#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)))
flowcell_id = models.CharField(max_length=20, unique=True, db_index=True, core=True)
run_date = models.DateTimeField(core=True)
advanced_run = models.BooleanField(default=False)
+ paired_end = models.BooleanField(default=False)
read_length = models.IntegerField(default=32)
lane_1_library = models.ForeignKey(Library, related_name="lane_1_library")
list_display_links = ('run_date', 'flowcell_id', 'lane_1_library', 'lane_2_library', 'lane_3_library', 'lane_4_library', 'lane_5_library', 'lane_6_library', 'lane_7_library', 'lane_8_library')
fields = (
(None, {
- 'fields': ('run_date', 'flowcell_id', ('read_length', 'advanced_run'),)
+ 'fields': ('run_date', 'flowcell_id', ('read_length', 'paired_end', 'advanced_run',),)
}),
('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'),)