Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Multiple Alignment

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 pa...

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Bibliographic Details
Published in:Science (American Association for the Advancement of Science) 1993-10, Vol.262 (5131), p.208-214
Main Authors: Lawrence, Charles E., Altschul, Stephen F., Boguski, Mark S., Liu, Jun S., Neuwald, Andrew F., Wootton, John C.
Format: Article
Language:English
Subjects:
550400 - Genetics
990200 - Mathematics & Computers
Algorithms
AMINO ACID SEQUENCE
Amino acids
Analysis
AUTOMATION
BASIC BIOLOGICAL SCIENCES
CALCULATION METHODS
Carrier Proteins - chemistry
Chromosome mapping
Datasets
DNA SEQUENCING
GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
Helix-Loop-Helix Motifs
Heuristics
Information economics
ITERATIVE METHODS
Mathematical sequences
Models, Statistical
Molecular Sequence Data
MOLECULAR STRUCTURE
Nucleic acids
Parametric models
PATTERN RECOGNITION
Probabilities
PROGRAMMING
Protein Prenylation
Protein Structure, Secondary
Sequence Alignment - methods
Software
STRUCTURAL CHEMICAL ANALYSIS
Transferases - chemistry
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Summary: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.
ISSN:0036-8075
1095-9203
DOI:10.1126/science.8211139