The alignment of nucleotide, RNA or amino acid sequences is one of the most common tasks in Bioinformatics. The motivation for doing alignments is that high sequence similarity usually implies significant functional or structural similarity. The simultaneous alignment of three or more sequences is an essential precondition to study phylogeny or predict structure. SeqAn provides two kinds of alignment data structures, a matrix-like alignment data structure and a graph-based alignment representation.

Although there is a multitude of different alignment algorithms the basic syntax for performing an alignment is always the same.

TSequences, TScore, and TAlgorithm are just placeholders explained below. An actual call could look like this:

In this case the sequences are stored in alignment_ds. The chosen Gotoh alignment algorithm uses affine gap costs. It is configured with a scoring scheme, where matches are scored +1, mismatches -1, gap-openings -2 and a gap-extensions -1. The globalAlignment call returns the score of the alignment and stores the actual alignment in alignment_ds, which could be an Alignment Graph or an Align data structure. If it is an alignment graph, its textual representation in PipMaker format is illustrated in the following figure.

If one is only interested in the score of an alignment, a StringSet that holds the sequences is sufficient.

In summary, TSequences must be either an Align or an Alignment Graph containing the sequences, or a simple StringSet. Note that there are also other possibilities, e.g., to retrieve just the matches of an alignment or to print the traceback in the console.

TScore is a scoring object. The most common scoring objects are illustrated below in conjunction with different algorithms.

Note that certain algorithms require different score types, e.g., for an affine scoring scheme Gotoh and Hirschberg work well but Needleman-Wunsch fails because it uses linear gap penalties.

By now you have seen the most common possibilities for TScore and TAlgorithm. For local alignments TAlgorithm can be substituted with SmithWaterman(), which implements the well-known Smith-Waterman algorithm.

You can find more example codes about computing global and local alignments here: Global Alignments, Local Alignments.

In some situations more sophisticated alignment algorithms are necessary. For example, in assembly projects one is usually not interested in ordinary global alignments but so-called overlap alignments. These require a special initialization of the dynamic programming matrix. Also semi-global alignments that try to fit one sequence into the other require adapted dynamic programming matrices. That's why, graph-based alignment algorithms can be configured with an AlignConfig object that has 4 boolean parameters.

The parameters indicate whether the first row and / or column is initialized with 0's and whether the maximum is searched in the last row and / or column. So in total there are 2 to the power of 4 = 16 different configurations. The following code snippet initializes the first row with 0's and looks for the maximum in the last row (classical sequence fitting).

Multiple sequence alignments are also part of SeqAn. A segmental version of the classical T-Coffee algorithm is available in the application folder of SeqAn.