barcode-constrained-phylogeny
Rules
create_database
Puts relevant BOLD data columns into a custom SQLite database. Uses BOLD dump TSV as defined in config file. Implemented by the create_database.py script.
map_opentol
Enriches the SQLite database with mappings to OpenToL. Because this
operates on the same database file as the previous rule, the output is a 0-byte file
map_opentol.ok to indicate that the task was run. Uses the
Open Tree of Life API
to map BOLD taxon names to Open Tree of Life taxonomy IDs. Implemented by the
map_opentol.py script.
megatree_loader
Loads the configured OpenToL topology in the into the SQLite database. This
is a multi-step process that first processes the tree using the conda package
perl-bio-phylo-forest-dbtree, exports this from a temporary database to an
SQL dump, imports that dump into the central database, and then creates a
0-byte file megatree_loader.ok.
family_fasta
Barcodes from the SQLite database are divided into their taxonomic family
groups and written to FASTA files: ‘fasta/family/{family}.fasta’. This rule
is run in parallel using scattergather and is implemented in the
family_fasta.py script. Note that the parallelization
needs to know ahead of time how many families to expect, which needs to be
specified in the configuration file.
family_constraint
For each family FASTA file generates a constraint tree. The constraints are
generated using the tree indexed by megatree_loader and the family FASTA
files from family_fasta. This rule is also parallelized using scattergather,
and is implemented using the conda package perl-bio-phylo-forest-dbtree.
msa_hmm
Creates a multiple sequence alignment for each family FASTA file using Hidden
Markov Model alignment. Here, the sequences are also corrected for possible
revcom issues. It is possible that this is not needed at all because so far 0
revcom sequences were observed in BOLD. This rule is implemented by the script
msa_hmm.py, which depends on the conda package hmmer,
and is parallelized using scattergather.
prep_raxml
Prepares the aligned ingroup sequences and constraint tree for analysis by
conda package raxml-ng. This step also includes outgroup selection. It is
possible that this rule needs to be reimplemented with an eye on doing
phylogenetic placement (as opposed to constrained tree searching) instead.
This would obviate the need for outgroup selection and for subsequent re-
rooting. The rule is implemented by the script
prep_raxml.py and is parallelized using scattergather.
run_raxml
Runs a phylogenetic analysis for each family FASTA file + constraint tree.
Implemented using the raxml-ng conda package and parallelized using
scattergather. Might be changed to phylogenetic placement.
reroot_raxml_output
Reroots the output from raxml-ng. The reason for this is that RAxML emits
unrooted trees when it runs a tree search. The way in which this is done
is by figuring out what the outgroup taxa were and then rooting the tree
on the smallest bipartition that separates the outgroup from the ingroup,
modifying the root branch length by taking the midpoint. There are several
issues with this, because the midpoint is a crude and possibly inappropriate
assumption and because there are cases where selected outgroups nest inside
the ingroup. Implemented by the script
reroot_backbone.py and parallelized using
scattergather.
choose_exemplars
Chooses two exemplars that represent the ingroup in the backbone topology.
At present these are selected by taking the shallowest tip in the ingroup.
This may be inappropriate: perhaps the tallest tips should be selected, or
the median ones (this needs to be tested empirically). Implemented by the
script choose_exemplars.py and parallelized
using scattergather.
prep_raxml_backbone
Prepares the backbone constraint tree and alignment by merging the exemplars from the separate family-level analyses. Implemented by the script backbone_constraint.py. From this rule onward, the pipeline is no longer parallel.
run_raxml_backbone
Runs the backbone analysis using raxml-ng. This is done by a heuristic
search, which may have to be replaced by a phylogenetic placement operation
instead.
reroot_backbone
Reroots the backbone tree. This is done by taking the input constraint tree and reconciling the resulting backbone with the orientation of the input. Perhaps this is not necessary if we use phylogenetic placement instead. Implemented by the reroot_backbone.py script.
graft_clades
Grafts the family level subtrees onto the backbone. The branch lengths on the backbone and those in the family level trees are on a different order of magnitude, with the (more or less) equivalent branches in the backbone much longer than in the subtrees. This is presumably because the backbone spans a broader taxon sample, which consequently has greater saturation and homoplasy in the alignment, which is accommodated by lengthening branches. How to scale those relative to one another is a challenge to be addressed empirically by testing different exemplar selections and rescaling rules.