# Sequence Collections and Alignments

For loading collections of unaligned or aligned sequences see Loading an alignment from a file or url.

## What’s the difference between `Alignment` and `ArrayAlignment`?

The `Alignment` class can be annotated, meaning you can add annotations to an Alignment or it’s member sequences and you can slice the alignment via those objects. This capability is achieved, under the hood, by having the individual sequences represent gaps as a “span”, rather than explicitly as a “-” character in the sequence itself. This representation is also efficient for very long sequences.

The `ArrayAlignment` class cannot be annotated. The class represents its sequences as a `numpy.ndarray` instance. Thus, the gaps are included as a state in the array. This class is better at handling a lot of sequences and should typically be faster. This is the default class returned by the `load_aligned_seqs` and `make_aligned_seqs` functions. (See Loading an alignment from a file or url for details.)

You can change alignment types using the `to_type()` method.

## Basic Collection objects

### Constructing a `SequenceCollection` or `Alignment` object from strings

### Constructing a `ArrayAlignment` using `make_aligned_seqs`

### Converting a `SequenceCollection` to FASTA format

## Adding new sequences to an existing collection or alignment

New sequences can be either appended or inserted using the `add_seqs` method. More than one sequence can be added at the same time. Note that `add_seqs` does not modify the existing collection/alignment, it creates a new one.

### Appending the sequences

`add_seqs` without additional parameters will append the sequences to the end of the collection/alignment.

Note

The order is not preserved if you use the `to_fasta()` method, which sorts sequences by name.

### Inserting the sequences

Sequences can be inserted into an alignment at the specified position using either the `before_name` or `after_name` arguments.

### Inserting sequence(s) based on their alignment to a reference sequence

Already aligned sequences can be added to an existing `Alignment` object and aligned at the same time using the `add_from_ref_aln` method. The alignment is performed based on their alignment to a reference sequence (which must be present in both alignments). The method assumes the first sequence in `ref_aln.names[0]` is the reference.

`add_from_ref_aln` has the same arguments as `add_seqs` so `before_name` and `after_name` can be used to insert the new sequences at the desired position.

Note

This method does not work with the `ArrayAlignment` class.

#### Removing all columns with gaps in a named sequence

## The elements of a collection or alignment

### Accessing individual sequences from a collection or alignment by name

Using the `get_seq` method allows for extracting an unaligned sequence from a collection or alignment by name.

Alternatively, if you want to extract the aligned (i.e., gapped) sequence from an alignment, you can use `get_gapped_seq`.

To see the names of the sequences in a sequence collection, use the `names` attribute.

### Slice the sequences from an alignment like a list

The usual approach is to access a `SequenceCollection` or `Alignment` object as a dictionary, obtaining the individual sequences using the titles as “keys” (above). However, one can also iterate through the collection like a list.

### Getting a subset of sequences from the alignment

Note

The `Alignment` class (which you get if you set `array_align=False`) is more memory efficient. The subset contain references to the original sequences, not copies.

## Alignments

### Creating an `Alignment` object from a `SequenceCollection`

### Convert alignment to DNA, RNA or PROTEIN moltypes

This is useful if you’ve loaded a sequence alignment without specifying the moltype and later need to convert it using the dedicated method

Or using the generic `to_moltype()` method

To RNA

To PROTEIN

### Handling gaps

#### Remove all gaps from an alignment in FASTA format

This necessarily returns a `SequenceCollection`.

### Writing sequences to file

Both collection and alignment objects have a `write` method. The output format is inferred from the filename suffix,

or by the `format` argument.

### Converting an alignment to FASTA format

### Converting an alignment into Phylip format

### Converting an alignment to a list of strings

## Slicing an alignment

### By rows (sequences)

An `Alignment` can be sliced

but a `SequenceCollection` cannot be sliced

### Getting a single column from an alignment

### Getting a region of contiguous columns

### Iterating over alignment positions

### Getting codon 3rd positions from `Alignment`

We’ll do this by specifying the position indices of interest, creating a sequence `Feature` and using that to extract the positions.

### Getting codon 3rd positions from `ArrayAlignment`

We can use more conventional slice notation in this instance. Note, because Python counts from 0, the 3rd position starts at index 2.

### Filtering positions

#### Trim terminal stop codons

For evolutionary analyses that use codon models we need to exclude terminating stop codons. For the case where the sequences are all of length divisible by 3.

To detect if the alignment contains sequences not divisible by 3, use the `strict` argument. This argument covers both allowing partial terminating codons / not divisible by 3.

#### Eliminating columns with non-nucleotide characters

We sometimes want to eliminate ambiguous or gap data from our alignments. We show how to exclude alignment columns by the characters they contain. In the first instance we do this just for single nucleotide columns, then for trinucleotides (equivalent for handling codons). Both are done using the `no_degenerates` method.

We apply to nucleotides,

Applying the same filter to trinucleotides (specified by setting `motif_length=3`).

#### Getting all variable positions from an alignment

#### Getting all constant positions from an alignment

#### Getting all variable codons from an alignment

This is done using the `filtered` method using the `motif_length` argument. We demonstrate this first for the `ArrayAlignment`.

Then for the standard `Alignment` by first converting the `ArrayAlignment`.

### Filtering sequences

#### Extracting sequences by sequence identifier into a new alignment object

You can use `take_seqs` to extract some sequences by sequence identifier from an alignment to a new alignment object:

Alternatively, you can extract only the sequences which are not specified by passing `negate=True`:

#### Extracting sequences using an arbitrary function into a new alignment object

You can use `take_seqs_if` to extract sequences into a new alignment object based on whether an arbitrary function applied to the sequence evaluates to True. For example, to extract sequences which don’t contain any N bases you could do the following:

You can additionally get the sequences where the provided function evaluates to False:

## Computing alignment statistics

### Getting motif counts

We state the motif length we want and whether to allow gap or ambiguous characters. The latter only has meaning for IPUAC character sets (the DNA, RNA or PROTEIN moltypes). We illustrate this for the DNA moltype with motif lengths of 1 and 3.

Note

Only the observed motifs are returned, rather than all defined by the alphabet.

### Computing motif probabilities from an alignment

The method `get_motif_probs` of `Alignment` objects returns the probabilities for all motifs of a given length. For individual nucleotides:

For dinucleotides or longer, we need to pass in an `Alphabet` with the appropriate word length. Here is an example with trinucleotides:

The same holds for other arbitrary alphabets, as long as they match the alignment `MolType`.

Some calculations in `cogent3` require all non-zero values in the motif probabilities, in which case we use a pseudo-count. We illustrate that here with a simple example where T is missing. Without the pseudo-count, the frequency of T is 0.0, with the pseudo-count defined as 1e-6 then the frequency of T will be slightly less than 1e-6.

It is important to notice that motif probabilities are computed by treating sequences as non-overlapping tuples. Below is a very simple pair of identical sequences where there are clearly 2 ‘AA’ dinucleotides per sequence but only the first one is ‘in-frame’ (frame width = 2).

We then create a dinucleotide `Alphabet` object and use this to get dinucleotide probabilities. These frequencies are determined by breaking each aligned sequence up into non-overlapping dinucleotides and then doing a count. The expected value for the ‘AA’ dinucleotide in this case will be 2/8 = 0.25.

What about counting the total incidence of dinucleotides including those not in-frame? A naive application of the Python string object’s count method will not work as desired either because it “returns the number of non-overlapping occurrences”.

To count all occurrences of a given dinucleotide in a DNA sequence, one could use a standard Python approach such as list comprehension:

### Working with alignment gaps

#### Filtering extracted columns for the gap character

#### Calculating the gap fraction

#### Extracting maps of aligned to unaligned positions (i.e., gap maps)

It’s often important to know how an alignment position relates to a position in one or more of the sequences in the alignment. The `gap_maps` method of the individual sequences is useful for this. To get a map of sequence to alignment positions for a specific sequence in your alignment, do the following:

It’s now possible to look up positions in the `seq1`, and find out what they map to in the alignment:

This tells us that in position 3 in `seq1` corresponds to position 3 in `aln`, and that position 8 in `seq1` corresponds to position 9 in `aln`.

Notice that we grabbed the first result from the call to `gap_maps`. This is the sequence position to alignment position map. The second value returned is the alignment position to sequence position map, so if you want to find out what sequence positions the alignment positions correspond to (opposed to what alignment positions the sequence positions correspond to) for a given sequence, you would take the following steps:

If an alignment position is a gap, and therefore has no corresponding sequence position, you’ll get a `KeyError`.

Note

The first position in alignments and sequences is always numbered position 0.

#### Filtering alignments based on gaps

Note

An alternate, computationally faster, approach to removing gaps is to use the `filtered` method as discussed in Filtering positions.

The `omit_gap_runs` method can be applied to remove long stretches of gaps in an alignment. In the following example, we remove sequences that have more than two adjacent gaps anywhere in the aligned sequence.

If instead, we just wanted to remove positions from the alignment which are gaps in more than a certain percentage of the sequences, we could use the `omit_gap_pos` function. For example:

If you wanted to remove sequences which contain more than a certain percent gap characters, you could use the `omit_gap_seqs` method. This is commonly applied to filter partial sequences from an alignment.

Note that following this call to `omit_gap_seqs`, the 4th column of `filtered_aln` is 100% gaps. This is generally not desirable, so a call to `omit_gap_seqs` is frequently followed with a call to `omit_gap_pos` with no parameters – this defaults to removing positions which are all gaps:

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