skbio.alignment.global_pairwise_align_nucleotide#

skbio.alignment.global_pairwise_align_nucleotide(seq1, seq2, gap_open_penalty=5, gap_extend_penalty=2, match_score=1, mismatch_score=-2, substitution_matrix=None, penalize_terminal_gaps=False)[source]#

Globally align nucleotide seqs or alignments with Needleman-Wunsch.

Parameters:

seq1DNA, RNA, or TabularMSA[DNA|RNA]: The first unaligned sequence(s).
seq2DNA, RNA, or TabularMSA[DNA|RNA]: The second unaligned sequence(s).
gap_open_penaltyint or float, optional: Penalty for opening a gap (this is substracted from previous best alignment score, so is typically positive).
gap_extend_penaltyint or float, optional: Penalty for extending a gap (this is substracted from previous best alignment score, so is typically positive).
match_scoreint or float, optional: The score to add for a match between a pair of bases (this is added to the previous best alignment score, so is typically positive).
mismatch_scoreint or float, optional: The score to add for a mismatch between a pair of bases (this is added to the previous best alignment score, so is typically negative).
substitution_matrix: 2D dict (or similar): Lookup for substitution scores (these values are added to the previous best alignment score). If provided, this overrides match_score and mismatch_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.