This documentation is for version 2.0.dev, which is not released yet.
Basic class for multiple sequence alignment analyses.
Parameters : | seqs : list
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Notes
Depending on the structure of the sequences, further keywords can be specified that manage how the items get tokenized.
Methods
get_pairwise_alignments([new_calc, model, ...]) | Function creates a dictionary of all pairwise alignments scores. |
get_peaks([gap_weight]) | Calculate the profile score for each column of the alignment. |
get_pid([mode]) | Return the Percentage Identity (PID) score of the calculated MSA. |
iterate_all_sequences([check, mode, gop, ...]) | Iterative refinement based on a complete realignment of all sequences. |
iterate_clusters(threshold[, check, mode, ...]) | Iterative refinement based on a flat cluster analysis of the data. |
iterate_orphans([check, mode, gop, scale, ...]) | Iterate over the most divergent sequences in the sample. |
iterate_similar_gap_sites([check, mode, ...]) | Iterative refinement based on the Similar Gap Sites heuristic. |
lib_align([model, mode, modes, scale, ...]) | Carry out a library-based progressive alignment analysis of the sequences. |
prog_align([model, mode, gop, scale, ...]) | Carry out a progressive alignment analysis of the input sequences. |
sum_of_pairs([alm_matrix, mat, gap_weight, gop]) | Calculate the sum-of-pairs score for a given alignment analysis. |
swap_check([swap_penalty, score_mode]) | Check for possibly swapped sites in the alignment. |