction
  • BLAST 1.0
  • BLAST 2.0
  • Future works
  • Conclusions
blast 2 0
BLAST 2.0
  • Features
    • Two hit seeding
    • Gapped extension
  • Pros
    • Reduced computational and memory consumption
    • More relevant results
  • Cons
    • Less specific searching
blast 2 0 gapped extension
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
psi blast
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.
future works
Future Works
  • High-performance computing.
  • Medicine Specialized
  • DNA sequencers
conclusions
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.