Advertisement

BLAST QuickStart

Example-Driven Web-Based BLAST Tutorial
  • David Wheeler
  • Medha Bhagwat
Part of the Methods in Molecular Biology™ book series (MIMB, volume 395)

Summary

The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity between protein or nucleotide sequences. The program compares nucleotide or protein sequences to sequence in a database and calculates the statistical significance of the matches. This chapter first provides an introduction to BLAST and then describes the practical application of different BLAST programs based on the BLAST Quick Start mini-course (www.ncbi.nlm.nih.gov/Class/minicourses). In each example, emphasis is placed on practical step-by-step procedures, although relevant theory is also given where it affects the choice of BLAST program, parameters, and database.

Keywords

NCBI BLAST mini-courses MegaBLAST human genome 

References

  1. 1.
    Madden, T. L. and McGinnis, S. (2004) Blast: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res. 32, W20–W25.CrossRefPubMedGoogle Scholar
  2. 2.
    Henikoff, S. and Henikoff, J. G. (1992) Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA 89, 10,915–10,919.Google Scholar
  3. 3.
    (1978) Atlas of Protein Sequence and Structure, chapter Matrices for detecting distant relationships. Natl. Biomed. Res. Found. Washington, DC.Google Scholar
  4. 4.
    Altschul, S. F. and Gish, W. (1996) Local alignment statistics. Methods Enzymol. 266, 460–480.CrossRefPubMedGoogle Scholar
  5. 5.
    Madden, T. L., Schaffer, A. A., Zhang, J., et al. (1997) Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402.CrossRefPubMedGoogle Scholar
  6. 6.
    Madden, T. The blast sequence analysis tool, in The NCBI Handbook.Google Scholar
  7. 7.
    Zhang, Z., Schwartz, S., Wagner, L., and Miller, W. (2000) A greedy algorithm for aligning dna sequences. J. Comput. Biol. 7, 203–214.CrossRefPubMedGoogle Scholar
  8. 8.
    Hermankova, M., Ray, S. C., Ruff, C., et al. (2001) Hiv- 1 drug resistance profiles in children and adults with viral load of <50 copies/ml receiving combination therapy. JAMA 286, 196–207.CrossRefPubMedGoogle Scholar
  9. 9.
    Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., and Wheeler, D. L. (2006) Genbank. Nucleic Acids Res. 34, 16–20.CrossRefGoogle Scholar
  10. 10.
    Mizrachi, I. Genbank, in The NCBI Handbook.Google Scholar
  11. 11.
    Wootton, J. C. and Federhen, S. (1996) Analysis of compositionally biased regions in sequence databases. Methods Enzymol. 266, 554–571 http://www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=handbook.chapter.ch16.
  12. 12.
    Tatusov, R. and Lipman, D.J., Dust. Unpublished data.Google Scholar
  13. 13.
    Wootton, J. C. and Federhen, S. (1993) Statistics of local complexity in amino acid sequences and sequence databases. Computers and Chemistry, Elsevier Science, Amsterdam, The Netherlands.Google Scholar

Copyright information

© Humana Press Inc. 2007

Authors and Affiliations

  • David Wheeler
    • 1
  • Medha Bhagwat
    • 1
  1. 1.National Center for Biotechnology Information, National Library of Medicine, National Institutes of HealthBethasda

Personalised recommendations