seq.alg – Algorithms for Sequences
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MergePairwiseAlignments(pairwise_alns, ref_seq)
Merge a list of pairwise alignments into a multiple sequence alignments. This
function uses the reference sequence as the anchor and inserts gaps where
needed. This is also known as the star method.
The resulting multiple sequence alignment provides a simple way to map between
residues of pairwise alignments, e.g. to compare distances in two structural
templates.
There are a few things to keep in mind when using this function:
- The reference sequence mustn’t contain any gaps
- The first sequence of each pairwise alignments corresponds to the reference
sequence. Apart from the presence of gaps, these two sequences must be
completely identical.
- If the reference sequence has an offset, the first sequence of each pairwise alignment
must have the same offset. This offset is inherited by the first sequence of the final
output alignment.
- The resulting multiple sequence alignment is by no means optimal. For
better results, consider using a multiple-sequence alignment program such
as MUSCLE or ClustalW.
- Residues in columns where the reference sequence has gaps should not be
considered as aligned. There is no information in the pairwise alignment to
guide the merging, the result is undefined.
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ValidateSEQRESAlignment(aln, chain=None)
Checks a sequence aligned to a SEQRES sequence to be free of strand breaks.
Residues divided by gaps are not considered as breakage but may also not be
connected.
Parameters: |
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Returns: | True if all residues (beside gaped ones) are connected, False
otherwise.
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AlignToSEQRES(chain, seqres, try_resnum_first=False, validate=True)
Aligns the residues of chain to the SEQRES sequence, inserting gaps where
needed. The function uses the connectivity of the protein backbone to find
consecutive peptide fragments. These fragments are then aligned to the SEQRES
sequence.
All the non-ligand, peptide-linking residues of the chain must be listed in
SEQRES. If there are any additional residues in the chain, the function
raises a ValueError.
If ‘try_resnum_first’ is set, building the alignment following residue numbers
is tried first.
If ‘validate’ is set (default), the alignment is checked using
ValidateSEQRESAlignment().
Parameters: |
- chain (ChainHandle) – Source of the sequence
- seqres (str) – SEQRES sequence
- try_resnum_first (bool) – Try to align by residue number
- validate (bool) – Validate alignment by
ValidateSEQRESAlignment()
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Returns: | The alignment of the residues in the chain and the SEQRES entries.
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Return type: | AlignmentHandle
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AlignmentFromChainView(chain, handle_seq_name='handle', view_seq_name='view')
Creates and returns the sequence alignment of the given chain view to the
chain handle. The alignment contains two sequences, the first containing all
non-ligand peptide-linking residues, the second containing all non-ligand
peptide-linking residues that are part of the view.
Parameters: |
- chain (ChainView) – A valid chain
- handle_seq_name – Name of the handle sequence in the output alignment
- view_seq_name – Name of the view sequence in the output alignment
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Returns: | The alignment
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Return type: | AlignmentHandle
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Conservation(aln, assign=true, prop_name="cons", ignore_gap=false)
Calculates conservation scores for each column in the alignment, according to
the ConSurf method (Armon et al., J. Mol. Biol. (2001) 307, 447-463).
The conservation score is a value between 0 and 1. The bigger the number
the more conserved the aligned residues are.
Parameters: |
- aln (AlignmentHandle) – An alignment handle
- assign – If true, the conservation scores are assigned to attached
residues. The name of the property can be changed with the prop_name
parameter. Useful when coloring entities based on sequence conservation.
- prop_name – The property name for assigning the conservation to
attached residues. Defaults to ‘cons’.
- ignore_gap – If true, the dissimilarity between two gaps is increased to
6.0 instead of 0.5 as defined in the original version. Without this, a
stretch where in the alignment there is only one sequence which is
aligned to only gaps, is considered highly conserved (depending on the
number of gap sequences).
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LocalAlign(seq1, seq2, subst_weight, gap_open=-5, gap_ext=-2)
Performs a Smith/Waterman local alignment of seq1 and seq2 and returns
the best-scoring alignments as a list of pairwise alignments.
Example:
seq_a=seq.CreateSequence('A', 'acdefghiklmn')
seq_b=seq.CreateSequence('B', 'acdhiklmn')
alns=seq.alg.LocalAlign(seq_a, seq_b, seq.alg.BLOSUM62)
print alns[0].ToString(80)
# >>> A acdefghiklmn
# >>> B acd---hiklmn
Parameters: |
- seq1 (ConstSequenceHandle) – A valid sequence
- seq2 (ConstSequenceHandle) – A valid sequence
- subst_weigth – The substitution weights matrix
- gap_open – The gap opening penalty. Must be a negative number
- gap_ext – The gap extension penalty. Must be a negative number
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Returns: | list of best-scoring, non-overlapping alignments of seq1 and
seq2. Since alignments always start with a replacement, the start is
stored in the sequence offset of the two sequences.
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GlobalAlign(seq1, seq2, subst_weight, gap_open=-5, gap_ext=-2)
Performs a Needleman/Wunsch global alignment of seq1 and seq2 and returns
the best-scoring alignment.
Example:
seq_a=seq.CreateSequence('A', 'acdefghiklmn')
seq_b=seq.CreateSequence('B', 'acdhiklmn')
alns=seq.alg.GlobalAlign(seq_a, seq_b, seq.alg.BLOSUM62)
print alns[0].ToString(80)
# >>> A acdefghiklmn
# >>> B acd---hiklmn
Parameters: |
- seq1 (ConstSequenceHandle) – A valid sequence
- seq2 (ConstSequenceHandle) – A valid sequence
- subst_weigth – The substitution weights matrix
- gap_open – The gap opening penalty. Must be a negative number
- gap_ext – The gap extension penalty. Must be a negative number
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Returns: | best-scoring alignment of seq1 and seq2.
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