phyckle

In the src folder are several scripts to help conduct phylogenomic analyses.

conducting alternative relationship analyses

These analyses require RAxML.

There are several ways that these analyses can be conducted. Once means to compare alternatives is to do the following:

• Create a file that has the alternative bipartitions listed. There is an example in example/test.bp with the focal bipartition on a line and then alternatives listed below preceded by a -.
• Run edge_investigate_conflicts_given.py like phyckle/src/edge_investigate_conflicts_given.py test.bp test.out seqs/ temp/ where temp is an outdir, seqs only has the sequence files (nothing else), test.bp is the set of bipartitions and test.out is an outfile.
• Then run postprocess_edge_investigate.py link python phyckle/src/postprocess_edge_investigate.py test.out temp/ with test.out from above and temp from above.
• This will result in a file called cons_0.csv that has each line with gene,cons_0,cons_0_conf_0,cons_0_conf_1,bestone,best,secondbest,diffbestsecondbest listing the gene, likelihoods for each edge (cons_0 is the main and cons_0_conf_0,cons_0_conf_1 are the two alternatives from an example). best is the highest likelihood with the differences listed.

There are several other analyses that can be conducted but this is the basic set of analyses.

conducting combinability analyses

At some point for this, you will need to use iqtree and bp (from here).

There are two scripts for conducting these analyses currently: test_clusters.py and test_clusters_multi_measure.py. The test_clusters.py will conduct analyses by using the RFW, constructing a graph, and proceeding through the graph. test_clusters_multi_measure.py has additional considerations like penalizing based on poor overlap of taxa (using rfp from bp).

• Conduct iqtree analyses on each gene. You can do this with run_iqtree.py.
• Create a file with the seq filenames in order (ls *.fa > treemap) and place them all in a file in the same order (cat *.treefile > mltrees)
• Probably delete extra files (rm *.bionj *.mldist *.log *.ckp.gz) and move iqtree files and treefiles to the gene directory.
• Calculate the weighted RF for the trees in the tree file (bp -rfw -t mltres > rfw).
• Conduct the clustering analysis python phyckle/src/test_clusters.py -d seqs/ -m treemap -w rfw -e spp -a

Example with pxbdsim and pxseqgen. You can use phyx to generate data that you can use to conduct an example analysis. Here are the commands:

pxbdsim -e 10 | pxtscale -r 1 > test.tre
pxseqgen -t test.tre -l 500 -o test1.fa
pxseqgen -t test.tre -l 500 -o test2.fa
pxseqgen -t test.tre -l 500 -o test3.fa
pxseqgen -t test.tre -l 500 -o test4.fa
pxseqgen -t test.tre -l 500 -o test5.fa
mkdir seqs/
mv *.fa seqs/
python phyckle/src/run_iqtree.py seqs/ test
rm *.bionj *.mldist *.log *.ckp.gz
mv *.treefile *.iqtree seqs/
cd seqs/
ls test*.fa > ../treemap
cat test*.treefile > ../mltrees
cd ../
bp -rfw -t mltrees > rfw
python phyckle/src/test_clusters.py -d seqs/ -m treemap -w rfw -e spp -a

some comments

The runs above switch between iqtree and RAxML. This is primarily because iqtree has several branch length options when concatenating and RAxML is more efficient when conducting constrained analyses.