Saarbrücken, Univ., Diplomarb., 2002.

Saarbrücken, Univ., Diss., 2001. / Erscheinungsjahr an der Haupttitelstelle: 2000. Computerdatei im Fernzugriff.

Paderborn, University, Diss., 2002.

Paderborn, University, Diss., 2003.

Berlin, Humboldt-University, Diss., 2004.

University, Diss., 2001--Saarbrücken. / Erscheinungsjahr an der Haupttitelstelle: 2000.

Univ., Diss.--Pisa, 2003.

Sequence alignment has become one of the most common tasks in bioinformatics. Most of the existing sequence alignment methods use general scoring schemes. But these alignments are sometimes not completely relevant because they do not necessarily provide the desired information. It would be extremely difficult, if not impossible, to include any possible objective into an algorithm. Our goal is to allow a working biologist to augment a given alignment with additional information based on their knowledge and objectives.<p></p>In this thesis, we will formally define constraints and compatible constraint sets for an alignment which require some positions of the sequences to be aligned together. Using this approach, one can align some specific segments such as domains within protein sequences by inputting constraints (the positions of the segments on the sequences), and the algorithm will automatically find an optimal alignment in which the segments are aligned together.<p></p>A necessary prerequisite of calculating an alignment is that the constraints inputted be compatible with each other, and we will develop algorithms to check this condition for both pairwise and multiple sequence alignments. The algorithms are based on a depth-first search on a graph that is converted from the constraints and the alignment. We then develop algorithms to perform pairwise and multiple sequence alignments satisfying these compatible constraints.<p></p>Using straightforward dynamic programming for pairwise sequence alignment satisfying a compatible constraint set, an optimal alignment corresponds to a path going through the dynamic programming matrix, and as we are only using single-position constraints, a constraint can be represented as a point on the matrix, so a compatible constraint set is a set of points. We try to determine a new path, rather than the original path, that achieves the highest score which goes through all the compatible constraint set points. The path is a concatenation of sub-paths, so that only the scores in the sub-matrices need to be calculated. This means the time required to get the new path decreases as the number of constraints increases, and it also varies as the positions of the points change. It can be further reduced by using the information from the original alignment, which can offer a significant speed gain.<p></p>We then use exact and progressive algorithms to find multiple sequence alignments satisfying a compatible constraint set, which are extensions of pairwise sequence alignments. With exact algorithms for three sequences, where constraints are represented as lines, we discuss a method to force the optimal path to cross the constraint lines. And with progressive algorithms, we use a set of pairwise alignments satisfying compatible constraints to construct multiple sequence alignments progressively. Because they are more complex, we leave some extensions as future work.

Sequence alignment has become one of the most common tasks in bioinformatics. Most of the existing sequence alignment methods use general scoring schemes. But these alignments are sometimes not completely relevant because they do not necessarily provide the desired information. It would be extremely difficult, if not impossible, to include any possible objective into an algorithm. Our goal is to allow a working biologist to augment a given alignment with additional information based on their knowledge and objectives.<p></p>In this thesis, we will formally define constraints and compatible constraint sets for an alignment which require some positions of the sequences to be aligned together. Using this approach, one can align some specific segments such as domains within protein sequences by inputting constraints (the positions of the segments on the sequences), and the algorithm will automatically find an optimal alignment in which the segments are aligned together.<p></p>A necessary prerequisite of calculating an alignment is that the constraints inputted be compatible with each other, and we will develop algorithms to check this condition for both pairwise and multiple sequence alignments. The algorithms are based on a depth-first search on a graph that is converted from the constraints and the alignment. We then develop algorithms to perform pairwise and multiple sequence alignments satisfying these compatible constraints.<p></p>Using straightforward dynamic programming for pairwise sequence alignment satisfying a compatible constraint set, an optimal alignment corresponds to a path going through the dynamic programming matrix, and as we are only using single-position constraints, a constraint can be represented as a point on the matrix, so a compatible constraint set is a set of points. We try to determine a new path, rather than the original path, that achieves the highest score which goes through all the compatible constraint set points. The path is a concatenation of sub-paths, so that only the scores in the sub-matrices need to be calculated. This means the time required to get the new path decreases as the number of constraints increases, and it also varies as the positions of the points change. It can be further reduced by using the information from the original alignment, which can offer a significant speed gain.<p></p>We then use exact and progressive algorithms to find multiple sequence alignments satisfying a compatible constraint set, which are extensions of pairwise sequence alignments. With exact algorithms for three sequences, where constraints are represented as lines, we discuss a method to force the optimal path to cross the constraint lines. And with progressive algorithms, we use a set of pairwise alignments satisfying compatible constraints to construct multiple sequence alignments progressively. Because they are more complex, we leave some extensions as future work.

The thesis examines issues of consistency maintenance in advanced database systems; primarily, multidatabase systems. A multidatabase system consists of a number of pre-existing local database systems. A local database system is unaware of its participation in the multidatabase system and, likewise, the multidatabase system has no knowledge of local transaction executions. Enforcing global constraints in such an environment is clearly a challenging task. A methodology for constraint enforcement is presented which utilises existing technology for the replication of data in an attempt to enforce global consistency. While it is shown to have limited applicability, it is nonetheless an interesting study and serves to qualify the limits of such a solution. An alternative method for global consistency maintenance, which relies on the existence of triggers at each of the participant local databases, is then discussed. This method is shown to be particularly suitable when the issue of local database autonomy is of concern. It is, however, only suited to systems where each of the local databases provides a capability for triggering external actions based on the occurrence of particular database events. As a result of this methodology, the requirement for identification of enforcement actions which access sites where the instigating transaction originated becomes apparent. Such enforcement actions can cause deadlock in certain circumstances when they are executed at the same site which initially triggered the global constraint. This issue is dealt with in a novel way by proposing a methodology for statically checking relations at each participant site with a view to determining their susceptibility to this form of deadlock. The method, a graphical representation of the constraint enforcement process in a distributed system, is also shown to have other desirable properties. Arising from the requirements of other work within the thesis, an algorithm for detecting all cycles in a given directed graph is presented. It is shown that, while the well-known adaptation of the Depth First Search algorithm to cycle detection in directed graphs can detect the existence of cycles, it cannot in all circumstances identify all cycles. An algorithm which performs this task is presented together with an analysis of its complexity and correctness. In a more general sense, the issue of deferred constraint enforcement is discussed. Several scenarios where deferred enforcement of constraints is required are presented, together with a method for detecting the presence of cyclic dependencies within a given database schema.