skbio.alignment.global_pairwise_align¶

skbio.alignment.global_pairwise_align(seq1, seq2, gap_open_penalty, gap_extend_penalty, substitution_matrix, penalize_terminal_gaps=False)[source]¶

Globally align a pair of seqs or alignments with Needleman-Wunsch

State: Experimental as of 0.4.0.

Parameters:

Parameters:	seq1 : IUPACSequence or TabularMSA The first unaligned sequence(s). seq2 : IUPACSequence or TabularMSA The second unaligned sequence(s). gap_open_penalty : int or float Penalty for opening a gap (this is substracted from previous best alignment score, so is typically positive). gap_extend_penalty : int or float Penalty for extending a gap (this is substracted from previous best alignment score, so is typically positive). substitution_matrix: 2D dict (or similar) Lookup for substitution scores (these values are added to the previous best alignment score). penalize_terminal_gaps: bool, optional If True, will continue to penalize gaps even after one sequence has been aligned through its end. This behavior is true Needleman-Wunsch alignment, but results in (biologically irrelevant) artifacts when the sequences being aligned are of different length. This is `False` by default, which is very likely to be the behavior you want in all or nearly all cases.
Returns:	tuple `TabularMSA` object containing the aligned sequences, alignment score (float), and start/end positions of each input sequence (iterable of two-item tuples). Note that start/end positions are indexes into the unaligned sequences.

seq1 : IUPACSequence or TabularMSA

The first unaligned sequence(s).

seq2 : IUPACSequence or TabularMSA

The second unaligned sequence(s).

gap_open_penalty : int or float

Penalty for opening a gap (this is substracted from previous best alignment score, so is typically positive).

gap_extend_penalty : int or float

Penalty for extending a gap (this is substracted from previous best alignment score, so is typically positive).

substitution_matrix: 2D dict (or similar)

Lookup for substitution scores (these values are added to the previous best alignment score).

penalize_terminal_gaps: bool, optional

If True, will continue to penalize gaps even after one sequence has been aligned through its end. This behavior is true Needleman-Wunsch alignment, but results in (biologically irrelevant) artifacts when the sequences being aligned are of different length. This is False by default, which is very likely to be the behavior you want in all or nearly all cases.

Returns:

tuple

TabularMSA object containing the aligned sequences, alignment score (float), and start/end positions of each input sequence (iterable of two-item tuples). Note that start/end positions are indexes into the unaligned sequences.