With reference mapped RAD loci you can select windows of loci located close together on scaffolds and automate extracting and filtering and concatenating the RAD data to write to phylip format (see also the window_extracter
tool.) The treeslider
tool here automates this process across many windows, distributes the tree inference jobs in parallel, and organizes the results.
Key features:
clade_weights
).# conda install ipyrad -c bioconda
# conda install raxml -c bioconda
# conda install toytree -c eaton-lab
import ipyrad.analysis as ipa
import toytree
The treeslider()
tool takes the .seqs.hdf5
database file from ipyrad as its input file. Select scaffolds by their index (integer) which can be found in the .scaffold_table
.
# the path to your HDF5 formatted seqs file
data = "/home/deren/Downloads/ref_pop2.seqs.hdf5"
# check scaffold idx (row) against scaffold names
ipa.treeslider(data).scaffold_table.head()
scaffold_name | scaffold_length | |
---|---|---|
0 | Qrob_Chr01 | 55068941 |
1 | Qrob_Chr02 | 115639695 |
2 | Qrob_Chr03 | 57474983 |
3 | Qrob_Chr04 | 44977106 |
4 | Qrob_Chr05 | 70629082 |
Here I select the scaffold Qrob_Chr03 (scaffold_idx
=2), and run 2Mb windows (window_size
) non-overlapping (2Mb slide_size
) across the entire scaffold. I use the default inference method "raxml", and modify its default arguments to run 100 bootstrap replicates. More details on modifying raxml params later. I set for it to skip windows with <10 SNPs (minsnps
), and to filter sites within windows (mincov
) to only include those that have coverage across all 9 clades, with samples grouped into clades using an imap
dictionary.
# select a scaffold idx, start, and end positions
ts = ipa.treeslider(
name="test2",
data="/home/deren/Downloads/ref_pop2.seqs.hdf5",
workdir="analysis-treeslider",
scaffold_idxs=2,
window_size=250000,
slide_size=250000,
inference_method="raxml",
inference_args={"N": 100, "T": 4},
minsnps=10,
consensus_reduce=True,
mincov=5,
imap={
"reference": ["reference"],
"virg": ["TXWV2", "LALC2", "SCCU3", "FLSF33", "FLBA140"],
"mini": ["FLSF47", "FLMO62", "FLSA185", "FLCK216"],
"gemi": ["FLCK18", "FLSF54", "FLWO6", "FLAB109"],
"bran": ["BJSL25", "BJSB3", "BJVL19"],
"fusi-N": ["TXGR3", "TXMD3"],
"fusi-S": ["MXED8", "MXGT4"],
"sagr": ["CUVN10", "CUCA4", "CUSV6"],
"oleo": ["CRL0030", "HNDA09", "BZBB1", "MXSA3017"],
},
)
ts.show_inference_command()
/home/deren/miniconda3/envs/py36/bin/raxmlHPC-PTHREADS-AVX2 -f a -T 2 -m GTRGAMMA -n ... -w ... -s ... -p 54321 -N 100 -x 12345
ts.run(auto=True, force=True)
Parallel connection | latituba: 8 cores building database: nwindows=229; minsnps=10 [####################] 100% 0:02:45 | inferring trees
The main result of a tree slider analysis is the tree_table
. This is a pandas dataframe that includes information about the size and informativeness of each window in addition to the inferred tree for that window. This table is also saved as a CSV file. You can later re-load this CSV to perform further analysis on the tree results. For example, see the clade_weights
tool for how to analyze the support for clades throughout the genome, or see the example tutorial for running ASTRAL species tree or SNAQ species network analyses using the list of trees inferred here.
ts.tree_table.head()
scaffold | start | end | sites | snps | samples | missing | tree | |
---|---|---|---|---|---|---|---|---|
0 | 2 | 0 | 250000 | 186 | 7 | 9 | 0.00 | NaN |
1 | 2 | 250000 | 500000 | 3782 | 54 | 9 | 0.01 | (fusi-S:0.00462226,fusi-N:0.00519121,(bran:0.0... |
2 | 2 | 500000 | 750000 | 994 | 14 | 9 | 0.00 | (fusi-S:0.00202972,sagr:0.00866641,((fusi-N:0.... |
3 | 2 | 750000 | 1000000 | 1652 | 24 | 9 | 0.01 | (fusi-S:0.00137008,virg:0.00843241,((mini:0.00... |
4 | 2 | 1000000 | 1250000 | 1468 | 37 | 9 | 0.01 | (sagr:0.00593372,(fusi-N:0.00956099,(fusi-S:0.... |
Some windows in your analysis may not include a tree if for example there was too much missing data or insufficient information in that region. You can use pandas masking like below to filter based on various criteria.
# example: remove any rows where the tree is NaN
df = ts.tree_table.loc[ts.tree_table.tree.notna()]
mtre = toytree.mtree(df.tree)
mtre.treelist = [i.root("reference") for i in mtre.treelist]
mtre.draw_tree_grid(
nrows=3, ncols=4, start=20,
tip_labels_align=True,
tip_labels_style={"font-size": "9px"},
);
# select a scaffold idx, start, and end positions
ts = ipa.treeslider(
name="test",
data="/home/deren/Downloads/ref_pop2.seqs.hdf5",
workdir="analysis-treeslider",
scaffold_idxs=2,
window_size=1000000,
slide_size=1000000,
inference_method="mb",
inference_args={"N": 0, "T": 4},
minsnps=10,
mincov=9,
consensus_reduce=True,
imap={
"reference": ["reference"],
"virg": ["TXWV2", "LALC2", "SCCU3", "FLSF33", "FLBA140"],
"mini": ["FLSF47", "FLMO62", "FLSA185", "FLCK216"],
"gemi": ["FLCK18", "FLSF54", "FLWO6", "FLAB109"],
"bran": ["BJSL25", "BJSB3", "BJVL19"],
"fusi-N": ["TXGR3", "TXMD3"],
"fusi-S": ["MXED8", "MXGT4"],
"sagr": ["CUVN10", "CUCA4", "CUSV6"],
"oleo": ["CRL0030", "HNDA09", "BZBB1", "MXSA3017"],
},
)
# select a scaffold idx, start, and end positions
ts = ipa.treeslider(
name="test",
data="/home/deren/Downloads/ref_pop2.seqs.hdf5",
workdir="analysis-treeslider",
scaffold_idxs=2,
window_size=2000000,
slide_size=2000000,
inference_method="raxml",
inference_args={"N": 100, "T": 4},
minsnps=10,
mincov=9,
imap={
"reference": ["reference"],
"virg": ["TXWV2", "LALC2", "SCCU3", "FLSF33", "FLBA140"],
"mini": ["FLSF47", "FLMO62", "FLSA185", "FLCK216"],
"gemi": ["FLCK18", "FLSF54", "FLWO6", "FLAB109"],
"bran": ["BJSL25", "BJSB3", "BJVL19"],
"fusi-N": ["TXGR3", "TXMD3"],
"fusi-S": ["MXED8", "MXGT4"],
"sagr": ["CUVN10", "CUCA4", "CUSV6"],
"oleo": ["CRL0030", "HNDA09", "BZBB1", "MXSA3017"],
},
)
You can examine the command that will be called on each genomic window. By modifying the inference_args
above we can modify this string. See examples later in this tutorial.
# this is the tree inference command that will be used
ts.show_inference_command()
/home/deren/miniconda3/envs/py36/bin/raxmlHPC-PTHREADS-AVX2 -f a -T 2 -m GTRGAMMA -n ... -w ... -s ... -p 54321 -N 100 -x 12345
To run the command on every window across all available cores call the .run()
command. This will automatically save checkpoints to a file of the tree_table as it runs, and can be restarted later if it interrupted.
ts.run(auto=True, force=True)
Parallel connection | latituba: 8 cores building database: nwindows=28; minsnps=10 [########### ] 57% 0:27:20 | inferring trees Keyboard Interrupt by user Error: ipcluster shutdown and must be restarted
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <ipython-input-7-570f9190531f> in <module> ----> 1 ts.run(auto=True, force=True) ~/Documents/ipyrad/ipyrad/analysis/treeslider.py in run(self, ipyclient, force, show_cluster, auto) 382 rkwargs={"force": force}, 383 ) --> 384 pool.wrap_run() 385 386 ~/Documents/ipyrad/ipyrad/core/Parallel.py in wrap_run(self, dry_run) 348 # cancel/kill any unfinished jobs and shutdown hub if 'auto=True' 349 finally: --> 350 self.cleanup() 351 352 # print traceback and exit if CLI, just print if API ~/Documents/ipyrad/ipyrad/core/Parallel.py in cleanup(self) 385 # Shutdown the hub if it was auto-launched or broken 386 if self.auto: --> 387 self.ipyclient.shutdown(hub=True, block=False) 388 self.ipyclient.close() 389 if self.show_cluster: ~/miniconda3/envs/py36/lib/python3.6/site-packages/ipyparallel/client/client.py in shutdown(self, targets, restart, hub, block) 1310 if block or hub: 1311 for f in futures: -> 1312 f.wait() 1313 msg = f.result() 1314 if msg['content']['status'] != 'ok': ~/miniconda3/envs/py36/lib/python3.6/site-packages/ipyparallel/client/futures.py in wait(self, timeout) 26 def wait(self, timeout=None): 27 if not self.done(): ---> 28 return self._evt.wait(timeout) 29 return True 30 ~/miniconda3/envs/py36/lib/python3.6/threading.py in wait(self, timeout) 549 signaled = self._flag 550 if not signaled: --> 551 signaled = self._cond.wait(timeout) 552 return signaled 553 ~/miniconda3/envs/py36/lib/python3.6/threading.py in wait(self, timeout) 293 try: # restore state no matter what (e.g., KeyboardInterrupt) 294 if timeout is None: --> 295 waiter.acquire() 296 gotit = True 297 else: KeyboardInterrupt:
Our goal is to fill the .tree_table
, a pandas DataFrame where rows are genomic windows and the information content of each window is recorded, and a newick string tree is inferred and filled in for each. The tree table is also saved as a CSV formatted file in the workdir. You can re-load it later using Pandas. Below I demonstrate how to plot results from the tree_able. To examine how phylogenetic relationships vary across the genome see also the clade_weights()
tool, which takes the tree_table as input.
# the tree table is automatically saved to disk as a CSV during .run()
ts.tree_table.head()
scaffold | start | end | sites | snps | samples | missing | tree | |
---|---|---|---|---|---|---|---|---|
0 | 2 | 0 | 2000000 | 13263 | 155 | 9 | 0.0 | (sagr:0.00343708,(oleo:0.00266064,(mini:0.0020... |
1 | 2 | 2000000 | 4000000 | 10544 | 112 | 9 | 0.0 | (fusi-N:0.00441769,reference:0.0186764,((bran:... |
2 | 2 | 4000000 | 6000000 | 5544 | 46 | 9 | 0.0 | (virg:0.00297301,fusi-N:0.00243431,(oleo:0.003... |
3 | 2 | 6000000 | 8000000 | 12777 | 138 | 9 | 0.0 | (fusi-N:0.00283693,(bran:0.00363545,reference:... |
4 | 2 | 8000000 | 10000000 | 14441 | 166 | 9 | 0.0 | (bran:0.00446094,reference:0.0119105,(fusi-S:0... |
You can select trees from the .tree column of the tree_table and plot them one by one using toytree, or any other tree drawing tool. Below I use toytree to draw a grid of the first 12 trees.
# filter to only windows with >50 SNPS
trees = ts.tree_table[ts.tree_table.snps > 50].tree.tolist()
# load all trees into a multitree object
mtre = toytree.mtree(trees)
# root trees and collapse nodes with <50 bootstrap support
mtre.treelist = [
i.root("reference").collapse_nodes(min_support=50)
for i in mtre.treelist
]
# draw the first 12 trees in a grid
mtre.draw_tree_grid(
nrows=3, ncols=4, start=0,
tip_labels_align=True,
tip_labels_style={"font-size": "9px"},
);
Using toytree you can easily draw a cloud tree of overlapping gene trees to visualize discordance. These typically look much better if you root the trees, order tips by their consensus tree order, and do not use edge lengths. See below for an example, and see the toytree documentation.
# filter to only windows with >50 SNPS (this could have been done in run)
trees = ts.tree_table[ts.tree_table.snps > 50].tree.tolist()
# load all trees into a multitree object
mtre = toytree.mtree(trees)
# root trees
mtre.treelist = [i.root("reference") for i in mtre.treelist]
# infer a consensus tree to get best tip order
ctre = mtre.get_consensus_tree()
# draw the first 12 trees in a grid
mtre.draw_cloud_tree(
width=400,
height=400,
fixed_order=ctre.get_tip_labels(),
use_edge_lengths=False,
);
In this analysis I entered multiple scaffolds to create windows across each scaffold. I also entered a smaller slide size than window size so that windows are partially overlapping. The raxml command string was modified to perform 10 full searches with no bootstraps.
# select a scaffold idx, start, and end positions
ts = ipa.treeslider(
name="chr1_w500K_s100K",
data=data,
workdir="analysis-treeslider",
scaffold_idxs=[0, 1, 2],
window_size=500000,
slide_size=100000,
minsnps=10,
inference_method="raxml",
inference_args={"m": "GTRCAT", "N": 10, "f": "d", 'x': None},
)
# this is the tree inference command that will be used
ts.show_inference_command()
raxmlHPC-PTHREADS-SSE3 -f d -T 2 -m GTRCAT -n ... -w ... -s ... -p 54321 -N 10