Alignment#

class Alignment(*args, **kwargs)#

An annotatable alignment class

Attributes:

annotation_db
named_seqs
num_seqs: Returns the number of sequences in the alignment.
positions: Iterates over positions in the alignment, in order.
seqs

Methods

`add_feature`(*, biotype, name, spans[, ...])	add feature on named sequence, or on the alignment itself
`add_from_ref_aln`(ref_aln[, before_name, ...])	Insert sequence(s) to self based on their alignment to a reference sequence.
`add_seqs`(other[, before_name, after_name])	Returns new object of class self with sequences from other added.
`alignment_quality`([app_name])	Computes the alignment quality using the indicated app
`annotate_from_gff`(f[, seq_ids])	copies annotations from a gff file to a sequence in self
`apply_pssm`([pssm, path, background, ...])	scores sequences using the specified pssm
`coevolution`([method, segments, drawable, ...])	performs pairwise coevolution measurement
`copy`()	Returns deep copy of self.
`copy_annotations`(seq_db)	copy annotations into attached annotation db
`count_gaps_per_pos`([include_ambiguity])	return counts of gaps per position as a DictArray
`count_gaps_per_seq`([induced_by, unique, ...])	return counts of gaps per sequence as a DictArray
`counts`([motif_length, include_ambiguity, ...])	counts of motifs
`counts_per_pos`([motif_length, ...])	return DictArray of counts per position
`counts_per_seq`([motif_length, ...])	counts of non-overlapping motifs per sequence
`deepcopy`([sliced])	returns deep copy of self.
`degap`(**kwargs)	Returns copy in which sequences have no gaps.
`distance_matrix`([calc, show_progress, ...])	Returns pairwise distances between sequences.
`dotplot`([name1, name2, window, threshold, ...])	make a dotplot between specified sequences.
`entropy_per_pos`([motif_length, ...])	returns shannon entropy per position
`entropy_per_seq`([motif_length, ...])	returns the Shannon entropy per sequence
`filtered`(predicate[, motif_length, ...])	The alignment positions where predicate(column) is true.
`get_ambiguous_positions`()	Returns dict of seq:{position:char} for ambiguous chars.
`get_degapped_relative_to`(name)	Remove all columns with gaps in sequence with given name.
`get_drawable`(*[, biotype, width, vertical])	make a figure from sequence features
`get_drawables`(*[, biotype])	returns a dict of drawables, keyed by type
`get_features`(*[, seqid, biotype, name, ...])	yields Feature instances
`get_gap_array`([include_ambiguity])	returns bool array with gap state True, False otherwise
`get_gapped_seq`(seq_name[, recode_gaps])	Return a gapped Sequence object for the specified seqname.
`get_identical_sets`([mask_degen])	returns sets of names for sequences that are identical
`get_lengths`([include_ambiguity, allow_gap])	returns {name: seq length, ...}
`get_motif_probs`([alphabet, ...])	Return a dictionary of motif probs, calculated as the averaged frequency across sequences.
`get_position_indices`(f[, native, negate])	Returns list of column indices for which f(col) is True.
`get_projected_feature`(*, seqid, feature)	returns an alignment feature projected onto the seqid sequence
`get_projected_features`(, seqid, *kwargs)	projects all features from other sequences onto seqid
`get_seq`(seqname)	Return a ungapped Sequence object for the specified seqname.
`get_seq_indices`(f[, negate])	Returns list of keys of seqs where f(row) is True.
`get_similar`(target[, min_similarity, ...])	Returns new Alignment containing sequences similar to target.
`get_translation`([gc, incomplete_ok, ...])	translate from nucleic acid to protein
`has_terminal_stop`([gc, strict])	Returns True if any sequence has a terminal stop codon.
`information_plot`([width, height, window, ...])	plot information per position
`is_ragged`()	Returns True if alignment has sequences of different lengths.
`iter_positions`([pos_order])	Iterates over positions in the alignment, in order.
`iter_selected`([seq_order, pos_order])	Iterates over elements in the alignment.
`iter_seqs`([seq_order])	Iterates over values (sequences) in the alignment, in order.
`iupac_consensus`([alphabet, allow_gap])	Returns string containing IUPAC consensus sequence of the alignment.
`majority_consensus`()	Returns list containing most frequent item at each position.
`make_feature`(*, feature[, on_alignment])	create a feature on named sequence, or on the alignment itself
`matching_ref`(ref_name, gap_fraction, gap_run)	Returns new alignment with seqs well aligned with a reference.
`no_degenerates`([motif_length, allow_gap])	returns new alignment without degenerate characters
`omit_bad_seqs`([quantile])	Returns new alignment without sequences with a number of uniquely introduced gaps exceeding quantile
`omit_gap_pos`([allowed_gap_frac, motif_length])	Returns new alignment where all cols (motifs) have <= allowed_gap_frac gaps.
`omit_gap_runs`([allowed_run])	Returns new alignment where all seqs have runs of gaps <=allowed_run.
`omit_gap_seqs`([allowed_gap_frac])	Returns new alignment with seqs that have <= allowed_gap_frac.
`pad_seqs`([pad_length])	Returns copy in which sequences are padded to same length.
`probs_per_pos`([motif_length, ...])	returns MotifFreqsArray per position
`probs_per_seq`([motif_length, ...])	return MotifFreqsArray per sequence
`quick_tree`([calc, bootstrap, drop_invalid, ...])	Returns pairwise distances between sequences.
`rc`()	Returns the reverse complement alignment
`rename_seqs`(renamer)	returns new instance with sequences renamed
`replace_seqs`(seqs[, aa_to_codon])	Returns new alignment with same shape but with data taken from seqs.
`reverse_complement`()	Returns the reverse complement alignment.
`sample`([n, with_replacement, motif_length, ...])	Returns random sample of positions from self, e.g. to bootstrap.
`seqlogo`([width, height, wrap, vspace, colours])	returns Drawable sequence logo using mutual information
`set_repr_policy`([num_seqs, num_pos, ...])	specify policy for repr(self)
`sliding_windows`(window, step[, start, end])	Generator yielding new alignments of given length and interval.
`strand_symmetry`([motif_length])	returns dict of strand symmetry test results per seq
`take_positions`(cols[, negate])	Returns new Alignment containing only specified positions.
`take_positions_if`(f[, negate])	Returns new Alignment containing cols where f(col) is True.
`take_seqs`(seqs[, negate])	Returns new Alignment containing only specified seqs.
`take_seqs_if`(f[, negate])	Returns new Alignment containing seqs where f(row) is True.
`to_dict`()	Returns the alignment as dict of names -> strings.
`to_dna`()	returns copy of self as an alignment of DNA moltype seqs
`to_fasta`()	Return alignment in Fasta format
`to_html`([name_order, wrap, limit, ref_name, ...])	returns html with embedded styles for sequence colouring
`to_json`()	returns json formatted string
`to_moltype`(moltype)	returns copy of self with moltype seqs
`to_nexus`(seq_type[, wrap])	Return alignment in NEXUS format and mapping to sequence ids
`to_phylip`()	Return alignment in PHYLIP format and mapping to sequence ids
`to_pretty`([name_order, wrap])	returns a string representation of the alignment in pretty print format
`to_protein`()	returns copy of self as an alignment of PROTEIN moltype seqs
`to_rich_dict`()	returns detailed content including info and moltype attributes
`to_rna`()	returns copy of self as an alignment of RNA moltype seqs
`to_type`([array_align, moltype, alphabet])	returns alignment of type indicated by array_align
`trim_stop_codons`([gc, strict])	Removes any terminal stop codons from the sequences
`variable_positions`([include_gap_motif])	Return a list of variable position indexes.
`with_gaps_from`(template)	Same alignment but overwritten with the gaps from 'template'
`with_masked_annotations`(biotypes[, ...])	returns an alignment with annot_types regions replaced by mask_char if shadow is False, otherwise all other regions are masked.
`with_modified_termini`()	Changes the termini to include termini char instead of gapmotif.
`write`([filename, format])	Write the alignment to a file, preserving order of sequences.

gapped_by_map

add_feature(*, biotype: str, name: str, spans: List[Tuple[int, int]], seqid: str | None = None, parent_id: str | None = None, strand: str = '+', on_alignment: bool | None = None) → Feature#

add feature on named sequence, or on the alignment itself

Parameters:

seqid: sequence name, incompatible with on_alignment
parent_id: name of the parent feature
biotype: biological type, e.g. CDS
name: name of the feature
spans: plus strand coordinates of feature
strand: ‘+’ (default) or ‘-’
on_alignment: the feature is in alignment coordinates, incompatible with setting seqid. Set to True if seqid not provided.

Returns:

Feature

Raises:

ValueError if define a seqid not on alignment or use seqid and
on_alignment.

add_from_ref_aln(ref_aln, before_name=None, after_name=None)#

Insert sequence(s) to self based on their alignment to a reference sequence. Assumes the first sequence in ref_aln.names[0] is the reference.

By default the sequence is appended to the end of the alignment, this can be changed by using either before_name or after_name arguments.

Returns Alignment object of the same class.

Parameters:

ref_aln: reference alignment (Alignment object/series) of reference sequence and sequences to add. New sequences in ref_aln (ref_aln.names[1:] are sequences to add. If series is used as ref_aln, it must have the structure [[‘ref_name’, SEQ], [‘name’, SEQ]]
before_name: name of the sequence before which sequence is added
after_name: name of the sequence after which sequence is added If both before_name and after_name are specified seqs will be inserted using before_name.
Example:
Aln1:
-AC-DEFGHI (name: seq1)
XXXXXX–XX (name: seq2)
YYYY-YYYYY (name: seq3)
Aln2:
ACDEFGHI (name: seq1)
KL–MNPR (name: seqX)
KLACMNPR (name: seqY)
KL–MNPR (name: seqZ)
Out:
-AC-DEFGHI (name: seq1)
XXXXXX–XX (name: seq2)
YYYY-YYYYY (name: seq3)
-KL—MNPR (name: seqX)
-KL-ACMNPR (name: seqY)
-KL—MNPR (name: seqZ)

add_seqs(other, before_name=None, after_name=None)#

Returns new object of class self with sequences from other added.

Parameters:

other: same class as self or coerceable to that class
before_namestr: which sequence is added
after_namestr: which sequence is added

Notes

If both before_name and after_name are specified, the seqs will be inserted using before_name.

By default the sequence is appended to the end of the alignment, this can be changed by using either before_name or after_name arguments.

alignment_quality(app_name: str = 'ic_score', **kwargs)#

Computes the alignment quality using the indicated app

Parameters:

app_name: name of an alignment score calculating app, e.g. ‘ic_score’, ‘cogent3_score’, ‘sp_score’
kwargs: keyword arguments to be passed to the app. Use cogent3.app_help(app_name) to see the available options.

Returns:

float or a NotCompleted instance if the score could not be computed

annotate_from_gff(f: PathLike, seq_ids: list[str] | str | None = None)#

copies annotations from a gff file to a sequence in self

Parameters:

f: path to gff annotation file.
seq_name: names of seqs to be annotated. Does not support setting offset, set offset directly on sequences with seq.annotation_offset = offset

property annotation_db#

apply_pssm(pssm=None, path=None, background=None, pseudocount=0, names=None, ui=None)#

scores sequences using the specified pssm

Parameters:

pssmprofile.PSSM: if not provided, will be loaded from path
path: path to either a jaspar or cisbp matrix (path must end have a suffix matching the format).
pseudocount: adjustment for zero in matrix
names: returns only scores for these sequences and in the name order

Returns:

numpy array of log2 based scores at every position

coevolution(method='nmi', segments=None, drawable=None, show_progress=False, ui=None)#

performs pairwise coevolution measurement

Parameters:

methodstr: coevolution metric, defaults to ‘nmi’ (Normalized Mutual Information). Valid choices are ‘rmi’ (Resampled Mutual Information) and ‘mi’, mutual information.
segmentscoordinate series: coordinates of the form [(start, end), …] where all possible pairs of alignment positions within and between segments are examined.
drawableNone or str: Result object is capable of plotting data specified type. str value must be one of plot type ‘box’, ‘heatmap’, ‘violin’.
show_progressbool: shows a progress bar

Returns:

DictArray of results with lower-triangular values. Upper triangular
elements and estimates that could not be computed for numerical reasons
are set as nan

copy()#: Returns deep copy of self.

copy_annotations(seq_db: SupportsFeatures) → None#

copy annotations into attached annotation db

Parameters:

seq_db: compatible annotation db

Notes

Only copies annotations for records with seqid in self.names

count_gaps_per_pos(include_ambiguity=True)#

return counts of gaps per position as a DictArray

Parameters:

include_ambiguitybool: if True, ambiguity characters that include the gap state are included

count_gaps_per_seq(induced_by=False, unique=False, include_ambiguity=True, drawable=False)#

return counts of gaps per sequence as a DictArray

Parameters:

induced_bybool: a gapped column is considered to be induced by a seq if the seq has a non-gap character in that column.
uniquebool: count is limited to gaps uniquely induced by each sequence
include_ambiguitybool: if True, ambiguity characters that include the gap state are included
drawablebool or str: if True, resulting object is capable of plotting data via specified plot type ‘bar’, ‘box’ or ‘violin’

counts(motif_length=1, include_ambiguity=False, allow_gap=False, exclude_unobserved=False)#

counts of motifs

Parameters:

motif_length: number of elements per character.
include_ambiguity: if True, motifs containing ambiguous characters from the seq moltype are included. No expansion of those is attempted.
allow_gap: if True, motifs containing a gap character are included.
exclude_unobserved: if True, unobserved motif combinations are excluded.

Notes

only non-overlapping motifs are counted

counts_per_pos(motif_length=1, include_ambiguity=False, allow_gap=False, warn=False)#

return DictArray of counts per position

Parameters:

warn: warns if motif_length > 1 and alignment trimmed to produce motif columns

counts_per_seq(motif_length=1, include_ambiguity=False, allow_gap=False, exclude_unobserved=False, warn=False)#

counts of non-overlapping motifs per sequence

Parameters:

motif_length: number of elements per character.
include_ambiguity: if True, motifs containing ambiguous characters from the seq moltype are included. No expansion of those is attempted.
allow_gap: if True, motifs containing a gap character are included.
exclude_unobserved: if False, all canonical states included
warn: warns if motif_length > 1 and alignment trimmed to produce motif columns

Returns:

MotifCountsArray

deepcopy(sliced: bool = True)#

returns deep copy of self.

Parameters:

sliced: if True, reduces the sequence to current interval. This also causes dropping annotations.

default_gap = '-'#

degap(**kwargs)#

Returns copy in which sequences have no gaps.

Parameters:

kwargs: passed to class constructor

distance_matrix(calc='pdist', show_progress=False, drop_invalid=False)#

Returns pairwise distances between sequences.

Parameters:

calcstr: a pairwise distance calculator or name of one. For options see cogent3.evolve.fast_distance.available_distances
show_progressbool: controls progress display for distance calculation
drop_invalidbool: If True, sequences for which a pairwise distance could not be calculated are excluded. If False, an ArithmeticError is raised if a distance could not be computed on observed data.

dotplot(name1=None, name2=None, window=20, threshold=None, k=None, min_gap=0, width=500, title=None, rc=False, show_progress=False)#

make a dotplot between specified sequences. Random sequences chosen if names not provided.

Parameters:

name1, name2str or None: names of sequences. If one is not provided, a random choice is made
windowint: k-mer size for comparison between sequences
thresholdint: windows where the sequences are identical >= threshold are a match
kint: size of k-mer to break sequences into. Larger values increase speed but reduce resolution. If not specified, is computed as the maximum of (window-threshold), (window % k) * k <= threshold.
min_gapint: permitted gap for joining adjacent line segments, default is no gap joining
widthint: figure width. Figure height is computed based on the ratio of len(seq1) / len(seq2)
title: title for the plot
rcbool or None: include dotplot of reverse compliment also. Only applies to Nucleic acids moltypes

Returns:

a Drawable or AnnotatedDrawable

entropy_per_pos(motif_length=1, include_ambiguity=False, allow_gap=False, warn=False)#: returns shannon entropy per position

entropy_per_seq(motif_length=1, include_ambiguity=False, allow_gap=False, exclude_unobserved=True, warn=False)#

returns the Shannon entropy per sequence

Parameters:

motif_length: number of characters per tuple.
include_ambiguity: if True, motifs containing ambiguous characters from the seq moltype are included. No expansion of those is attempted.
allow_gap: if True, motifs containing a gap character are included.
exclude_unobserved: if True, unobserved motif combinations are excluded.
warn: warns if motif_length > 1 and alignment trimmed to produce motif columns

Notes

For motif_length > 1, it’s advisable to specify exclude_unobserved=True, this avoids unnecessary calculations.

filtered(predicate, motif_length=1, drop_remainder=True, **kwargs)#

The alignment positions where predicate(column) is true.

Parameters:

predicatecallable: a callback function that takes an tuple of motifs and returns True/False
motif_lengthint: length of the motifs the sequences should be split into, eg. 3 for filtering aligned codons.
drop_remainderbool: If length is not modulo motif_length, allow dropping the terminal remaining columns

gap_chars = {'-': None, '?': None}#

gapped_by_map(keep, **kwargs)#

get_ambiguous_positions()#

Returns dict of seq:{position:char} for ambiguous chars.

Used in likelihood calculations.

get_degapped_relative_to(name)#

Remove all columns with gaps in sequence with given name.

Returns Alignment object of the same class. Note that the seqs in the new Alignment are always new objects.

Parameters:

name: sequence name

get_drawable(*, biotype: str | Iterable[str] | None = None, width: int = 600, vertical: int = False)#

make a figure from sequence features

Parameters:

biotype: passed to get_features(biotype). Can be a single biotype or series. Only features matching this will be included.
width: width in pixels
vertical: rotates the drawable

Returns:

a Drawable instance

get_drawables(*, biotype: str | Iterable[str] | None = None) → dict#

returns a dict of drawables, keyed by type

Parameters:

biotype: passed to get_features(biotype). Can be a single biotype or series. Only features matching this will be included.

get_features(*, seqid: str | None = None, biotype: str | None = None, name: str | None = None, on_alignment: bool | None = None, allow_partial: bool = False) → Iterator[Feature]#

yields Feature instances

Parameters:

seqid: limit search to features on this named sequence, defaults to search all
biotype: biotype of the feature, e.g. CDS, gene
name: name of the feature
on_alignment: limit query to features on Alignment, ignores sequences. Ignored on SequenceCollection instances.
allow_partial: allow features partially overlaping self

Notes

When dealing with a nucleic acid moltype, the returned features will yield a sequence segment that is consistently oriented irrespective of strand of the current instance.

get_gap_array(include_ambiguity=True)#

returns bool array with gap state True, False otherwise

Parameters:

include_ambiguitybool: if True, ambiguity characters that include the gap state are included

get_gapped_seq(seq_name, recode_gaps=False)#

Return a gapped Sequence object for the specified seqname.

Note: always returns Sequence object, not ArraySequence.

get_identical_sets(mask_degen=False)#

returns sets of names for sequences that are identical

Parameters:

mask_degen: if True, degenerate characters are ignored

get_lengths(include_ambiguity=False, allow_gap=False)#

returns {name: seq length, …}

Parameters:

include_ambiguity: if True, motifs containing ambiguous characters from the seq moltype are included. No expansion of those is attempted.
allow_gap: if True, motifs containing a gap character are included.

get_motif_probs(alphabet=None, include_ambiguity=False, exclude_unobserved=False, allow_gap=False, pseudocount=0)#

Return a dictionary of motif probs, calculated as the averaged frequency across sequences.

Parameters:

include_ambiguity: if True resolved ambiguous codes are included in estimation of frequencies, default is False.
exclude_unobserved: if True, motifs that are not present in the alignment are excluded from the returned dictionary, default is False.
allow_gap: allow gap motif

Notes

only non-overlapping motifs are counted

get_position_indices(f, native=False, negate=False)#

Returns list of column indices for which f(col) is True.

fcallable: function that returns true/false given an alignment position
nativeboolean: if True, and ArrayAlignment, f is provided with slice of array otherwise the string is used
negateboolean: if True, not f() is used

get_projected_feature(*, seqid: str, feature: Feature) → Feature#

returns an alignment feature projected onto the seqid sequence

Parameters:

seqid: name of the sequence to project the feature onto
feature: a Feature, bound to self, that will be projected

Returns:

a new Feature bound to seqid

Notes

The alignment coordinates of feature are converted into the seqid sequence coordinates and the object is bound to that sequence.

The feature is added to the annotation_db.

get_projected_features(*, seqid: str, **kwargs) → list[Feature]#: projects all features from other sequences onto seqid

get_seq(seqname)#

Return a ungapped Sequence object for the specified seqname.

Note: always returns Sequence object, not ArraySequence.

get_seq_indices(f, negate=False)#

Returns list of keys of seqs where f(row) is True.

List will be in the same order as self.names, if present.

get_similar(target, min_similarity=0.0, max_similarity=1.0, metric=<cogent3.util.transform.for_seq object>, transform=None)#

Returns new Alignment containing sequences similar to target.

Parameters:

target: sequence object to compare to. Can be in the alignment.
min_similarity: minimum similarity that will be kept. Default 0.0.
max_similarity: maximum similarity that will be kept. Default 1.0. (Note that both min_similarity and max_similarity are inclusive.) metric similarity function to use. Must be f(first_seq, second_seq).
The default metric is fraction similarity, ranging from 0.0 (0%
identical) to 1.0 (100% identical). The Sequence classes have lots
of methods that can be passed in as unbound methods to act as the
metric, e.g. frac_same_gaps.
transform: transformation function to use on the sequences before the metric is calculated. If None, uses the whole sequences in each case. A frequent transformation is a function that returns a specified range of a sequence, e.g. eliminating the ends. Note that the transform applies to both the real sequence and the target sequence.
WARNING: if the transformation changes the type of the sequence (e.g.
extracting a string from an RnaSequence object), distance metrics that
depend on instance data of the original class may fail.

get_translation(gc=None, incomplete_ok=False, include_stop=False, trim_stop=True, **kwargs)#

translate from nucleic acid to protein

Parameters:

gc: genetic code, either the number or name (use cogent3.core.genetic_code.available_codes)
incomplete_ok: codons that are mixes of nucleotide and gaps converted to ‘?’. raises a ValueError if False
include_stop: whether to allow a stops in the translated sequence
trim_stop: exclude terminal stop codons if they exist
kwargs: related to construction of the resulting object

Returns:

A new instance of self translated into protein

has_terminal_stop(gc: Any = None, strict: bool = False) → bool#

Returns True if any sequence has a terminal stop codon.

Parameters:

gc: valid input to cogent3.get_code(), a genetic code object, number or name
strict: If True, raises an exception if a seq length not divisible by 3

information_plot(width=None, height=None, window=None, stat='median', include_gap=True)#

plot information per position

Parameters:

widthint: figure width in pixels
heightint: figure height in pixels
windowint or None: used for smoothing line, defaults to sqrt(length)
statstr: ‘mean’ or ‘median, used as the summary statistic for each window
include_gap: whether to include gap counts, shown on right y-axis

is_array = {'array', 'array_seqs'}#

is_ragged()#: Returns True if alignment has sequences of different lengths.

iter_positions(pos_order=None)#

Iterates over positions in the alignment, in order.

pos_order refers to a list of indices (ints) specifying the column order. This lets you rearrange positions if you want to (e.g. to pull out individual codon positions).

Note that self.iter_positions() always returns new objects, by default lists of elements. Use map(f, self.iter_positions) to apply the constructor or function f to the resulting lists (f must take a single list as a parameter).

Will raise IndexError if one of the indices in order exceeds the sequence length. This will always happen on ragged alignments: assign to self.seq_len to set all sequences to the same length.

iter_selected(seq_order=None, pos_order=None)#

Iterates over elements in the alignment.

seq_order (names) can be used to select a subset of seqs. pos_order (positions) can be used to select a subset of positions.

Always iterates along a seq first, then down a position (transposes normal order of a[i][j]; possibly, this should change)..

WARNING: Alignment.iter_selected() is not the same as alignment.iteritems() (which is the built-in dict iteritems that iterates over key-value pairs).

iter_seqs(seq_order=None)#

Iterates over values (sequences) in the alignment, in order.

seq_order: list of keys giving the order in which seqs will be returned. Defaults to self.Names. Note that only these sequences will be returned, and that KeyError will be raised if there are sequences in order that have been deleted from the Alignment. If self.Names is None, returns the sequences in the same order as self.named_seqs.values().

Use map(f, self.seqs()) to apply the constructor f to each seq. f must accept a single list as an argument.

Always returns references to the same objects that are values of the alignment.

iupac_consensus(alphabet=None, allow_gap=True)#: Returns string containing IUPAC consensus sequence of the alignment.

majority_consensus()#

Returns list containing most frequent item at each position.

Optional parameter transform gives constructor for type to which result will be converted (useful when consensus should be same type as originals).

make_feature(*, feature: FeatureDataType, on_alignment: bool | None = None) → Feature#

create a feature on named sequence, or on the alignment itself

Parameters:

feature: a dict with all the necessary data rto construct a feature
on_alignment: the feature is in alignment coordinates, incompatible with setting ‘seqid’. Set to True if ‘seqid’ not provided.

Returns:

Feature

Raises:

ValueError if define a ‘seqid’ not on alignment or use ‘seqid’ and
on_alignment.

Notes

To get a feature AND add it to annotation_db, use add_feature().

matching_ref(ref_name, gap_fraction, gap_run)#

Returns new alignment with seqs well aligned with a reference.

gap_fraction = fraction of positions that either have a gap in the: template but not in the seq or in the seq but not in the template
gap_run = number of consecutive gaps tolerated in query relative to: sequence or sequence relative to query

moltype = MolType(('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'))#

property named_seqs#

no_degenerates(motif_length=1, allow_gap=False)#

returns new alignment without degenerate characters

Parameters:

motif_length: sequences are segmented into units of this size
allow_gap: whether gaps are to be treated as a degenerate character (default, most evolutionary modelling treats gaps as N) or not.

property num_seqs#: Returns the number of sequences in the alignment.

omit_bad_seqs(quantile=None)#

Returns new alignment without sequences with a number of uniquely introduced gaps exceeding quantile

Uses count_gaps_per_seq(unique=True) to obtain the counts of gaps uniquely introduced by a sequence. The cutoff is the quantile of this distribution.

Parameters:

quantilefloat or None: sequences whose unique gap count is in a quantile larger than this cutoff are excluded. The default quantile is (num_seqs - 1) / num_seqs

omit_gap_pos(allowed_gap_frac=0.999999, motif_length=1)#

Returns new alignment where all cols (motifs) have <= allowed_gap_frac gaps.

Parameters:

allowed_gap_frac: specifies proportion of gaps is allowed in each column (default is just < 1, i.e. only cols with at least one gap character are preserved). Set to 1 - e-6 to exclude strictly gapped columns.
motif_length: set’s the “column” width, e.g. setting to 3 corresponds to codons. A motif that includes a gap at any position is included in the counting. Default is 1.

omit_gap_runs(allowed_run=1)#

Returns new alignment where all seqs have runs of gaps <=allowed_run.

Note that seqs with exactly allowed_run gaps are not deleted. Default is for allowed_run to be 1 (i.e. no consecutive gaps allowed).

Because the test for whether the current gap run exceeds the maximum allowed gap run is only triggered when there is at least one gap, even negative values for allowed_run will still let sequences with no gaps through.

omit_gap_seqs(allowed_gap_frac=0)#

Returns new alignment with seqs that have <= allowed_gap_frac.

allowed_gap_frac should be a fraction between 0 and 1 inclusive. Default is 0.

pad_seqs(pad_length=None, **kwargs)#

Returns copy in which sequences are padded to same length.

Parameters:

pad_length: Length all sequences are to be padded to. Will pad to max sequence length if pad_length is None or less than max length.

property positions#

Iterates over positions in the alignment, in order.

pos_order refers to a list of indices (ints) specifying the column order. This lets you rearrange positions if you want to (e.g. to pull out individual codon positions).

Will raise IndexError if one of the indices in order exceeds the sequence length. This will always happen on ragged alignments: assign to self.seq_len to set all sequences to the same length.

probs_per_pos(motif_length=1, include_ambiguity=False, allow_gap=False, warn=False)#: returns MotifFreqsArray per position

probs_per_seq(motif_length=1, include_ambiguity=False, allow_gap=False, exclude_unobserved=False, warn=False)#

return MotifFreqsArray per sequence

Parameters:

motif_length: number of characters per tuple.
include_ambiguity: if True, motifs containing ambiguous characters from the seq moltype are included. No expansion of those is attempted.
allow_gap: if True, motifs containing a gap character are included.
exclude_unobserved: if True, unobserved motif combinations are excluded.
warn: warns if motif_length > 1 and alignment trimmed to produce motif columns

quick_tree(calc='percent', bootstrap=None, drop_invalid=False, show_progress=False, ui=None)#

Returns pairwise distances between sequences.

Parameters:

calcstr: a pairwise distance calculator or name of one. For options see cogent3.evolve.fast_distance.available_distances
show_progressbool: controls progress display for distance calculation
drop_invalidbool: If True, sequences for which a pairwise distance could not be calculated are excluded. If False, an ArithmeticError is raised if a distance could not be computed on observed data.
bootstrapint or None: Number of non-parametric bootstrap replicates. Resamples alignment columns with replacement and builds a phylogeny for each such resampling.
drop_invalidbool: If True, sequences for which a pairwise distance could not be calculated are excluded. If False, an ArithmeticError is raised if a distance could not be computed on observed data.

Returns:

a phylogenetic tree. If bootstrap specified, returns the weighted
majority consensus. Support for each node is stored as
edge.params[‘params’].

Notes

Sequences in the observed alignment for which distances could not be computed are omitted. Bootstrap replicates are required to have distances for all seqs present in the observed data distance matrix.

rc()#: Returns the reverse complement alignment

rename_seqs(renamer)#

returns new instance with sequences renamed

Parameters:

renamercallable: function that will take current sequences and return the new one

replace_seqs(seqs, aa_to_codon=True)#

Returns new alignment with same shape but with data taken from seqs.

Parameters:

aa_to_codon: If True (default) aligns codons from protein alignment, or, more generally, substituting in codons from a set of protein sequences (not necessarily aligned). For this reason, it takes characters from seqs three at a time rather than one at a time (i.e. 3 characters in seqs are put in place of 1 character in self). If False, seqs must be the same lengths.
If seqs is an alignment, any gaps in it will be ignored.

reverse_complement()#: Returns the reverse complement alignment. A synonym for rc.

sample(n=None, with_replacement=False, motif_length=1, randint=<built-in method randint of numpy.random.mtrand.RandomState object>, permutation=<built-in method permutation of numpy.random.mtrand.RandomState object>)#

Returns random sample of positions from self, e.g. to bootstrap.

Parameters:

n

the number of positions to sample from the alignment. Default is alignment length

with_replacement

boolean flag for determining if sampled positions

random_series

a random number generator with .randint(min,max) .random() methods

Notes:

By default (resampling all positions without replacement), generates a permutation of the positions of the alignment.

Setting with_replacement to True and otherwise leaving parameters as defaults generates a standard bootstrap resampling of the alignment.

seqlogo(width=700, height=100, wrap=None, vspace=0.005, colours=None)#

returns Drawable sequence logo using mutual information

Parameters:

width, heightfloat: plot dimensions in pixels
wrapint: number of alignment columns per row
vspacefloat: vertical separation between rows, as a proportion of total plot
coloursdict: mapping of characters to colours. If note provided, defaults to custom for everything ecept protein, which uses protein moltype colours.

Notes

Computes MI based on log2 and includes the gap state, so the maximum possible value is -log2(1/num_states)

property seqs#

set_repr_policy(num_seqs=None, num_pos=None, ref_name=None, wrap=None)#

specify policy for repr(self)

Parameters:

num_seqsint or None: number of sequences to include in represented display.
num_posint or None: length of sequences to include in represented display.
ref_namestr or None: name of sequence to be placed first, or “longest” (default). If latter, indicates longest sequence will be chosen.
wrapint or None: number of printed bases per row

sliding_windows(window, step, start=None, end=None)#

Generator yielding new alignments of given length and interval.

Parameters:

window: The length of each returned alignment.
step: The interval between the start of the successive alignment objects returned.
start: first window start position
end: last window start position

strand_symmetry(motif_length=1)#: returns dict of strand symmetry test results per seq

take_positions(cols, negate=False)#

Returns new Alignment containing only specified positions.

By default, the seqs will be lists, but an alternative constructor can be specified.

Note that take_positions will fail on ragged positions.

take_positions_if(f, negate=False)#: Returns new Alignment containing cols where f(col) is True.

take_seqs(seqs, negate=False, **kwargs)#

Returns new Alignment containing only specified seqs.

Note that the seqs in the new alignment will be references to the same objects as the seqs in the old alignment.

take_seqs_if(f, negate=False, **kwargs)#

Returns new Alignment containing seqs where f(row) is True.

Note that the seqs in the new Alignment are the same objects as the seqs in the old Alignment, not copies.

to_dict()#

Returns the alignment as dict of names -> strings.

Note: returns strings, NOT Sequence objects.

to_dna()#: returns copy of self as an alignment of DNA moltype seqs

to_fasta()#

Return alignment in Fasta format

Parameters:

make_seqlabel: callback function that takes the seq object and returns a label str

to_html(name_order=None, wrap=60, limit=None, ref_name='longest', colors=None, font_size=12, font_family='Lucida Console')#

returns html with embedded styles for sequence colouring

Parameters:

name_order

order of names for display.

wrap

number of alignment columns per row

limit

truncate alignment to this length

ref_name

Name of an existing sequence or ‘longest’. If the latter, the longest sequence (excluding gaps and ambiguities) is selected as the reference.

colors

{character moltype.

font_size

in points. Affects labels and sequence and line spacing (proportional to value)

font_family

string denoting font family

To display in jupyter notebook:

>>> from IPython.core.display import HTML
>>> HTML(aln.to_html())

to_json()#: returns json formatted string

to_moltype(moltype)#: returns copy of self with moltype seqs

to_nexus(seq_type, wrap=50)#

Return alignment in NEXUS format and mapping to sequence ids

NOTE Not that every sequence in the alignment MUST come from: a different species!! (You can concatenate multiple sequences from same species together before building tree)

seq_type: dna, rna, or protein

Raises exception if invalid alignment

to_phylip()#

Return alignment in PHYLIP format and mapping to sequence ids

raises exception if invalid alignment

to_pretty(name_order=None, wrap=None)#

returns a string representation of the alignment in pretty print format

Parameters:

name_order: order of names for display.
wrap: maximum number of printed bases

to_protein()#: returns copy of self as an alignment of PROTEIN moltype seqs

to_rich_dict()#: returns detailed content including info and moltype attributes

to_rna()#: returns copy of self as an alignment of RNA moltype seqs

to_type(array_align=False, moltype=None, alphabet=None)#

returns alignment of type indicated by array_align

Parameters:

array_align: bool: if True, returns as ArrayAlignment. Otherwise as “standard” Alignment class. Conversion to ArrayAlignment loses annotations.
moltypeMolType instance: overrides self.moltype
alphabetAlphabet instance: overrides self.alphabet
If array_align would result in no change (class is same as self),
returns self

trim_stop_codons(gc: Any = None, strict: bool = False, **kwargs)#

Removes any terminal stop codons from the sequences

Parameters:

gc: valid input to cogent3.get_code(), a genetic code object, number or name
strict: If True, raises an exception if a seq length not divisible by 3

variable_positions(include_gap_motif=True)#

Return a list of variable position indexes.

Parameters:

include_gap_motif: if False, sequences with a gap motif in a column are ignored.

with_gaps_from(template)#: Same alignment but overwritten with the gaps from ‘template’

with_masked_annotations(biotypes, mask_char=None, shadow=False)#

returns an alignment with annot_types regions replaced by mask_char if shadow is False, otherwise all other regions are masked.

Parameters:

biotypes: annotation type(s)
mask_char: must be a character valid for the seq moltype. The default value is the most ambiguous character, eg. ‘?’ for DNA
shadow: whether to mask the annotated regions, or everything but the annotated regions

with_modified_termini()#

Changes the termini to include termini char instead of gapmotif.

Useful to correct the standard gap char output by most alignment programs when aligned sequences have different ends.

write(filename=None, format=None, **kwargs)#

Write the alignment to a file, preserving order of sequences.

Parameters:

filename: name of the sequence file
format: format of the sequence file

Notes

If format is None, will attempt to infer format from the filename suffix.