#data.append("GENOME_DIR %s" % (BASE_DIR))
#data.append("CONTAM_DIR %s" % (BASE_DIR))
read_length = fcObj.read_length
- data.append("READ_LENGTH %d" % (read_length))
#data.append("ELAND_REPEAT")
data.append("ELAND_MULTIPLE_INSTANCES 8")
#Loop through and create entries for each species.
for genome in genome_list:
lanes = ''.join(genome_dict[genome])
- data.append('%s:ANALYSIS eland' % (lanes))
+ if fcObj.paired_end:
+ data.append('%s:ANALYSIS eland_pair' % (lanes))
+ else:
+ data.append('%s:ANALYSIS eland_extended' % (lanes))
data.append('%s:READ_LENGTH %s' % (lanes, read_length))
data.append('%s:ELAND_GENOME %s' % (lanes, '%%(%s)s' % (genome)))
data.append('%s:USE_BASES %s' % (lanes, 'Y'*int(read_length)))
list_filter = ('seq_mac_id','cluster_mac_id')
fieldsets = (
(None, {
- 'fields': ('run_date', ('flowcell_id','cluster_mac_id','seq_mac_id'), ('read_length'),)
+ 'fields': ('run_date', ('flowcell_id','cluster_mac_id','seq_mac_id'), ('read_length', 'paired_end'),)
}),
('Lanes:', {
'fields' : (('lane_1_library', 'lane_1_pM', 'lane_1_cluster_estimate'), ('lane_2_library', 'lane_2_pM', 'lane_2_cluster_estimate'), ('lane_3_library', 'lane_3_pM', 'lane_3_cluster_estimate'), ('lane_4_library', 'lane_4_pM', 'lane_4_cluster_estimate'), ('lane_5_library', 'lane_5_pM', 'lane_5_cluster_estimate'), ('lane_6_library', 'lane_6_pM', 'lane_6_cluster_estimate'), ('lane_7_library', 'lane_7_pM', 'lane_7_cluster_estimate'), ('lane_8_library', 'lane_8_pM', 'lane_8_cluster_estimate'),)
flowcell_id = models.CharField(max_length=20, unique=True, db_index=True)
run_date = models.DateTimeField()
advanced_run = models.BooleanField(default=False)
+ paired_end = models.BooleanField(default=False)
read_length = models.IntegerField(default=32) #Stanford is currenlty 25
lane_1_library = models.ForeignKey(Library, related_name="lane_1_library")