MoDEL: an efficient strategy for ungapped local multiple alignment

We introduce a method for ungapped local multiple alignment (ULMA) in a given set of amino acid or nucleotide sequences. This method explores two search spaces using a linked optimization strategy. The first search space M consists of all possible words of a given length W, defined on the residue alphabet. An evolutionary algorithm searches this space globally. The second search space P consists of all possible ULMAs in the sequence set, each ULMA being represented by a position vector defining exactly one subsequence of length W per sequence. This search space is sampled with hill-climbing processes. The search of both spaces are coupled by projecting high scoring results from the global evolutionary search of M onto P. The hill-climbing processes then refine the optimization by local search, using the relative entropy between the ULMA and background residue frequencies as an objective function. We demonstrate some advantages of our strategy by analyzing difficult natural amino acid sequences and artificial datasets. A web interface is available at

[1]  Gary D. Stormo,et al.  Identification of consensus patterns in unaligned DNA sequences known to be functionally related , 1990, Comput. Appl. Biosci..

[2]  Douglas L. Brutlag,et al.  BioProspector: Discovering Conserved DNA Motifs in Upstream Regulatory Regions of Co-Expressed Genes , 2000, Pacific Symposium on Biocomputing.

[3]  William R. Atchley,et al.  Molecular Evolution of Helix–Turn–Helix Proteins , 1999, Journal of Molecular Evolution.

[4]  D. Higgins,et al.  T-Coffee: A novel method for fast and accurate multiple sequence alignment. , 2000, Journal of molecular biology.

[5]  Solomon Kullback,et al.  Information Theory and Statistics , 1970, The Mathematical Gazette.

[6]  Saurabh Sinha,et al.  A Statistical Method for Finding Transcription Factor Binding Sites , 2000, ISMB.

[7]  J. Thompson,et al.  CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. , 1994, Nucleic acids research.

[8]  Andrea Califano,et al.  SPLASH: structural pattern localization analysis by sequential histograms , 2000, Bioinform..

[9]  Charles Elkan,et al.  Unsupervised learning of multiple motifs in biopolymers using expectation maximization , 1995, Mach. Learn..

[10]  Burkhard Morgenstern,et al.  DIALIGN2: Improvement of the segment to segment approach to multiple sequence alignment , 1999, German Conference on Bioinformatics.

[11]  P. Bucher.  Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences. , 1990, Journal of molecular biology.

[12]  Terri K. Attwood,et al.  PRINTS and its automatic supplement, prePRINTS , 2003, Nucleic Acids Res..

[13]  Tim Jones.  Evolutionary Algorithms, Fitness Landscapes and Search , 1995 .

[14]  Uri Keich,et al.  Finding motifs in the twilight zone , 2002, RECOMB '02.

[15]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[16]  Jun S. Liu,et al.  Gibbs motif sampling: Detection of bacterial outer membrane protein repeats , 1995, Protein science : a publication of the Protein Society.

[17]  C. Notredame,et al.  Recent progress in multiple sequence alignment: a survey. , 2002, Pharmacogenomics.

[18]  Charles Elkan,et al.  Fitting a Mixture Model By Expectation Maximization To Discover Motifs In Biopolymer , 1994, ISMB.

[19]  Aris Floratos,et al.  Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm [published erratum appears in Bioinformatics 1998;14(2): 229] , 1998, Bioinform..

[20]  G. Church,et al.  Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. , 2000, Journal of molecular biology.

[21]  D. Higgins,et al.  Finding flexible patterns in unaligned protein sequences , 1995, Protein science : a publication of the Protein Society.

[22]  A. A. Reilly,et al.  An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences , 1990, Proteins.

[23]  Hiroki Arimura,et al.  On approximation algorithms for local multiple alignment , 2000, RECOMB '00.

[24]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[25]  Robert D. Finn,et al.  The Pfam protein families database , 2004, Nucleic Acids Res..

[26]  Uri Keich,et al.  Finding motifs in the twilight zone , 2002, Bioinform..

[27]  Gary D. Stormo,et al.  Identifying DNA and protein patterns with statistically significant alignments of multiple sequences , 1999, Bioinform..

[28]  Marie-France Sagot,et al.  Algorithms for Extracting Structured Motifs Using a Suffix Tree with an Application to Promoter and Regulatory Site Consensus Identification , 2000, J. Comput. Biol..

[29]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[30]  David R. Gilbert,et al.  Approaches to the Automatic Discovery of Patterns in Biosequences , 1998, J. Comput. Biol..

[31]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[32]  Jeremy Buhler,et al.  Finding Motifs Using Random Projections , 2002, J. Comput. Biol..

[33]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[34]  J. Collado-Vides,et al.  Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. , 1998, Journal of molecular biology.

[35]  Lothar Thiele,et al.  A Comparison of Selection Schemes used in Genetic Algorithms , 1995 .

[36]  Jun S. Liu,et al.  Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. , 1993, Science.

[37]  Pavel A. Pevzner,et al.  Combinatorial Approaches to Finding Subtle Signals in DNA Sequences , 2000, ISMB.

[38]  Amos Bairoch,et al.  PROSITE: A Documented Database Using Patterns and Profiles as Motif Descriptors , 2002, Briefings Bioinform..

[39]  Timothy Bailey.  Likelihood vs. Information in Aligning Biopolymer Sequences , 1993 .

[40]  Maria Jesus Martin,et al.  The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003 , 2003, Nucleic Acids Res..

[41]  Olivier Martin,et al.  Cooperative Metaheuristics for Exploring Proteomic Data , 2003, Artificial Intelligence Review.

[42]  Sriram Ramabhadran,et al.  Finding subtle motifs by branching from sample strings , 2003, ECCB.

[43]  Jeremy Buhler,et al.  Finding motifs using random projections , 2001, RECOMB.

[44]  Laurent Marsan.  Inférence de motifs structurés : algorithmes et outils appliqués à la détection de sites de fixation dans le séquences génomiques , 2002 .