In this recipe, we'll work with a myoglobin/hemoglobin mulitple sequence alignment to create and view a visual summary of the alignment, and then to apply some filtering of sequences and positions in the alignment. Because by definition a mutliple sequence alignment contains sequences all of the same length, an alignment can be thought of as a matrix or table. A convenient way to get a visual summary of an alignment is with a heatmap. We're going to use the scikit-bio TabularMSA object for this, along with some information from the AAIndex database, and matplotlib.

First we'll load a pre-existing multiple sequence alignment into a TabularMSA and set the MSA's index labels to the sequence identifiers read in from the FASTA file.

In [1]:
from skbio import TabularMSA, Protein

msa.reassign_index(minter='id')


Next we're going to load the AAIndex database hydrophobicity index values. This provides a measure of the hydrophobicity of all of the amino acids, where low values are associated with more hydrophilic amino acids, and high values are associated with more hydrophobic amino acids. We're going to use these in our visualization of the alignment.

In [2]:
# Data derived from AAIndex:
# http://www.genome.jp/dbget-bin/www_bget?aaindex:ARGP820101
import numpy as np
from collections import defaultdict
hydrophobicity_idx = defaultdict(lambda: np.nan)
hydrophobicity_idx.update({'A': 0.61, 'L': 1.53, 'R': 0.60, 'K': 1.15, 'N': 0.06, 'M': 1.18,
'D': 0.46, 'F': 2.02, 'C': 1.07, 'P': 1.95, 'Q': 0., 'S': 0.05,
'E': 0.47, 'T': 0.05, 'G': 0.07, 'W': 2.65, 'H': 0.61, 'Y': 1.88,
'I': 2.22, 'V': 1.32})
hydrophobicity_labels=['Hydrophilic', 'Medium', 'Hydrophobic']


## Visualizing alignments¶

Next we'll define a function that makes use of matplotlib to generate a heatmap-style image of the alignment. In the future, this functionality will be a TabularMSA method (#765). The heatmap and legend code was adapted from the matplotlib gallery example here.

In [3]:
import matplotlib.pyplot as plt
%matplotlib inline

def msa_to_heatmap(msa, value_map, legend_labels=('Low', 'Medium', 'High'), fig_size=(15,10), cmap='YlGn', sequence_order=None):
"""Plot a multiple sequence alignment as a heatmap.

Parameters
----------
msa : skbio.TabularMSA
The multiple sequence alignment to be plotted
value_map : dict, collections.defaultdict
Dictionary mapping characters in the alignment to values. KeyErrors are not
caught, so all possible values should be in this dict, or it should be a
collections.defaultdict which can, for example, default to nan.
legend_labels : iterable, optional
Labels for the min, median, and max values in the legend.
fig_size : tuple, optional
Size of figure in inches.
cmap : matplotlib colormap, optional
See here for choices: http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
sequence_order : iterable, optional
The order, from top-to-bottom, that the sequences should be plotted in.

Raises
------
KeyError
If a character in msa is not in value_map, and value_map is not a
collections.defaultdict.

"""
if sequence_order is None:
sequence_order = msa.index

# fill a data matrix by iterating over the alignment and mapping
# characters to values
mtx = []
for label in sequence_order:
seq = str(msa.loc[label])
mtx.append([value_map[aa] for aa in seq])

# build the heatmap, this code derived from the Matplotlib Gallery
# http://matplotlib.org/examples/pylab_examples/colorbar_tick_labelling_demo.html
fig, ax = plt.subplots()
fig.set_size_inches(fig_size)

cax = ax.imshow(mtx, interpolation='nearest', cmap=cmap)

# Add colorbar and define tick labels
values = list(value_map.values())
cbar = fig.colorbar(cax,
ticks=[min(values),
np.nanmedian(values),
max(values)],
orientation='horizontal')
ax.set_yticks([0] + list(range(3, msa.shape.sequence - 3, 3)) + [msa.shape.sequence - 1])
ax.set_yticklabels(sequence_order)
ax.set_xticks(range(msa.shape.position))
ax.set_xticklabels(msa.consensus(), size=7)
cbar.ax.set_xticklabels(legend_labels) # horizontal colorbar


We can now use this function to visualize our alignment. Note that the human hemoglobin sequence, which is the top-most one, is clearly the most different. We can also clearly see that hydrophobicity is highly conserved, as is illustrated by vertical bands that are similar in color. Some select sequence identifiers are used on the y-axis as labels, and the x-axis is the majority consensus character (either an amino acid abbreviation or a gap character) at each position in the alignment.

In [4]:
msa_to_heatmap(msa, hydrophobicity_idx, legend_labels=hydrophobicity_labels)


One limitation in the above plot is that there isn't any order to the sequences. If we want to vertically group sequences from more closely related organisms, we can build a neighbor joining tree from the alignment, perform outgroup rooting, and then traverse over the resulting tips to define the order.

In [5]:
from skbio import DistanceMatrix
from skbio.sequence.distance import hamming
from skbio.tree import nj

dm = DistanceMatrix.from_iterable(msa, metric=hamming, keys=msa.index)
tree = nj(dm)
new_root = tree.find('hemoglobin-human').ancestors()[0]
outgroup_rooted_tree = tree.root_at(new_root)
sequence_order = [t.name for t in outgroup_rooted_tree.tips()]


We can then pass that order in, and we'll see that more similar organisms are now better grouped then they were above. Similarly, the vertical bands are now more consistent in color, since each pair of adjacent rows in the matrix are now from organisms that diverged from one another more recently.

In [6]:
msa_to_heatmap(msa, hydrophobicity_idx, legend_labels=hydrophobicity_labels, sequence_order=sequence_order)


## Filtering alignments¶

The TabularMSA object supports a variety of slicing operations, allowing users to filter sequences and/or positions from the alignment. The examples in this notebook only illustrate a couple of ways to slice a TabularMSA: using a boolean vector and labels. Briefly, a boolean vector is a 1-D array_like of boolean values identifying sequences or positions to retain or discard. For more examples of supported slicing operations, see TabularMSA.loc for label-based slicing and TabularMSA.iloc for index position-based slicing. In the following examples, we use TabularMSA.__getitem__ (i.e. the [] syntax) to slice with boolean vectors. This is simply an alias for TabularMSA.iloc. Slicing by a boolean vector can be performed equivalently using [] syntax (as illustrated below), TabularMSA.loc, or TabularMSA.iloc.

Imagine you wanted to exclude the perfectly conserved and most variable positions in the alignment for a phylogenetic reconstruction process. You might want to do this because the most variable positions may be ones that are essentially unconserved, and therefore are only noise for the tree building process. The most perfectly conserved positions, on the other hand, don't contain any useful information about the relationships between the organisms, so can only increase the runtime of phylogenetic reconstruction without improving the quality of the tree.

We can perform conservation-based positional filtering by making use of TabularMSA.conservation, which will provide a measure of conservation for each position in the alignment. Positional conservation is discussed in more detail in the neighbor joining recipe.

First, compute conservation for each position in the alignment:

In [7]:
positional_conservation = msa.conservation(metric='inverse_shannon_uncertainty', degenerate_mode='nan', gap_mode='nan')


Next, use the positional conservation array to create a boolean vector indicating positions to retain or discard, and slice the second axis (positions) with the boolean vector:

In [8]:
filtered_msa = msa[:, (positional_conservation >= 0.15) & (positional_conservation < 1.0)]


After this process we have only 119 positions of our original 154 positions. We can visualize this to see that we no longer have any perfectly conserved positions, and the least conserved positions have also been filtered out.

In [9]:
filtered_msa

Out[9]:
TabularMSA[Protein]
-----------------------------------------------------------------------
Stats:
sequence count: 43
position count: 119
-----------------------------------------------------------------------
HTPEKSATALGNVDEVGEALGRLLVVYWQRFES ... GNVLVCVLHHFGKTPPVAYQKVVAGVANALHKH
...
GSDDWHHLGIAEPDLSQEVIIRLFQVHEQERAK ... CEIIVKVIEKHPSGADSAMRKALELFRNDMSKK
In [10]:
msa_to_heatmap(filtered_msa, hydrophobicity_idx, legend_labels=hydrophobicity_labels, sequence_order=sequence_order)


We can also filter sequences from the alignment. For example, maybe we want to exclude our hemoglobin sequence, since it's a paralog of all of the other sequences in the alignment. We can do that by finding the name of the sequence (it's in our sequence_order list and in msa.index). Rather than having to pass the names of the sequences we want to keep, we can create a boolean vector excluding the single hemoglobin sequence and slice the alignment with it:

In [11]:
myoglobin_msa = msa[msa.index != 'hemoglobin-human']

In [12]:
msa_to_heatmap(myoglobin_msa, hydrophobicity_idx, legend_labels=hydrophobicity_labels, sequence_order=sequence_order[1:])


Similarly, if we want to keep only the sequences in some specific taxonomic group, we could identify those names and slice using TabularMSA.loc, which performs label-based slicing. Here we'll get the alignment of only the whale sequences.

In [13]:
whale_sequence_ids = [e for e in sequence_order if e.endswith('whale')]
whale_msa = msa.loc[whale_sequence_ids]

In [14]:
msa_to_heatmap(whale_msa, hydrophobicity_idx, legend_labels=hydrophobicity_labels, sequence_order=whale_sequence_ids)