An Introduction to Sequence Similarity (“Homology”) Searching
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Abstract
Sequence similarity searching, typically with BLAST, is the most widely used and most reliable strategy for characterizing newly determined sequences. Sequence similarity searches can identify ?homologous? proteins or genes by detecting excess similarity? statistically significant similarity that reflects common ancestry. This unit provides an overview of the inference of homology from significant similarity, and introduces other units in this chapter that provide more details on effective strategies for identifying homologs. Curr. Protoc. Bioinform. 42:3.1.1?3.1.8. © 2013 by John Wiley & Sons, Inc.
Keywords: sequence similarity; homology; orthlogy; paralogy; sequence alignment; multiple alignment; sequence evolution
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- An Introduction to Identifying Homologous Sequences
- Inferring Homology from Similarity
- Inferring Function from Homology
- From Pairwise to Multiple Sequence Alignment
- Summary
- Literature Cited
- Figures
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Figure 3.1.1 The distribution of real and expected similarity scores. The human dual specificity protein phosphatase 12 (DUS12_HUMAN) was compared to 38,114 human RefSeq proteins using the SSEARCH program. The distribution of bit‐scores (or standard deviations above and below the mean 0) for all 38,114 alignments is shown (squares, □), as well as the mathematically expected distribution of z ‐scores based on the size of the database, using the extreme‐value distribution. The close agreement between the observed and expected distribution of scores reflects the observation that the distribution of unrelated sequence scores is indistinguishable from random (mathematically generated) scores, so sequences with significant sequence similarity can be inferred to be not‐unrelated, or homologous. View Image
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Literature Cited
| Literature Cited | |
| Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI‐BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25:3389‐3402. | |
| Edgar, R.C. 2004. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32:1792‐1797. | |
| Gerlt, J.A. and Babbitt, P.C. 2000. Can sequence determine function? Genome Biol. 1:REVIEWS0005. | |
| Gonzalez, M.W. and Pearson, W.R. 2010. Homologous over‐extension: A challenge for iterative similarity searches. Nucleic Acids Res. 38:2177‐2189. | |
| Johnson, L.S., Eddy, S.R., and Portugaly, E. 2010. Hidden markov model speed heuristic and iterative hmm search procedure. BMC Bioinformatics 11:431. | |
| Katoh, K., Misawa, K., Kuma, K., and Miyata, T. 2002. MAFFT: A novel method for rapid multiple sequence alignment based on fast fourier transform. Nucleic Acids Res. 30:3059‐3066. | |
| Koonin, E.V. 2005. Orthologs, paralogs, and evolutionary genomics. Ann. Rev. Genet. 39:309‐338. | |
| Larkin, M.A., Blackshields, G., Brown, N.P., Chenna, R., McGettigan, P.A., McWilliam, H., Valentin, F., Wallace, I.M., Wilm, A., Lopez, R., Thompson, J.D., Gibson, T.J., and Higgins, D.G. 2007. Clustal W and Clustal X version 2.0. Bioinformatics 23:2947‐2948. | |
| Li, W., McWilliam, H., Goujon, M., Cowley, A., Lopez, R., and Pearson, W.R. 2012. PSI‐Search: Iterative HOE‐reduced profile Ssearch searching. Bioinformatics 28:1650‐1651. | |
| Pearson, W.R. 1991. Searching protein sequence libraries: Comparison of the sensitivity and selectivity of the smith‐waterman and FASTA algorithms. Genomics 11:635‐650. | |
| Pearson, W.R. and Lipman, D.J. 1988. Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. U.S.A. 85:2444‐2448. | |
| Smith, T.F. and Waterman, M.S. 1981. Identification of common molecular subsequences. J. Mol. Biol. 147:195‐197. |
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