def store_data(self, fasta, phylip):
# self.alg_fasta_file = db.add_task_data(self.taskid, DATATYPES.alg_fasta,
# fasta)
# self.alg_phylip_file = db.add_task_data(self.taskid,
# DATATYPES.alg_phylip, phylip)
db.add_task_data(self.taskid, DATATYPES.alg_fasta, fasta)
db.add_task_data(self.taskid, DATATYPES.alg_phylip, phylip)
def finish(self):
# Once executed, alignment is converted into relaxed
# interleaved phylip format.
alg = SeqGroup(os.path.join(self.jobs[0].jobdir, "mcoffee.fasta"))
fasta = alg.write(format="fasta")
phylip = alg.write(format="iphylip_relaxed")
alg_list_string = '\n'.join([pjoin(GLOBALS["input_dir"],
aname) for aname in self.all_alg_files])
db.add_task_data(self.taskid, DATATYPES.alg_list, alg_list_string)
AlgTask.store_data(self, fasta, phylip)
def process_task(task, wkname, npr_conf, nodeid2info):
cogconf, cogclass = npr_conf.cog_selector
concatconf, concatclass = npr_conf.alg_concatenator
treebuilderconf, treebuilderclass = npr_conf.tree_builder
splitterconf, splitterclass = npr_conf.tree_splitter
threadid, nodeid, seqtype, ttype = (task.threadid, task.nodeid,
task.seqtype, task.ttype)
cladeid, targets, outgroups = db.get_node_info(threadid, nodeid)
if not treebuilderclass or task.size < 4:
# Allows to dump algs in workflows with no tree tasks or if tree
# inference does not make sense given the number of sequences. DummyTree
# will produce a fake fully collapsed newick tree.
treebuilderclass = DummyTree
if outgroups and len(outgroups) > 1:
constrain_id = nodeid
else:
constrain_id = None
node_info = nodeid2info[nodeid]
conf = GLOBALS[task.configid]
new_tasks = []
if ttype == "cog_selector":
# Generates a md5 id based on the genetree configuration workflow used
# for the concat alg task. If something changes, concat alg will change
# and the associated tree will be rebuilt
config_blocks = set([wkname])
for key, value in conf[wkname].iteritems():
if isinstance(value, list) or isinstance(value, tuple) \
or isinstance(value, set):
for elem in value:
config_blocks.add(elem[1:]) if isinstance(elem, str) and elem.startswith("@") else None
elif isinstance(value, str):
config_blocks.add(value[1:]) if value.startswith("@") else None
config_checksum = md5(''.join(["[%s]\n%s" %(x, dict_string(conf[x]))
for x in sorted(config_blocks)]))
# THIS PART HAS BEEN MOVED TO COG_SELECTOR TASK
# Check that current selection of cogs will cover all target and
# outgroup species
#cog_hard_limit = int(conf[concatconf]["_max_cogs"])
#sp_repr = defaultdict(int)
#for co in task.raw_cogs[:cog_hard_limit]:
# for sp, seq in co:
# sp_repr[sp] += 1
#missing_sp = (targets | outgroups) - set(sp_repr.keys())
#if missing_sp:
# raise TaskError("missing species under current cog selection: %s" %missing_sp)
#else:
# log.log(28, "Analysis of current COG selection:")
# for sp, ncogs in sorted(sp_repr.items(), key=lambda x:x[1]):
# log.log(28, " % 30s species present in % 6d COGs" %(sp, ncogs))
# register concat alignment task. NodeId associated to concat_alg tasks
# and all its children jobs should take into account cog information and
# not only species and outgroups included.
concat_job = concatclass(task.cogs, seqtype, conf, concatconf,
config_checksum)
db.add_node(threadid,
concat_job.nodeid, cladeid,
targets, outgroups)
# Register Tree constrains
constrain_tree = "(%s, (%s));" %(','.join(sorted(outgroups)),
','.join(sorted(targets)))
_outs = "\n".join(map(lambda name: ">%s\n0" %name, sorted(outgroups)))
_tars = "\n".join(map(lambda name: ">%s\n1" %name, sorted(targets)))
constrain_alg = '\n'.join([_outs, _tars])
db.add_task_data(concat_job.nodeid, DATATYPES.constrain_tree, constrain_tree)
db.add_task_data(concat_job.nodeid, DATATYPES.constrain_alg, constrain_alg)
db.dataconn.commit() # since the creation of some Task objects
# may require this info, I need to commit
# right now.
concat_job.size = task.size
new_tasks.append(concat_job)
elif ttype == "concat_alg":
# register tree for concat alignment, using constraint tree if
# necessary
alg_id = db.get_dataid(task.taskid, DATATYPES.concat_alg_phylip)
try:
parts_id = db.get_dataid(task.taskid, DATATYPES.model_partitions)
except ValueError:
parts_id = None
nodeid2info[nodeid]["size"] = task.size
nodeid2info[nodeid]["target_seqs"] = targets
nodeid2info[nodeid]["out_seqs"] = outgroups
tree_task = treebuilderclass(nodeid, alg_id,
constrain_id, None,
task.seqtype, conf, treebuilderconf,
parts_id=parts_id)
tree_task.size = task.size
new_tasks.append(tree_task)
elif ttype == "tree":
merger_task = splitterclass(nodeid, seqtype, task.tree_file, conf, splitterconf)
merger_task.size = task.size
new_tasks.append(merger_task)
elif ttype == "treemerger":
# Lets merge with main tree
if not task.task_tree:
task.finish()
log.log(24, "Saving task tree...")
annotate_node(task.task_tree, task)
db.update_node(nid=task.nodeid, runid=task.threadid,
newick=db.encode(task.task_tree))
db.commit()
if not isinstance(treebuilderclass, DummyTree) and npr_conf.max_iters > 1:
current_iter = get_iternumber(threadid)
if npr_conf.max_iters and current_iter >= npr_conf.max_iters:
log.warning("Maximum number of iterations reached!")
else:
# Add new nodes
source_seqtype = "aa" if "aa" in GLOBALS["seqtypes"] else "nt"
ttree, mtree = task.task_tree, task.main_tree
log.log(26, "Processing tree: %s seqs, %s outgroups",
len(targets), len(outgroups))
target_cladeids = None
if tobool(conf[splitterconf].get("_find_ncbi_targets", False)):
tcopy = mtree.copy()
ncbi.connect_database()
tax2name, tax2track = ncbi.annotate_tree_with_taxa(tcopy, None)
#tax2name, tax2track = ncbi.annotate_tree_with_taxa(tcopy, "fake") # for testing sptree example
n2content = tcopy.get_cached_content()
broken_branches, broken_clades, broken_clade_sizes, tax2name = ncbi.get_broken_branches(tcopy, n2content)
log.log(28, 'restricting NPR to broken clades: '+
colorify(', '.join(map(lambda x: "%s"%tax2name[x], broken_clades)), "wr"))
target_cladeids = set()
for branch in broken_branches:
print branch.get_ascii(attributes=['spname', 'taxid'], compact=True)
print map(lambda x: "%s"%tax2name[x], broken_branches[branch])
target_cladeids.add(branch.cladeid)
for node, seqs, outs, wkname in get_next_npr_node(task.configid, ttree,
task.out_seqs, mtree, None,
npr_conf, target_cladeids): # None is to avoid alg checks
log.log(24, "Adding new node: %s seqs, %s outgroups",
len(seqs), len(outs))
new_task_node = cogclass(seqs, outs,
source_seqtype, conf, cogconf)
new_task_node.target_wkname = wkname
new_tasks.append(new_task_node)
db.add_node(threadid,
new_task_node.nodeid, new_task_node.cladeid,
new_task_node.targets,
new_task_node.outgroups)
return new_tasks
def store_data(self, cogs, cog_analysis):
db.add_task_data(self.taskid, DATATYPES.cogs, cogs)
db.add_task_data(self.taskid, DATATYPES.cog_analysis, cog_analysis)
self.cogs = cogs
self.cog_analysis = cog_analysis
def store_data(self, fasta, phylip, partitions):
db.add_task_data(self.taskid, DATATYPES.model_partitions, partitions)
db.add_task_data(self.taskid, DATATYPES.concat_alg_fasta, fasta)
db.add_task_data(self.taskid, DATATYPES.concat_alg_phylip, phylip)
def store_data(self, newick, stats):
db.add_task_data(self.taskid, DATATYPES.tree, newick)
db.add_task_data(self.taskid, DATATYPES.tree_stats, stats)
self.stats = stats
def store_data(self, best_model, ranking):
db.add_task_data(self.taskid, DATATYPES.best_model, best_model)
db.add_task_data(self.taskid, DATATYPES.model_ranking, ranking)
self.best_model = best_model
self.model_ranking[:] = []
self.model_ranking.extend(ranking)
def store_data(self, fasta, phylip, kept_columns):
db.add_task_data(self.taskid, DATATYPES.clean_alg_fasta, fasta)
db.add_task_data(self.taskid, DATATYPES.clean_alg_phylip, phylip)
db.add_task_data(self.taskid, DATATYPES.kept_alg_columns, kept_columns)
self.kept_columns[:] = [] # security clear
self.kept_columns.extend(kept_columns)
def store_data(self, msf):
db.add_task_data(self.taskid, DATATYPES.msf, msf)
def process_task(task, wkname, npr_conf, nodeid2info):
alignerconf, alignerclass = npr_conf.aligner
cleanerconf, cleanerclass = npr_conf.alg_cleaner
mtesterconf, mtesterclass = npr_conf.model_tester
treebuilderconf, treebuilderclass = npr_conf.tree_builder
if not treebuilderclass:
# Allows to dump algs in workflows with no tree tasks
treebuilderclass = DummyTree
splitterconf, splitterclass = npr_conf.tree_splitter
conf = GLOBALS[task.configid]
seqtype = task.seqtype
nodeid = task.nodeid
ttype = task.ttype
taskid = task.taskid
threadid = task.threadid
node_info = nodeid2info[nodeid]
size = task.size#node_info.get("size", 0)
target_seqs = node_info.get("target_seqs", [])
out_seqs = node_info.get("out_seqs", [])
if not treebuilderclass or size < 4:
# Allows to dump algs in workflows with no tree tasks or if tree
# inference does not make sense given the number of sequences. DummyTree
# will produce a fake fully collapsed newick tree.
treebuilderclass = DummyTree
mtesterclass = None
# If more than one outgroup are used, enable the use of constrain
if out_seqs and len(out_seqs) > 1:
constrain_id = nodeid
else:
constrain_id = None
new_tasks = []
if ttype == "msf":
# Register Tree constrains
constrain_tree = "(%s, (%s));" %(','.join(sorted(task.out_seqs)),
','.join(sorted(task.target_seqs)))
_outs = "\n".join(map(lambda name: ">%s\n0" %name, sorted(task.out_seqs)))
_tars = "\n".join(map(lambda name: ">%s\n1" %name, sorted(task.target_seqs)))
constrain_alg = '\n'.join([_outs, _tars])
db.add_task_data(nodeid, DATATYPES.constrain_tree, constrain_tree)
db.add_task_data(nodeid, DATATYPES.constrain_alg, constrain_alg)
db.dataconn.commit() # since the creation of some Task
# objects may require this info, I need
# to commit right now.
# Register node
db.add_node(task.threadid,
task.nodeid, task.cladeid,
task.target_seqs,
task.out_seqs)
nodeid2info[nodeid]["size"] = task.size
nodeid2info[nodeid]["target_seqs"] = task.target_seqs
nodeid2info[nodeid]["out_seqs"] = task.out_seqs
alg_task = alignerclass(nodeid, task.multiseq_file,
seqtype, conf, alignerconf)
alg_task.size = task.size
new_tasks.append(alg_task)
elif ttype == "alg" or ttype == "acleaner":
if ttype == "alg":
nodeid2info[nodeid]["alg_path"] = task.alg_fasta_file
elif ttype == "acleaner":
nodeid2info[nodeid]["alg_clean_path"] = task.clean_alg_fasta_file
alg_fasta_file = getattr(task, "clean_alg_fasta_file",
task.alg_fasta_file)
alg_phylip_file = getattr(task, "clean_alg_phylip_file",
task.alg_phylip_file)
# Calculate alignment stats
# cons_mean, cons_std = get_trimal_conservation(task.alg_fasta_file,
# conf["app"]["trimal"])
#
# max_identity = get_trimal_identity(task.alg_fasta_file,
# conf["app"]["trimal"])
# log.info("Conservation: %0.2f +-%0.2f", cons_mean, cons_std)
# log.info("Max. Identity: %0.2f", max_identity)
#import time
#t1 = time.time()
#mx, mn, mean, std = get_identity(task.alg_fasta_file)
#print time.time()-t1
#log.log(26, "Identity: max=%0.2f min=%0.2f mean=%0.2f +- %0.2f",
# mx, mn, mean, std)
#t1 = time.time()
if seqtype == "aa" and npr_conf.switch_aa_similarity < 1:
try:
alg_stats = db.get_task_data(taskid, DATATYPES.alg_stats)
except Exception, e:
alg_stats = {}
if ttype == "alg":
algfile = pjoin(GLOBALS["input_dir"], task.alg_phylip_file)
dataid = DATATYPES.alg_phylip
elif ttype == "acleaner":
algfile = pjoin(GLOBALS["input_dir"], task.clean_alg_phylip_file)
dataid = DATATYPES.clean_alg_phylip
if "i_mean" not in alg_stats:
log.log(24, "Calculating alignment stats...")
# dump data if necesary
algfile = pjoin(GLOBALS["input_dir"], task.alg_phylip_file)
if not pexist(algfile):
# dump phylip alg
open(algfile, "w").write(db.get_data(db.get_dataid(taskid, dataid)))
mx, mn, mean, std = get_statal_identity(algfile,
conf["app"]["statal"])
alg_stats = {"i_max":mx, "i_mean":mean, "i_min":mn, "i_std":std}
db.add_task_data(taskid, DATATYPES.alg_stats, alg_stats)
log.log(22, "Alignment stats (sequence similarity):")
log.log(22, " max: %(i_max)0.2f, min:%(i_min)0.2f, avg:%(i_mean)0.2f+-%(i_std)0.2f" %
(alg_stats))
else:
alg_stats = {"i_max":-1, "i_mean":-1, "i_min":-1, "i_std":-1}
#print time.time()-t1
#log.log(24, "Identity: max=%0.2f min=%0.2f mean=%0.2f +- %0.2f",
# mx, mn, mean, std)
task.max_ident = alg_stats["i_max"]
task.min_ident = alg_stats["i_min"]
task.mean_ident = alg_stats["i_mean"]
task.std_ident = alg_stats["i_std"]
next_task = None
if ttype == "alg" and cleanerclass:
next_task = cleanerclass(nodeid, seqtype, alg_fasta_file,
alg_phylip_file,
conf, cleanerconf)
else:
# Converts aa alignment into nt if necessary
if seqtype == "aa" and \
"nt" in GLOBALS["seqtypes"] and \
task.mean_ident >= npr_conf.switch_aa_similarity:
log.log(28, "@@2:Switching to codon alignment!@@1: amino-acid sequence similarity: %0.2f >= %0.2f" %\
(task.mean_ident, npr_conf.switch_aa_similarity))
alg_fasta_file = "%s.%s" %(taskid, DATATYPES.alg_nt_fasta)
alg_phylip_file = "%s.%s" %(taskid, DATATYPES.alg_nt_phylip)
try:
alg_fasta_file = db.get_dataid(taskid, DATATYPES.alg_nt_fasta)
alg_fasta_file = db.get_dataid(taskid, DATATYPES.alg_nt_phylip)
except ValueError:
log.log(22, "Calculating codon alignment...")
source_alg = pjoin(GLOBALS["input_dir"], task.alg_fasta_file)
if ttype == "alg":
kept_columns = []
elif ttype == "acleaner":
# if original alignment was trimmed, use it as reference
# but make the nt alignment only on the kept columns
kept_columns = db.get_task_data(taskid, DATATYPES.kept_alg_columns)
if not pexist(source_alg):
open(source_alg, "w").write(db.get_task_data(taskid, DATATYPES.alg_fasta))
nt_alg = switch_to_codon(source_alg, kept_columns=kept_columns)
db.add_task_data(taskid, DATATYPES.alg_nt_fasta, nt_alg.write())
db.add_task_data(taskid, DATATYPES.alg_nt_phylip, nt_alg.write(format='iphylip_relaxed'))
npr_conf = IterConfig(conf, wkname, task.size, "nt")
seqtype = "nt"
if mtesterclass:
next_task = mtesterclass(nodeid, alg_fasta_file,
alg_phylip_file,
constrain_id,
conf, mtesterconf)
elif treebuilderclass:
next_task = treebuilderclass(nodeid, alg_phylip_file,
constrain_id,
None, seqtype,
conf, treebuilderconf)
if next_task:
next_task.size = task.size
new_tasks.append(next_task)
