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

1 Index  Introduction  BLAST 1.0  BLAST 2.0  Future works  Conclusions

2 Index  Introduction  BLAST 1.0  BLAST 2.0  Future works  Conclusions

Introduction  The most used tool for searching protein and DNA similarities.  Variants BLASTP – TBLASP BLASTN – TBLASN BLASTX – TBLASTX … 3

4 Index  Introduction  BLAST 1.0  BLAST 2.0  Future works  Conclusions

BLAST 1.0  Features One hit seeding Ungapped extension  Pros More specific searching  Cons Computational and memory consumption 5

6 Index  Introduction  BLAST 1.0  BLAST 2.0  Future works  Conclusions

BLAST 2.0  Features Two hit seeding Gapped extension  Pros Reduced computational and memory consumption More relevant results  Cons Less specific searching 7

BLAST 2.0 – Two Hit method 8 A

BLAST 2.0 – One Hit vs. Two Hit 9

BLAST 2.0 – Gapped Extension 1.Run an ungapped extension (as BLAST 1.0) 2.If the HSP > S g 3.Run a gapped extension 1.Apply Smith-Waterman algorithm 10

BLAST 2.0 – What’s S g ? 11

BLAST 2.0 – Gapped Extension 12

BLAST 2.0 – Comparison 13

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. 14

PSI-BLAST 15

Future Works  High-performance computing.  Medicine Specialized  DNA sequencers 16

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. 17