Multiple sequence alignments are an essential tool for protein structure and function prediction, phylogeny inference and other common tasks in sequence analysis. Recently developed systems have advanced the state of the art with respect to accuracy, ability to scale to thousands of proteins and flexibility in comparing proteins that do not share the same domain architecture. New multiple align...

As more data from DNA and protein sequences is discovered, sequence alignment programs are becoming increasingly important for analyzing this data. These alignments can help us learn more about the functions of certain genes and proteins and these observations could ultimately lead to the discovery of cures for certain genetic diseases or even a better insight into the evolutionary process. The...

The basic algorithms for alignment of two or more protein sequences are explained. Alternative methods for scoring substitutions and gaps (insertions and deletions) are described, as are global and local alignment methods. Multiple alignment techniques are explained, including methods for profile comparison. A summary is given of programs for the alignment and analysis of protein sequences, eit...

Given the explosive growth of biological sequence databases and the computational complexity of aligning large sequences over extremely large databases most researchers have opted for utilizing the BLAST algorithm. While BLAST is completely appropriate for some purposes, the more rigorous and more computationally expensive Smith-Waterman algorithm is preferred for certain purposes. This work pr...

Multiple sequence alignments are essential in computational analysis of protein sequences and structures, with applications in structure modeling, functional site prediction, phylogenetic analysis and sequence database searching. Constructing accurate multiple alignments for divergent protein sequences remains a difficult computational task, and alignment speed becomes an issue for large sequen...

In a recently proposed contextual alignment model, efficient algorithms exist for global and local pairwise alignment of protein sequences. Preliminary results obtained for biological data are very promising. Our main motivation was to adopt the idea of context dependency to the multiple-alignment setting. To this aim the relaxation of the model was developed (we call this new model averaged co...

We describe an new algorithm for visualizing an alignment of biological sequences according to a probabilistic model of evolution. The resulting data array is readily interpreted by the human eye and amenable to digital image techniques. We present examples using mRNA sequences from mouse and rat: three cytochromes and two zinc finger proteins. The underlying evolutionary model is derived from ...

Sequence alignment is an important tool for describing relationships between sequences. Many sequence alignment algorithms exist, differing in efficiency, and in their models of the sequences and of the relationship between sequences. The focus of this thesis is on algorithms for the optimal alignment of two or three sequences of biological data, particularly DNA sequences. The algorithms are d...