skbio.alignment.TabularMSA#

class skbio.alignment.TabularMSA(sequences, metadata=None, positional_metadata=None, minter=None, index=None)[source]#

Store a multiple sequence alignment in tabular (row/column) form.

Parameters:

sequencesiterable of GrammaredSequence, TabularMSA: Aligned sequences in the MSA. Sequences must all be the same type and length. For example, sequences could be an iterable of DNA, RNA, or Protein sequences. If sequences is a TabularMSA, its metadata, positional_metadata, and index will be used unless overridden by parameters metadata, positional_metadata, and minter/index, respectively.
metadatadict, optional: Arbitrary metadata which applies to the entire MSA. A shallow copy of the dict will be made.
positional_metadatapd.DataFrame consumable, optional: Arbitrary metadata which applies to each position in the MSA. Must be able to be passed directly to pd.DataFrame constructor. Each column of metadata must be the same length as the number of positions in the MSA. A shallow copy of the positional metadata will be made.
mintercallable or metadata key, optional: If provided, defines an index label for each sequence in sequences. Can either be a callable accepting a single argument (each sequence) or a key into each sequence’s metadata attribute. Note that minter cannot be combined with index.
indexpd.Index consumable, optional: Index containing labels for sequences. Must be the same length as sequences. Must be able to be passed directly to pd.Index constructor. Note that index cannot be combined with minter and the contents of index must be hashable.

Raises:

ValueError: If minter and index are both provided.
ValueError: If index is not the same length as sequences.
TypeError: If sequences contains an object that isn’t a GrammaredSequence.
TypeError: If sequences does not contain exactly the same type of GrammaredSequence objects.
ValueError: If sequences does not contain GrammaredSequence objects of the same length.

See also

skbio.sequence.DNA
skbio.sequence.RNA
skbio.sequence.Protein
pandas.DataFrame
pandas.Index
reassign_index

Notes

If neither minter nor index are provided, default index labels will be used: pd.RangeIndex(start=0, stop=len(sequences), step=1).

Examples

Create a TabularMSA object with three DNA sequences and four positions:

>>> from skbio import DNA, TabularMSA
>>> seqs = [
...     DNA('ACGT'),
...     DNA('AG-T'),
...     DNA('-C-T')
... ]
>>> msa = TabularMSA(seqs)
>>> msa
TabularMSA[DNA]
---------------------
Stats:
    sequence count: 3
    position count: 4
---------------------
ACGT
AG-T
-C-T

Since minter or index wasn’t provided, the MSA has default index labels:

>>> msa.index
RangeIndex(start=0, stop=3, step=1)

Create an MSA with metadata, positional metadata, and non-default index labels:

>>> msa = TabularMSA(seqs, index=['seq1', 'seq2', 'seq3'],
...                  metadata={'id': 'msa-id'},
...                  positional_metadata={'prob': [3, 4, 2, 2]})
>>> msa
TabularMSA[DNA]
--------------------------
Metadata:
    'id': 'msa-id'
Positional metadata:
    'prob': <dtype: int64>
Stats:
    sequence count: 3
    position count: 4
--------------------------
ACGT
AG-T
-C-T
>>> msa.index
Index(['seq1', 'seq2', 'seq3'], dtype='object')

Attributes

`default_write_format`
`dtype`	Data type of the stored sequences.
`iloc`	Slice the MSA on either axis by index position.
`index`	Index containing labels along the sequence axis.
`loc`	Slice the MSA on first axis by index label, second axis by position.
`metadata`	`dict` containing metadata which applies to the entire object.
`positional_metadata`	`pd.DataFrame` containing metadata along an axis.
`shape`	Number of sequences (rows) and positions (columns).

Built-ins

`__bool__`()	Boolean indicating whether the MSA is empty or not.
`__contains__`(label)	Determine if an index label is in this MSA.
`__copy__`()	Return a shallow copy of this MSA.
`__deepcopy__`(memo)	Return a deep copy of this MSA.
`__eq__`(other)	Determine if this MSA is equal to another.
`__ge__`(value, /)	Return self>=value.
`__getitem__`(indexable)	Slice the MSA on either axis.
`__getstate__`(/)	Helper for pickle.
`__gt__`(value, /)	Return self>value.
`__iter__`()	Iterate over sequences in the MSA.
`__le__`(value, /)	Return self<=value.
`__len__`()	Return number of sequences in the MSA.
`__lt__`(value, /)	Return self<value.
`__ne__`(other)	Determine if this MSA is not equal to another.
`__reversed__`()	Iterate in reverse order over sequences in the MSA.
`__str__`()	Return string summary of this MSA.

Methods

`append`(sequence[, minter, index, reset_index])	Append a sequence to the MSA without recomputing alignment.
`consensus`()	Compute the majority consensus sequence for this MSA.
`conservation`([metric, degenerate_mode, gap_mode])	Apply metric to compute conservation for all alignment positions.
`extend`(sequences[, minter, index, reset_index])	Extend this MSA with sequences without recomputing alignment.
`from_dict`(dictionary)	Create a `TabularMSA` from a `dict`.
`gap_frequencies`([axis, relative])	Compute frequency of gap characters across an axis.
`has_metadata`()	Determine if the object has metadata.
`has_positional_metadata`()	Determine if the object has positional metadata.
`iter_positions`([reverse, ignore_metadata])	Iterate over positions (columns) in the MSA.
`join`(other[, how])	Join this MSA with another by sequence (horizontally).
`read`(file[, format])	Create a new `TabularMSA` instance from a file.
`reassign_index`([mapping, minter])	Reassign index labels to sequences in this MSA.
`sort`([level, ascending])	Sort sequences by index label in-place.
`to_dict`()	Create a `dict` from this `TabularMSA`.
`write`(file[, format])	Write an instance of `TabularMSA` to a file.