Sequence Alignment
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John Kececioglu  One of the best experts on this subject based on the ideXlab platform.

inverse Sequence Alignment from partial examples
Workshop on Algorithms in Bioinformatics, 2007CoAuthors: John KececiogluAbstract:When aligning biological Sequences, the choice of parameter values for the Alignment scoring function is critical. Small changes in gap penalties, for example, can yield radically different Alignments. A rigorous way to compute parameter values that are appropriate for biological Sequences is inverse parametric Sequence Alignment. Given a collection of examples of biologically correct Alignments, this is the problem of finding parameter values that make the example Alignments score close to optimal. We extend prior work on inverse Alignment to partial examples and to an improved model based on minimizing the average error of the examples. Experiments on benchmark biological Alignments show we can find parameters that generalize across protein families and that boost the recovery rate for multiple Sequence Alignment by up to 25%.

WABI  Inverse Sequence Alignment from partial examples
Lecture Notes in Computer Science, 2007CoAuthors: John KececiogluAbstract:When aligning biological Sequences, the choice of parameter values for the Alignment scoring function is critical. Small changes in gap penalties, for example, can yield radically different Alignments. A rigorous way to compute parameter values that are appropriate for biological Sequences is inverse parametric Sequence Alignment. Given a collection of examples of biologically correct Alignments, this is the problem of finding parameter values that make the example Alignments score close to optimal. We extend prior work on inverse Alignment to partial examples and to an improved model based on minimizing the average error of the examples. Experiments on benchmark biological Alignments show we can find parameters that generalize across protein families and that boost the recovery rate for multiple Sequence Alignment by up to 25%.

A polyhedral approach to Sequence Alignment problems
Discrete Applied Mathematics, 2000CoAuthors: John Kececioglu, Knut Reinert, Hanspeter Lenhof, Kurt Mehlhorn, Petra Mutzel, Martin VingronAbstract:Abstract We study two new problems in Sequence Alignment both from a practical and a theoretical view, using tools from combinatorial optimization to develop branchandcut algorithms. The generalized maximum trace formulation captures several forms of multiple Sequence Alignment problems in a common framework, among them the original formulation of maximum trace. The RNA Sequence Alignment problem captures the comparison of RNA molecules on the basis of their primary Sequence and their secondary structure. Both problems have a characterization in terms of graphs which we reformulate in terms of integer linear programming. We then study the polytopes (or convex hulls of all feasible solutions) associated with the integer linear program for both problems. For each polytope we derive several classes of facetdefining inequalities and show that for some of these classes the corresponding separation problem can be solved in polynomial time. This leads to a polynomialtime algorithm for pairwise Sequence Alignment that is not based on dynamic programming. Moreover, for multiple Sequences the branchandcut algorithms for both Sequence Alignment problems are able to solve to optimality instances that are beyond the range of present dynamic programming approaches.
David W. Mount  One of the best experts on this subject based on the ideXlab platform.

Using multiple Sequence Alignment editors and formatters.
Cold Spring Harbor protocols, 2009CoAuthors: David W. MountAbstract:Sequence Alignment editors enable the user to manually edit a multiple Sequence Alignment (msa) in order to obtain a more reasonable or expected Alignment. Editors allow Sequences to be reordered and/or modified using the computer's cut and paste commands. They are designed to accept various msa formats and to provide the output file in a suitable userdesignated format. Sequence formatters provide various output formatting options, such as color and shading schemes to enhance visualization of residue Alignments. The formatters can output files in Postscript, EPS, RTF, and other widely recognized formats, while accepting the standard input formats, such as MSF, ALN, and FASTA. This article introduces a number of Sequence Alignment editors and formatters, and provides links to sites where they can be found.
David Fernándezbaca  One of the best experts on this subject based on the ideXlab platform.

COCOON  Inverse Parametric Sequence Alignment
Lecture Notes in Computer Science, 2002CoAuthors: Fangting Sun, David FernándezbacaAbstract:We consider the inverse parametric Sequence Alignment problem, where a Sequence Alignment is given and the task is to determine parameter values such that the given Alignment is optimal at that parameter setting. We describe a O(mnlog n)time algorithm for inverse global Alignment without gap penalty and a O(mn logm) time algorithm for global Alignment with gap penalty, where m, n (n = m) are the lengths of input strings. We then discuss algorithms for local Alignment.
Jaap Heringa  One of the best experts on this subject based on the ideXlab platform.

Contact‐based Sequence Alignment
Nucleic acids research, 2004CoAuthors: Jens Kleinjung, John W. Romein, Kuang Lin, Jaap HeringaAbstract:This paper introduces the novel method of contactbased protein Sequence Alignment, where structural information in the form of contact mutation probabilities is incorporated into an Alignment routine using contactmutation matrices (CAO: Contact Accepted mutatiOn). The contactbased Alignment routine optimizes the score of matched contacts, which involves four (two per contact) instead of two residues per match in pairwise Alignments. The first contact refers to a real sidechain contact in a template Sequence with known structure, and the second contact is the equivalent putative contact of a homologous query Sequence with unknown structure. An algorithm has been devised to perform a pairwise Sequence Alignment based on contact information. The contact scores were combined with PAMtype (Point Accepted Mutation) substitution scores after parameterization of gap penalties and score weights by means of a genetic algorithm. We show that owing to the structural information contained in the CAO matrices, significantly improved Alignments of distantly related Sequences can be obtained. This has allowed us to annotate eight putative Drosophila IGF Sequences. Contactbased Sequence Alignment should therefore prove useful in comparative modelling and fold recognition.

An overview of multiple Sequence Alignment.
Current protocols in bioinformatics, 2003CoAuthors: Victor Simossis, Jens Kleinjung, Jaap HeringaAbstract:Multiple Sequence Alignment is perhaps the most commonly applied bioinformatics technique. It often leads to fundamental biological insight into Sequencestructurefunction relationships of nucleotide or protein Sequence families. In this unit, an overview of multiple Sequence Alignment techniques is presented, covering a history of nearly 30 years from the early pioneering methods to the current stateoftheart techniques. Methodological and biological issues and enduser considerations, as well as Alignment evaluation issues, are discussed.
Erik Pitzer  One of the best experts on this subject based on the ideXlab platform.

EUROCAST  Parallel progressive multiple Sequence Alignment
Lecture Notes in Computer Science, 2005CoAuthors: Erik PitzerAbstract:Multiple Sequence Alignment is an essential tool in the analysis and comparison of biological Sequences. Unfortunately, the complexity of this problem is exponential. Currently feasible methods are, therefore, only approximations. The progressive multiple Sequence Alignment algorithms are the most widespread among these approximations. Still, the computation speed of typical problems is often not satisfactory. Hence, the well known progressive Alignment scheme of ClustalW has been subject to parallelization to further accelerate the computation. In the course of this action a unique scheme to parallelize Sequence Alignment in particular and dynamic programming in general was discovered, which yields an average of n / 2 parallel calculations for problem size n. The scalability of O(n) tasks for problem size n can be even maintained for slower networks.