allcopol 0.1.2

AllCoPol: Inferring allele co-ancestry in polyploids

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

To run PhyloNet, Java 1.7 or later has to be installed.

Contained tools

allcopol

This is the main tool of the package implementing heuristic optimization of ancestral allele combinations. It 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 (newest tested version: 3.8.0). 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 --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 -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 -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

This script takes a number of allele mapping strings (one per line, obtained by multiple runs of allcopol 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.

* 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 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

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, 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 -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

Using the output of align_clusters, the species trees obtained by multiple runs of allcopol can be relabeled to mitigate label switching. relabel_trees 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.

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 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.