The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Detecting Subtle Sequence Signals: a Gibbs Sampling Strategy for Multiple Alignment
Abstract
A wealth of protein and DNA sequence data is being generated by genome projects and other sequencing efforts. A crucial barrier to deciphering these sequences and understanding the relations among them is the difficulty of detecting subtle local residue patterns common to multiple sequences. Such patterns frequently reflect similar molecular structures and biological properties. A mathematical definition of this "local multiple alignment" problem suitable for full computer automation has been used to develop a new and sensitive algorithm, based on the statistical method of iterative sampling. This algorithm finds an optimized local alignment model for N sequences in N-linear time, requiring only seconds on current workstations, and allows the simultaneous detection and optimization of multiple patterns and pattern repeats. The method is illustrated as applied to helix-turn-helix proteins, lipocalins, and prenyltransferases.
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A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
PROTEIN PRENYLATION: GENES, ENZYMES, TARGETS, AND FUNCTIONS
Schafer, W. R., & Rine, J. (1992) · Annual Review of Genetics · doi:10.1146/annurev.ge.26.120192.001233 · Cited by 377
Cited by
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Self-citationThompson, J. D., Higgins, D. G., & Gibson, T. J. (1994) · Nucleic Acids Research · doi:10.1093/nar/22.22.4673 · Cited by 64,617
The ENCODE Project Consortium (2007) · Nature · doi:10.1038/nature05874 · Cited by 5,223
The Transcriptional Coactivators p300 and CBP Are Histone Acetyltransferases
Ogryzko, V. V., Schiltz, R. L., Russanova, V., Howard, B. H., & Nakatani, Y. (1996) · Cell · doi:10.1016/s0092-8674(00)82001-2 · Cited by 2,952
Roth, F. P., Hughes, J. D., Estep, P. W., & Church, G. M. (1998) · Nature Biotechnology · doi:10.1038/nbt1098-939 · Cited by 939
Metadata
- Authors
- Lawrence, C. E., Altschul, S. F., Boguski, M. S., Liu, J. S., Neuwald, A. F., & Wootton, J. C.
- Venue
- Science
- Published
- 1993-10-08
- Volume
- 262
- Issue
- 5131
- Pages
- 208-214
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W1998300401
- Cited by
- 1,807
- References
- 5
- FWCI
- 11.62
- Type
- journal article
- Language
- en
- Open access
- closed
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