from htsworkflow.pipelines import desplit_fastq
from htsworkflow.util.rdfhelp import get_model, dump_model, load_into_model, \
fromTypedNode, \
- libraryOntology, \
- stripNamespace, \
- rdfNS
+ stripNamespace
+from htsworkflow.util.rdfns import *
from htsworkflow.util.conversion import parse_flowcell_id
from django.conf import settings
template_map = {'srf': 'srf.condor',
'qseq': 'qseq.condor',
'split_fastq': 'split_fastq.condor',
- 'by_sample': 'lane_to_fastq.turtle',
}
env = None
'qseq': self.condor_qseq_to_fastq,
'split_fastq': self.condor_desplit_fastq
}
- by_sample = {}
sequences = self.find_archive_sequence_files(result_map)
- needed_targets = self.find_missing_targets(result_map, sequences)
+ needed_targets = self.update_fastq_targets(result_map, sequences)
for target_pathname, available_sources in needed_targets.items():
LOGGER.debug(' target : %s' % (target_pathname,))
if sources is not None:
condor_entries.setdefault(condor_type, []).append(
conversion(sources, target_pathname))
- for s in sources:
- by_sample.setdefault(s.lane_number,[]).append(
- target_pathname)
else:
print " need file", target_pathname
- condor_entries['by_sample'] = by_sample
return condor_entries
def find_archive_sequence_files(self, result_map):
flowcell_ids = self.find_relavant_flowcell_ids()
self.import_sequences(flowcell_ids)
-
- query = RDF.SPARQLQuery("""
+ query_text = """
prefix libns: <http://jumpgate.caltech.edu/wiki/LibraryOntology#>
prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
prefix xsd: <http://www.w3.org/2001/XMLSchema#>
libns:flowcell_id ?flowcell_id ;
libns:library ?library ;
libns:library_id ?library_id ;
- rdf:type ?filetype ;
- a libns:raw_file .
+ libns:file_type ?filetype ;
+ a libns:illumina_result .
?flowcell libns:read_length ?read_length ;
libns:flowcell_type ?flowcell_type .
OPTIONAL { ?flowcell libns:flowcell_status ?flowcell_status }
- FILTER(?filetype != libns:raw_file)
+ FILTER(?filetype != libns:sequencer_result)
}
- """)
+ """
+ LOGGER.debug("find_archive_sequence_files query: %s",
+ query_text)
+ query = RDF.SPARQLQuery(query_text)
results = []
for r in query.execute(self.model):
library_id = fromTypedNode(r['library_id'])
if library_id in result_map:
- results.append(SequenceResult(r))
+ seq = SequenceResult(r)
+ LOGGER.debug("Creating sequence result for library %s: %s",
+ library_id,
+ repr(seq))
+ results.append(seq)
return results
def import_libraries(self, result_map):
"""Import library data into our model if we don't have it already
"""
q = RDF.Statement(library, rdfNS['type'], libraryOntology['library'])
+ present = False
if not self.model.contains_statement(q):
+ present = True
load_into_model(self.model, 'rdfa', library)
+ LOGGER.debug("Did we import %s: %s", library, present)
def find_relavant_flowcell_ids(self):
"""Generate set of flowcell ids that had samples of interest on them
return flowcell_ids
def import_sequences(self, flowcell_ids):
-
seq_dirs = []
for f in flowcell_ids:
seq_dirs.append(os.path.join(self.sequences_path, str(f)))
seq.save_to_model(self.model, self.host)
update_model_sequence_library(self.model, self.host)
- def find_missing_targets(self, result_map, raw_files):
- """
- Check if the sequence file exists.
- This requires computing what the sequence name is and checking
- to see if it can be found in the sequence location.
+ def update_fastq_targets(self, result_map, raw_files):
+ """Return list of fastq files that need to be built.
- Adds seq.paired flag to sequences listed in lib_db[*]['lanes']
+ Also update model with link between illumina result files
+ and our target fastq file.
"""
fastq_paired_template = '%(lib_id)s_%(flowcell)s_c%(cycle)s_l%(lane)s_r%(read)s.fastq'
fastq_single_template = '%(lib_id)s_%(flowcell)s_c%(cycle)s_l%(lane)s.fastq'
if self.force or not os.path.exists(target_pathname):
t = needed_targets.setdefault(target_pathname, {})
t.setdefault(seq.filetype, []).append(seq)
-
+ self.add_target_source_links(target_pathname, seq)
return needed_targets
+ def add_target_source_links(self, target, seq):
+ """Add link between target pathname and the 'lane' that produced it
+ (note lane objects are now post demultiplexing.)
+ """
+ target_uri = 'file://' + target
+ target_node = RDF.Node(RDF.Uri(target_uri))
+ source_stmt = RDF.Statement(target_node, dcNS['source'], seq.filenode)
+ self.model.add_statement(source_stmt)
+
def condor_srf_to_fastq(self, sources, target_pathname):
if len(sources) > 1:
raise ValueError("srf to fastq can only handle one file")
errmsg = u"Unsupported scheme {0} for {1}"
raise ValueError(errmsg.format(url.scheme, unicode(url)))
path = property(_get_path)
+
+ def __repr__(self):
+ return "SequenceResult({0},{1},{2})".format(
+ str(self.filenode),
+ str(self.library_id),
+ str(self.flowcell_id))
projects / htsworkflow.git / blobdiff