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# Building alignments

## Using the cogent aligners

### Running a pairwise Needleman-Wunsch-Alignment

    >>> from cogent.align.algorithm import nw_align
    >>> seq1 = 'AKSAMITNY'
    >>> seq2 = 'AKHSAMMIT'
    >>> print nw_align(seq1,seq2)
    ('AK-SAM-ITNY', 'AKHSAMMIT--')

### Running a progressive aligner

We import useful functions and then load the sequences to be aligned.

    >>> from cogent import LoadSeqs, LoadTree, DNA
    >>> seqs = LoadSeqs('data/test2.fasta', aligned=False, moltype=DNA)

#### For nucleotides

We load a canned nucleotide substitution model and the progressive aligner `TreeAlign` function.

    >>> from cogent.evolve.models import HKY85
    >>> from cogent.align.progressive import TreeAlign

We first align without providing a guide tree. The `TreeAlign` algorithm builds pairwise alignments and estimates the substitution model parameters and pairwise distances. The distances are used to build a neighbour joining tree and the median value of substitution model parameters are provided to the substitution model for the progressive alignment step.

    >>> aln, tree = TreeAlign(HKY85(), seqs)
    Param Estimate Summary Stats: kappa
    ==============================
            Statistic        Value
    ------------------------------
                Count           10
                  Sum        1e+06
               Median        4.256
                 Mean        1e+05
    StandardDeviation    3.162e+05
             Variance        1e+11
    ------------------------------
    >>> aln
    5 x 60 text alignment: NineBande[-C-----GCCA...], Mouse[GCAGTGAGCCA...], DogFaced[GCAAGGAGCCA...], ...

We then align using a guide tree (pre-estimated) and specifying the ratio of transitions to transversions (kappa).

    >>> tree = LoadTree(treestring='(((NineBande:0.0128202449453,Mouse:0.184732725695):0.0289459522137,DogFaced:0.0456427810916):0.0271363715538,Human:0.0341320714654,HowlerMon:0.0188456837006)root;')
    >>> params={'kappa': 4.0}
    >>> aln, tree = TreeAlign(HKY85(), seqs, tree=tree, param_vals=params)
    >>> aln
    5 x 60 text alignment: NineBande[-C-----GCCA...], Mouse[GCAGTGAGCCA...], DogFaced[GCAAGGAGCCA...], ...

#### For codons

We load a canned codon substitution model and use a pre-defined tree and parameter estimates.

    >>> from cogent.evolve.models import MG94HKY
    >>> tree = LoadTree(treestring='((NineBande:0.0575781680031,Mouse:0.594704139406):0.078919659556,DogFaced:0.142151930069,(HowlerMon:0.0619991555435,Human:0.10343006422):0.0792423439112)')
    >>> params={'kappa': 4.0, 'omega': 1.3}
    >>> aln, tree = TreeAlign(MG94HKY(), seqs, tree=tree, param_vals=params)
    >>> aln
    5 x 60 text alignment: NineBande[------CGCCA...], Mouse[GCAGTGAGCCA...], DogFaced[GCAAGGAGCCA...], ...

## Building alignments with 3rd-party apps such as muscle or clustalw

See Using alignment application controllers to align unaligned sequences.

## Converting gaps from aa-seq alignment to nuc seq alignment

We load some unaligned DNA sequences and show their translation.

    >>> from cogent import LoadSeqs, DNA, PROTEIN
    >>> seqs = [('hum', 'AAGCAGATCCAGGAAAGCAGCGAGAATGGCAGCCTGGCCGCGCGCCAGGAGAGGCAGGCCCAGGTCAACCTCACT'),
    ...         ('mus', 'AAGCAGATCCAGGAGAGCGGCGAGAGCGGCAGCCTGGCCGCGCGGCAGGAGAGGCAGGCCCAAGTCAACCTCACG'),
    ...         ('rat', 'CTGAACAAGCAGCCACTTTCAAACAAGAAA')]
    >>> unaligned_DNA = LoadSeqs(data=seqs, moltype = DNA, aligned = False)
    >>> print unaligned_DNA.toFasta()
    >hum
    AAGCAGATCCAGGAAAGCAGCGAGAATGGCAGCCTGGCCGCGCGCCAGGAGAGGCAGGCCCAGGTCAACCTCACT
    >mus
    AAGCAGATCCAGGAGAGCGGCGAGAGCGGCAGCCTGGCCGCGCGGCAGGAGAGGCAGGCCCAAGTCAACCTCACG
    >rat
    CTGAACAAGCAGCCACTTTCAAACAAGAAA
    >>> print unaligned_DNA.getTranslation()
    >hum
    KQIQESSENGSLAARQERQAQVNLT
    >mus
    KQIQESGESGSLAARQERQAQVNLT
    >rat
    LNKQPLSNKK

We load an alignment of these protein sequences.

    >>> aligned_aa_seqs = [('hum', 'KQIQESSENGSLAARQERQAQVNLT'),
    ...                    ('mus', 'KQIQESGESGSLAARQERQAQVNLT'),
    ...                    ('rat', 'LNKQ------PLS---------NKK')]
    >>> aligned_aa = LoadSeqs(data = aligned_aa_seqs, moltype = PROTEIN)

We then obtain an alignment of the DNA sequences from the alignment of their translation.

    >>> aligned_DNA = aligned_aa.replaceSeqs(unaligned_DNA)
    >>> print aligned_DNA
    >hum
    AAGCAGATCCAGGAAAGCAGCGAGAATGGCAGCCTGGCCGCGCGCCAGGAGAGGCAGGCCCAGGTCAACCTCACT
    >mus
    AAGCAGATCCAGGAGAGCGGCGAGAGCGGCAGCCTGGCCGCGCGGCAGGAGAGGCAGGCCCAAGTCAACCTCACG
    >rat
    CTGAACAAGCAG------------------CCACTTTCA---------------------------AACAAGAAA

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