| jupytext | kernelspec | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
:tags: [remove-cell]
import msprime
import demes
def whatis_example():
demes_yml = """\
description:
Asymmetric migration between two extant demes.
time_units: generations
defaults:
epoch:
start_size: 5000
demes:
- name: Ancestral_population
epochs:
- end_time: 1000
- name: A
ancestors: [Ancestral_population]
- name: B
ancestors: [Ancestral_population]
epochs:
- start_size: 2000
end_time: 500
- start_size: 400
end_size: 10000
migrations:
- source: A
dest: B
rate: 1e-4
"""
with open("data/whatis_example.yml", "wt") as f:
f.write(demes_yml)
graph = demes.loads(demes_yml)
demography = msprime.Demography.from_demes(graph)
# Choose seed so num_trees=3, tips are in same order,
# first 2 trees are topologically different, and all trees have the same root
seed = 12581
ts = msprime.sim_ancestry(
samples={"A": 2, "B": 3},
demography=demography,
recombination_rate=1e-8,
sequence_length=1000,
random_seed=seed)
# Mutate
# Choose seed to give 12 muts, last one above node 14
seed = 1476
ts = msprime.sim_mutations(ts, rate=1e-7, random_seed=seed)
ts.dump("data/whatis_example.trees")
def create_notebook_data():
whatis_example()
# create_notebook_data() # uncomment to recreate the tree seqs used in this notebook
(sec_what_is)=
A succinct tree sequence, or "tree sequence" for short, represents the ancestral relationships between a set of DNA sequences. Tree sequences are based on fundamental biological principles of inheritance, DNA duplication, mutation, and recombination; they can be created by evolutionary simulation or by inferring genealogies from empirical DNA data.
:::{margin} Key point Tree sequences are used to encode and analyse large genetic datasets :::
Tree sequences provide an efficient way of storing genetic variation data, and can power analyses of millions of whole genomes. Plots (a) and (b) below summarize these aspects (see additional details on storage and compute further down).
:"tags": ["remove-input"]
# This cell deliberately removed (not just hidden via a toggle) as it's not helpful
# for understanding tskit code (it's merely plotting code)
import matplotlib_inline
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
matplotlib_inline.backend_inline.set_matplotlib_formats('svg')
data1 = np.genfromtxt("data/storing_everyone.csv", delimiter=",", usecols=np.arange(1,12), names=True)
data2 = np.genfromtxt("data/benchmarks_without_copy_longer_genome.txt", encoding=None, names=True, dtype=None)
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16, 4.5))
fig.subplots_adjust(wspace=0.5, left=0, right=1)
keep = data1['sample_size'] <= 1e6
x, y = data1['sample_size'][keep], data1['tsk_fit'][keep]/data1['vcf_fit'][keep]
ax1.spines["top"].set_visible(False)
ax1.spines["right"].set_visible(False)
ax1.loglog(x, y, c="C0", linewidth=4)
ax1.set_xlabel('# of 100Mb genomes', fontsize=18)
ax1.set_ylabel('Size of tree sequence\nfile (relative to VCF) ', fontsize=18)
ax1.tick_params(axis="both", labelsize=16)
txt = ax1.text(0.5, 1.3, "(a) Storing a million genomes as a tree sequence takes thousands of times less disk space",
ha='center', va='top', transform=ax1.transAxes, wrap=True, size=24)
txt._get_wrap_line_width = lambda: 600
ts_time = {n: t for s, n, t in data2[['toolkit','nsam','seconds']] if s == 'tskit'}
libseq_time = {n: t for s, n, t in data2[['toolkit','nsam','seconds']] if s == 'libseq'}
x = np.unique(list(ts_time.keys()) + list(libseq_time.keys()))
y = np.array([libseq_time[time]/ts_time[time] for time in x])
ax2.spines["top"].set_visible(False)
ax2.spines["right"].set_visible(False)
ax2.loglog(x, y, linewidth=4)
ax2.set_xlabel("# of genomes", fontsize=18)
ax2.set_ylabel("Tajima's D calculations per\nunit time (relative to libseq)", fontsize=18)
ax2.tick_params(axis="both", labelsize=16)
txt = ax2.text(0.5, 1.3, "(b) Genetic calculations on millions of genomes can be sped up by many orders of magnitude",
ha='center', va='top', transform=ax2.transAxes, wrap=True, size=24
)
txt._get_wrap_line_width = lambda: 600
plt.show()
(sec_what_is_local_trees)=
As the name suggests, the simplest way to think about a tree sequence is that it
describes a sequence of correlated "local trees" --- i.e. genetic trees located at
different points along a chromosome.
Here's a tiny example based on ten haploid genomes,
:"tags": ["hide-input"]
import string
import tskit
mutated_ts = tskit.load("data/whatis_example.trees")
ts = mutated_ts.delete_sites(list(range(mutated_ts.num_sites)))
# Extra code to label and order the tips alphabetically rather than numerically
labels = {i: string.ascii_lowercase[i] for i in range(ts.num_nodes)}
genome_order = [n for n in ts.first().nodes(order="minlex_postorder") if ts.node(n).is_sample()]
labels.update({n: labels[i] for i, n in enumerate(genome_order)})
style1 = (
".node:not(.sample) > .sym, .node:not(.sample) > .lab {visibility: hidden;}"
".mut {font-size: 12px} .y-axis .tick .lab {font-size: 85%}")
sz = (800, 250) # size of the plot, slightly larger than the default
ticks = [0, 5000, 10000, 15000, 20000]
ts.draw_svg(
size=sz, node_labels=labels, style=style1, y_label="Time ago",
y_axis=True, y_ticks=ticks)
::::{margin}
:::{note}
For clarity in these examples, we are using letters to label nodes. Normally, however,
the nodes are referred to by {ref}numerical ID<sec_terminology_nodes>.
:::
::::
The tickmarks on the X axis and background shading indicate the genomic positions covered
by the trees. The tickmarks indicate recombination events that explain relationships
between the ten genomes. There were two such recombination events, giving us three local trees.
For the first short portion of the chromosome, from the start until position 189,
the relationships between the ten genomes are shown by the first tree.
The second tree shows the relationships between positions 189 and 546.
By inspecting the first and the second local tree we can see that genomes
(sec_what_is_genealogical_network)=
In fact, succinct tree sequences don't store each tree separately, but instead are based on an interconnected genetic genealogy, in which genetic recombination has led to different regions of the chromosome having different histories. Another way of thinking about the tree sequence above is that it describes the full genetic ancestry of our 10 genomes.
(sec_what_is_dna_data)=
A tree sequence can be used to describe patterns of genetic variation by combining the trees with a knowledge of where mutations occur on their branches. Here's how that might look in our simple example:
:"tags": ["hide-input"]
mut_labels = {} # An array of labels for the mutations, listing position & allele change
l = "{:g} ({}→{})"
for mut in mutated_ts.mutations(): # This entire loop is just to make pretty labels
site = mutated_ts.site(mut.site)
older_mut = mut.parent >= 0 # is there an older mutation at the same position?
prev = mutated_ts.mutation(mut.parent).derived_state if older_mut else site.ancestral_state
mut_labels[mut.id] = l.format(site.position, prev, mut.derived_state)
mutated_ts.draw_svg(
size=sz, style=style1, node_labels=labels, mutation_labels=mut_labels)
There are now twelve single nucleotide mutations in the tree sequence. They are shown on the branches of the trees, and the positions of the twelve variable sites associated with the mutations are shown along the X axis.
:::{margin} Key point Mutation on trees are the source of genetic variation :::
The trees inform us that, for example, the final mutation (at position 987) is inherited
by genomes
:"tags": ["hide-input"]
haplotypes = [" ".join(h) for h in mutated_ts.haplotypes()]
print("Position: " + "".join(f"{s.position:^4g}" for s in mutated_ts.sites()))
print("\n".join(sorted(
[f"Genome {labels[i]}: {h}" for i, h in zip(mutated_ts.samples(), haplotypes)])))
This approach scales effectively to millions of genomes, and to chromosomes of hundreds of megabases in length. The ability to deal with huge datasets comes down to one key feature of genomic data: adjacent trees along a chromosome are highly correlated, that is, they share structure. In our example this becomes evident if we highlight the branches ("edges" in tree sequence terminology) that remain unchanged between the first and the second tree.
(fig_what_is_edge_diffs)=
:"tags": ["hide-input"]
# Highlight certain edges in certain trees. Other visualization possibilities in tutorials/viz.html
kept_edges = [e for e in ts.edges() if e.left==0 and e.right>ts.breakpoints(True)[1]]
style3 = (
",".join(f"#svg1 .tree:not(.t2) .node.a{e.parent}.n{e.child} > .edge" for e in kept_edges)
+ "{stroke:#00DD00; stroke-width: 2px}"
+ style1)
ts.draw_svg(
size=(500, 250), x_lim=(0, 500), root_svg_attributes={'id':'svg1'}, y_ticks=ticks,
node_labels=labels, style=style3)
:::{margin} Key point Tree sequences are efficient because they don't store each tree separately :::
A branch can be shared by many adjacent trees, but is stored as a single edge in the tree sequence. For large datasets this is a great saving, because typically each tree-change affects only a few branches at a time, regardless of the tree size.
Below is an extension of the plot at the top of this page, showing predicted file sizes when storing not just millions, but billions of human-like genomes: enough to encompass every human on the planet. This demonstrates that the tree sequence encoding leads to savings of many orders of magnitude, even when compared against compressed versions of the standard VCF storage format (original published data here). It's also worth noting that the efficiency extends to processing time too: tree sequences are often several orders of magnitude faster to process than other storage formats.
(plot_storing_everyone)=
:"tags": ["remove-input"]
# This cell deliberately removed (not just hidden via a toggle) as it's not helpful
# for understanding tskit code (it's merely plotting code)
x = data1['sample_size']
fig, ax1 = plt.subplots(1, figsize=(10, 4))
ax1.spines["top"].set_visible(False)
ax1.spines["right"].set_visible(False)
plt.loglog(x, data1['vcf_fit'], c="C1", label="VCF", linewidth=2)
plt.loglog(x, data1['vcfz_fit'], c="C1", label="compressed VCF", linewidth=2, linestyle=":")
plt.loglog(x, data1['tsk_fit'], c="C0", label="tree sequence", linewidth=2)
plt.loglog(x, data1['tskz_fit'], c="C0", label="compressed tree sequence", linewidth=2, linestyle=":")
plt.xlabel('Number of 100Mb genomes (log scale)', fontsize=12)
plt.ylabel('Space required (GB, log scale)', fontsize=12)
plt.text(16e9, 0.001, 'Size of\nentire\nhuman\npopulation', ha="center", va="bottom", size=14)
plt.annotate('', xy=(16e9, 0.0001), xytext=(16e9, 0.001),
arrowprops=dict(facecolor='black', shrink=0))
plt.legend()
plt.show()
(sec_what_is_ancestry)=
::::{margin}
:::{note}
The genetic genealogy is sometimes referred to as an ancestral recombination graph,
or ARG, and one way to think of tskit tree sequence is as a way
to store various different sorts of ARGs (see the {ref}ARG tutorial<sec_args>)
:::
::::
Often, we're not interested so much in the DNA sequence data as the genetic ancestry
itself (discussed e.g. here).
In other words, the main consideration is the actual trees in a tree sequence, rather
than the distributions of mutations placed upon them --- indeed in genetic simulations, it
{ref}may not be necessary<sec_tskit_no_mutations> to incorporate neutral mutations at all.
The trees reflect, for example, the origin and age of alleles under
selection, the spatial structure of populations, and the effects
of hybridization and admixture in the past.
The tree sequence in this tutorial was actually generated using a model of population
splits and expansions as shown in the following schematic,
{ref}plotted<sec_tskit_viz_other_demographic> using the
DemesDraw package. Our 10 genomes were sampled
from modern day populations A (a constant-size population) and B (a recently expanding
one), where limited migration is occuring from A to B.
:"tags": ["remove-input"]
# This cell deliberately removed (not just hidden via a toggle) as it's not helpful
# for understanding tskit code (it's merely plotting code taken from the demesdraw docs)
import demes
import demesdraw
def size_max(graph):
return max(
max(epoch.start_size, epoch.end_size)
for deme in graph.demes
for epoch in deme.epochs
)
graph = demes.load("data/whatis_example.yml")
w = 1.5 * size_max(graph)
positions = dict(Ancestral_population=0, A=-w, B=w)
fig, ax = plt.subplots(1, figsize=(5, 3))
ax = demesdraw.tubes(graph, ax=ax, positions=positions, seed=1)
plt.show(ax.figure)
A major benefit of "tree sequence thinking" is the close relationship between the
tree sequence and the underlying biological processes that produced the genetic
sequences in the first place, such as those pictured in the demography above. For
example, each branch point (or "internal node") in one of our trees can be
imagined as a genome which existed at a specific time in the past, and
which is a MRCA of the descendant genomes at that position on the chromosome.
We can mark these extra "ancestral genomes" on our tree diagrams with circular symbols,
distinguishing them from the sampled genomes (
:"tags": ["hide-input"]
colours = {0: "#1f77b4", 1: "#ff7f0e", 2: "#2ca02c"}
style2 = ".y-axis .tick .lab {font-size: 85%}"
style2 += "#svg2 .node > .sym {visibility: visible;}" # force-show all nodes: not normally needed
style2 += "".join([f".p{n.population} > .sym {{fill: {colours[n.population]}}}" for n in ts.nodes()])
mutated_ts.draw_svg(
size=sz, root_svg_attributes={'id':'svg2'}, y_label="Time ago (generations)",
y_axis=True, y_ticks=ticks, node_labels=labels, mutation_labels={}, style=style2)
The diagram shows that most of the ancestral genomes
:"tags": ["hide-input"]
import numpy as np
tables = mutated_ts.dump_tables()
# Flip sample and nonsample flags, making the haplotypes() method print out nonsample nodes
s_flags = tables.nodes.flags[ts.samples()[0]]
no_flags = s_flags-s_flags
tables.nodes.flags = np.where(tables.nodes.flags & tskit.NODE_IS_SAMPLE, no_flags, s_flags)
ts_flipped = tables.tree_sequence()
haplotypes = [" ".join(h) for h in ts_flipped.haplotypes(missing_data_character=" ")]
print(" " * ts_flipped.num_sites, " " * (ts_flipped.num_sites-4), "")
print(
"||ANCESTRAL GENOMES|| Position:",
"".join(f"{s.position:^4g}" for s in ts_flipped.sites()))
print(
"\n".join(reversed(sorted([
f"Genome {labels[i]} ({ts.node(i).time:7.1f} {ts_flipped.time_units} ago): {h}"
for i, h in zip(ts_flipped.samples(), haplotypes)]))))
You can see that some ancestors are missing genomic regions, because those parts of their genome have not been inherited by any of the sampled genomes. In other words, that ancestral node is not present in the corresponding local tree.
(sec_what_is_analysis)=
Using tree structures is a common way to implement efficient computer algorithms, and many phylogenetic methods use the structure provided by the evolutionary tree to implement efficient dynamic programming algorithms. The tree sequence structure allows these approaches to be extended to the particular form of phylogenetic network defined by multiple correlated trees along a genome.
Most genetic calculations involve iterating over trees, which is highly efficient in
{program}`tskit`
For example, statistical measures of genetic variation can be thought of as a calculation combining the local trees with the mutations on each branch (or, often preferably, the length of the branches: see this summary). Because a tree sequence is built on a set of small branch changes along the chromosome, statistical calculations can often be updated incrementally as we move along the genome, without having to perform the calculation de novo on each tree. Using tree sequences can result in speed-ups of many orders of magnitude when perfoming calculations on large datasets, as in this example of calculating Tajima's D (from here):
(plot_incremental_calculation)=
:"tags": ["remove-input"]
# This cell deliberately removed (not just hidden via a toggle) as it's not helpful
# for understanding tskit code (it's merely plotting code)
ts_time = np.array([[n,t] for s, n, t in data2[['toolkit','nsam','seconds']] if s == 'tskit'])
ska_time = np.array([[n, t] for s, n, t in data2[['toolkit','nsam','seconds']] if s == 'allel'])
libseq_time = np.array([[n, t] for s, n, t in data2[['toolkit','nsam','seconds']] if s == 'libseq'])
fig, ax1 = plt.subplots(1, figsize=(10, 5))
ax1.spines["top"].set_visible(False)
ax1.spines["right"].set_visible(False)
ax1.loglog(ska_time[:,0], ska_time[:,1], c="C3", linewidth=2, label="scikit-allel library")
ax1.loglog(libseq_time[:,0], libseq_time[:,1], c="C1", linewidth=2, label="libseq library")
ax1.loglog(ts_time[:,0], ts_time[:,1], c="C0", linewidth=2, label="tskit library")
ax1.set_ylabel("Time to calculate Tajima's D (secs/site)", fontsize=12)
ax1.set_xlabel("Number of sampled genomes", fontsize=12)
plt.legend()
plt.show()
The {program}tskit library has {ref}extensive support<sec_analysing_tree_sequences>
for these sorts of population genetic calculations. It provides efficient methods for
traversing through large {ref}trees<sec_analysing_trees_traversals> and
{ref}tree sequences<sec_processing_trees>, as well as providing other
phylogenetically relevant methods such as
{ref}parsimonious placement of mutations<sec_analysing_trees_parsimony>,
and the {ref}counting of topologies<sec_counting_topologies> embedded within
larger trees.
If you are new to tree sequences, and want to start finding out about {program}tskit,
you might now want to continue to the next tutorial: {ref}sec_terminology_and_concepts.
- Jump straight in: if you already have a tree sequence you wish to deal with, the
{ref}
sec_tskit_getting_startedtutorial show you how to do a number of common tasks. - How is a tree sequence stored: details in the {ref}
sec_tablestutorial - The offical {program}
tskitdocumentation