AllCoPol: Inferring allele co-ancestry in polyploids
Project description
AllCoPol
AllCoPol is collection of tools for the analysis of polyploids, that allows to infer ancestral allele combinations as well as corresponding subgenome phylogenies.
Installation
AllCoPol is hosted at the Python Package Index (PyPI), so it can be easily installed via pip:
python3 -m pip install allcopol
Contained tools
allcopol.py
This is the main tool of the package implementing heuristic optimization of
ancestral allele combinations. The script requires at least four arguments,
specifying the input files (-A
, -G
), the number of supplied gene trees per marker
(-S
), and the path to a PhyloNet jar file (-P
),
which can can be obtained from https://bioinfocs.rice.edu/phylonet.
Besides, the tabu tenure (-t
) and the number of iterations (-i
) are crucial
parameters, which have to be tuned for proper optimization.
The allele mapping file (-A
) is a tab-delimited text file with one line
per accession and four columns: accession, taxon, allele IDs (comma separated),
and ploidy level.
The gene tree file (-G
) consists of newick strings supplied as one tree per line.
For the trees, which are assumed to be rooted, topologies are sufficient while
edge lengths, support values, etc. will be ignored. Multiple gene trees per
marker can be supplied as consecutive lines in the tree file, e.g.
<tree1 for marker1>
<tree2 for marker1>
<tree3 for marker1>
<tree1 for marker2>
<tree2 for marker2>
<tree3 for marker2>
...
Because the program cannot know from the input file which trees belong to the
same marker, the -S
option has to be set correctly (for the example above: -S 3
).
To get a complete list of program options, type
allcopol.py --help
Minimal example:
mapping.nw (input):
acc1 sp1 A_1,A_2 2
acc2 sp1 B_1,B_2 2
acc3 sp2 C_1,C_2 2
acc4 sp2 D_1,D_2 2
acc5 sp3 E_1,E_2 2
acc6 sp4 F_1,F_2 2
acc7 sp5 G_1,G_2,H_1,H_2 4
trees.nw (input):
(((E_2,(B_1,C_2)),(G_1,F_2)),((H_1,H_2),((A_2,A_1),((G_2,D_2),(F_1,((C_1,B_2),(D_1,E_1)))))));
((((F_2,(G_2,(G_1,F_1))),(A_1,A_2)),((((C_1,C_2),B_1),B_2),(((D_2,D_1),E_1),E_2))),(H_1,H_2));
((((G_2,((F_1,F_2),G_1)),A_1),((H_2,H_1),(D_1,E_1))),(((D_2,E_2),(C_2,(B_2,(C_1,B_1)))),A_2));
(((B_2,(C_2,B_1)),(H_2,H_1)),((A_2,A_1),(((G_2,G_1),(F_2,F_1)),((E_1,D_1),((D_2,E_2),C_1)))));
(((A_1,A_2),(H_2,H_1)),(((E_1,D_1),((F_1,F_2),(G_2,G_1))),(((E_2,D_2),(B_1,(C_2,B_2))),C_1)));
command:
allcopol.py -A mapping.txt -G trees.nw -S 1 -P PhyloNet_3.8.0.jar -t 5 -i 20
Setting the tabu tenure to zero and using reinitialization (-u), it is also possible to perform random restart hillclimbing instead of tabu search. While this avoids extensive parameter tuning, it usually requires a higher number of iterations to obtain satisfactory solutions:
allcopol.py -A mapping.txt -G trees.nw -S 1 -P PhyloNet_3.8.0.jar -t 0 -u 1 -i 100
If runtime is limiting, the number of evaluated solutions per iteration can be limited via the -s option. Note that this may be at the expense of a lower final solution quality.
create_indfile.py
This script takes a number of allele mapping strings (one per line, obtained by multiple runs of allcopol.py based on the same* input files) as input and prints a matrix representation of the inferred allele partitions. The latter can be used as input for Clumpp or align_clusters.py.
* The used gene trees may vary, but the underlying markers and their order in the tree files must be identical.
Example:
mappings.txt (input):
sp2:C_1,C_2;sp3:D_1,D_2;sp1___01:B_1_m0,B_2_m0,B_1_m1,B_2_m1;sp1___02:A_1_m0,A_2_m0,A_1_m1,A_2_m1
sp2:C_1,C_2;sp3:D_1,D_2;sp1___01:A_1_m0,B_2_m0,B_1_m1,B_2_m1;sp1___02:A_2_m0,A_1_m1,A_2_m1,B_1_m0
sp2:C_1,C_2;sp3:D_1,D_2;sp1___01:A_1_m0,A_2_m0,A_1_m1,A_2_m1;sp1___02:B_1_m0,B_2_m0,B_1_m1,B_2_m1
sp2:C_1,C_2;sp3:D_1,D_2;sp1___01:A_1_m0,A_2_m0,A_1_m1,B_2_m1;sp1___02:B_1_m0,B_2_m0,B_1_m1,A_2_m1
command:
create_indfile.py mappings.txt sp1 > example.indfile
The second argument sp1
is the name of the polyploid taxon, whose
pseudo-diploid ancestors have been inferred.
example.indfile (output):
1 1 (x) 1 : 0 1
2 2 (x) 1 : 0 1
3 3 (x) 1 : 0 1
4 4 (x) 1 : 0 1
5 5 (x) 1 : 1 0
6 6 (x) 1 : 1 0
7 7 (x) 1 : 1 0
8 8 (x) 1 : 1 0
1 1 (x) 1 : 1 0
2 2 (x) 1 : 0 1
...
align_clusters.py
This tool can be used to match clusters (pseudo-diploids) among multiple
reconstructions. To avoid getting stuck in a local optimum, a tabu list is
applied, whose size can be specified via the -t
option - unlike allcopol.py,
the heuristic used for this step seems to be relatively robust.
-n
sets the total number of optimization iterations.
Applied to the matrix representation written above, the command
align_clusters.py -n 50 -t 2 example.indfile
creates the output file example.permutations:
2 1
2 1
1 2
1 2
and a second file containing the averaged cluster coefficients (example.clustering).
relabel_trees.py
Using the output of align_clusters.py, the species trees obtained by multiple runs of allcopol.py can be relabeled to mitigate label switching. relabel_trees.py expects three arguments, a file containing the inferred species trees, the name of the analyzed polyploid taxon and a permutation file as written by align_clusters.py.
Example:
sp_trees.nw (input):
(sp3,(sp1___02,(sp1___01,sp2)));
(sp3,(sp1___02,(sp1___01,sp2)));
(sp3,(sp1___01,(sp1___02,sp2)));
(sp3,(sp1___01,(sp1___02,sp2)));
The command
relabel_trees.py sp_trees.nw sp1 example.permutations
yields
(sp3,(sp1_P1,(sp1_P2,sp2)));
(sp3,(sp1_P1,(sp1_P2,sp2)));
(sp3,(sp1_P1,(sp1_P2,sp2)));
(sp3,(sp1_P1,(sp1_P2,sp2)));
Now that the pseudo-diploids are labeled according to their homology, conventional consensus methods can be applied to the trees.
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