Cookbook

Bio.Phylo Cookbook.

Here are some examples of using Bio.Phylo for some likely tasks. Some of these functions might be added to Biopython in a later release, but you can use them in your own code with Biopython 1.54.

Convenience functions

Get the parent of a clade

The Tree data structures in Bio.Phylo don’t store parent references for each clade. Instead, the get_path method can be used to trace the path of parent-child links from the tree root to the clade of choice:

def get_parent(tree, child_clade):
    node_path = tree.get_path(child_clade)
    return node_path[-2]


# Select a clade
myclade = tree.find_clades("foo").next()
# Test the function
parent = get_parent(tree, myclade)
assert myclade in parent

Note that get_path has a linear run time with respect to the size of the tree – i.e. for best performance, don’t call get_parent or get_path inside a time-critical loop. If possible, call get_path outside the loop, and look up parents in the list returned by that function.

Alternately, if you need to repeatedly look up the parents of arbitrary tree elements, create a dictionary mapping all nodes to their parents:

def all_parents(tree):
    parents = {}
    for clade in tree.find_clades(order="level"):
        for child in clade:
            parents[child] = clade
    return parents


# Example
parents = all_parents(tree)
myclade = tree.find_clades("foo").next()
parent_of_myclade = parents[myclade]
assert myclade in parent_of_myclade

Index clades by name

For large trees it can be useful to be able to select a clade by name, or some other unique identifier, rather than searching the whole tree for it during each operation.

def lookup_by_names(tree):
    names = {}
    for clade in tree.find_clades():
        if clade.name:
            if clade.name in names:
                raise ValueError("Duplicate key: %s" % clade.name)
            names[clade.name] = clade
    return names

Now you can retrieve a clade by name in constant time:

tree = Phylo.read("ncbi_taxonomy.xml", "phyloxml")
names = lookup_by_names(tree)
for phylum in ("Apicomplexa", "Euglenozoa", "Fungi"):
    print("Phylum size: %d" % len(names[phylum].get_terminals()))

A potential issue: The above implementation of lookup_by_names doesn’t include unnamed clades, generally internal nodes. We can fix this by adding a unique identifier for each clade. Here, all clade names are prefixed with a unique number (which can be useful for searching, too):

def tabulate_names(tree):
    names = {}
    for idx, clade in enumerate(tree.find_clades()):
        if clade.name:
            clade.name = "%d_%s" % (idx, clade.name)
        else:
            clade.name = str(idx)
        names[clade.name] = clade
    return names

Calculate distances between neighboring terminals

Suggested by Joel Berendzen

import itertools


def terminal_neighbor_dists(self):
    """Return a list of distances between adjacent terminals."""

    def generate_pairs(self):
        pairs = itertools.tee(self)
        pairs[1].next()
        return itertools.izip(pairs[0], pairs[1])

    return [self.distance(*i) for i in generate_pairs(self.find_clades(terminal=True))]

Test for “semi-preterminal” clades

Suggested by Joel Berendzen

The existing tree method is_preterminal returns True if all of the direct descendants are terminal. This snippet will instead return True if any direct descendent is terminal, but still False if the given clade itself is terminal.

def is_semipreterminal(clade):
    """True if any direct descendent is terminal."""
    for child in clade:
        if child.is_terminal():
            return True
    return False

In Python 2.5 and later, this is simplified with the built-in any function:

def is_semipreterminal(clade):
    return any(child.is_terminal() for child in clade)

Comparing trees

TODO:

Symmetric difference / partition metric, a.k.a. topological distance (Robinson-Foulds)
Quartets distance
Nearest-neighbor interchange
Path-length-difference

Consensus methods

TODO:

Majority-rules consensus
Strict consensus
Adams (Adams 1972, pdf)
Asymmetric median tree (Phillips and Warnow 1996)

Rooting methods

The basic method on the Tree class (not TreeMixin) is root_with_outgroup:

tree = Phylo.read("example.nwk", "newick")
print(tree)
# ...
tree.root_with_outgroup({"name": "A"})  # Operates in-place
print(tree)

Normally you’ll want the outgroup to be a monophyletic group, rather than a single taxon. This isn’t automatically checked, but you can do it yourself with the is_monophyletic method.

To save some typing, try keeping the query in a separate list and reusing it:

outgroup = [{"name": taxon_name} for taxon_name in ("E", "F", "G")]
if tree.is_monophyletic(outgroup):
    tree.root_with_outgroup(*outgroup)
else:
    raise ValueError("outgroup is paraphyletic")

TODO:

Root at the midpoint between the two most distant nodes (or “center” of all tips)

Graphics

TODO:

Party tricks with draw methods, covering each keyword argument

Exporting to other types

Convert to an ‘ape’ tree, via Rpy2

The R statistical programming environment provides support for phylogenetics through the ape package and several others that build on top of ape. The Python package rpy2 provides an interface between R and Python, so it’s possible to convert a Bio.Phylo tree into an ape tree object:

import tempfile
from rpy2.robjects import r


def to_ape(tree):
    """Convert a tree to the type used by the R package `ape`, via rpy2.

    Requirements:
        - Python package `rpy2`
        - R package `ape`
    """
    with tempfile.NamedTemporaryFile() as tmpf:
        Phylo.write(tree, tmpf, "newick")
        tmpf.flush()
        rtree = r(
            """
            library('ape')
            read.tree('%s')
            """
            % tmpf.name
        )
    return rtree

See that it works:

>>> from StringIO import StringIO
>>> from Bio import Phylo
>>> tree = Phylo.read(StringIO('(A,(B,C),(D,E));'), 'newick')
>>> rtree = to_ape(tree)
>>> len(rtree)
3
>>> print(r.summary(rtree))
Phylogenetic tree: structure(list(edge = structure(c(6, 6, 7, 7, 6, 8, 8, 1, 7,  2, 3, 8, 4, 5),
 .Dim = c(7L, 2L)), tip.label = c("A", "B", "C",  "D", "E"), Nnode = 3L),
 .Names = c("edge", "tip.label", "Nnode" ), class="phylo")

  Number of tips: 5
  Number of nodes: 3
  No branch lengths.
  No root edge.
  Tip labels: A
              B
              C
              D
              E
  No node labels.
NULL
>>> r.plot(rtree)

See the rpy2 documentation for further guidance.

Convert to a DendroPy or PyCogent tree

The tree objects used by Biopython, DendroPy and PyCogent are different. Nonetheless, all three toolkits support the Newick file format, so interoperability is straightforward at that level by writing to a temporary file or StringIO object with one library, then reading the same string again with another.

from Bio import Phylo
import cogent

Phylo.write(bptree, "mytree.nwk", "newick")  # Biopython tree
ctree = cogent.LoadTree("mytree.nwk")  # PyCogent tree

import dendropy

# Create or load a tree in DendroPy
dtree = dendropy.Tree.get_from_string("(A, (B, C), (D, E))", "newick")
dtree.write_to_path("tmp.nwk", "newick", suppress_rooting=True)
# Load the same tree in Biopython
bptree = Phylo.read("tmp.nwk", "newick")

Convert to a NumPy array or matrix

Adjacency matrix: cells are 1 (true) if a parent-child relationship exists, otherwise 0 (false).

import numpy


def to_adjacency_matrix(tree):
    """Create an adjacency matrix (NumPy array) from clades/branches in tree.

    Also returns a list of all clades in tree ("allclades"), where the position
    of each clade in the list corresponds to a row and column of the numpy
    array: a cell (i,j) in the array is 1 if there is a branch from allclades[i]
    to allclades[j], otherwise 0.

    Returns a tuple of (allclades, adjacency_matrix) where allclades is a list
    of clades and adjacency_matrix is a NumPy 2D array.
    """
    allclades = list(tree.find_clades(order="level"))
    lookup = {}
    for i, elem in enumerate(allclades):
        lookup[elem] = i
    adjmat = numpy.zeros((len(allclades), len(allclades)))
    for parent in tree.find_clades(terminal=False, order="level"):
        for child in parent.clades:
            adjmat[lookup[parent], lookup[child]] = 1
    if not tree.rooted:
        # Branches can go from "child" to "parent" in unrooted trees
        adjmat = adjmat + adjmat.transpose()
    return (allclades, numpy.matrix(adjmat))

Distance matrix: cell values are branch lengths if a branch exists, otherwise infinity (this plays well with graph algorithms).

import numpy


def to_distance_matrix(tree):
    """Create a distance matrix (NumPy array) from clades/branches in tree.

    A cell (i,j) in the array is the length of the branch between allclades[i]
    and allclades[j], if a branch exists, otherwise infinity.

    Returns a tuple of (allclades, distance_matrix) where allclades is a list of
    clades and distance_matrix is a NumPy 2D array.
    """
    allclades = list(tree.find_clades(order="level"))
    lookup = {}
    for i, elem in enumerate(allclades):
        lookup[elem] = i
    distmat = numpy.repeat(numpy.inf, len(allclades) ** 2)
    distmat.shape = (len(allclades), len(allclades))
    for parent in tree.find_clades(terminal=False, order="level"):
        for child in parent.clades:
            if child.branch_length:
                distmat[lookup[parent], lookup[child]] = child.branch_length
    if not tree.rooted:
        distmat += distmat.transpose
    return (allclades, numpy.matrix(distmat))

Enhancements:

Use an OrderedDict for allclades, so the separate dictionary lookup isn’t needed. (Python 2.7+)
Use NumPy’s structured arrays to assign clade names to rows and columns of the matrix, so allclades isn’t needed either (this works nicely along with the tabulate_names function given earlier).

TODO:

Relationship matrix? See Martins and Housworth 2002