+ restructured test cases
[qpalma.git] / scripts / qpalma_pipeline.py
index 20ed7b4..bcb2a12 100644 (file)
@@ -65,7 +65,8 @@ class System:
       print '\t\t\tStarting approximation...\n'
       print '#'*80
 
-      # 
+      # When we are given only genomic reads we first generate artificially spliced
+      # ones in order to generate a training set
       pre_task = TrainingPreprocessingTask(self.settings)
       pre_task.createJobs()
       pre_task.submit() 
@@ -99,10 +100,10 @@ class System:
       # Before creating a candidate spliced read dataset we have to first filter
       # the matches from the first seed finding run.
 
-      #approx_task = ApproximationTask(self.settings)
-      #approx_task.CreateJobs()
-      #approx_task.Submit()
-      #approx_task.CheckIfTaskFinished()
+      approx_task = ApproximationTask(self.settings)
+      approx_task.CreateJobs()
+      approx_task.Submit()
+      approx_task.CheckIfTaskFinished()
       
       # After filtering combine the filtered matches from the first run and the
       # found matches from the second run to a full dataset
@@ -111,17 +112,17 @@ class System:
       print '\t\t\tStarting dataset generation...\n'
       print '#'*80
 
-      #generatePredictionDataset(self.settings)
+      generatePredictionDataset(self.settings)
 
       print '#'*80
       print '\t\t\tStarting alignments...\n'
       print '#'*80
 
       # Now that we have a dataset we can perform accurate alignments
-      #align_task = AlignmentTask(self.settings)
-      #align_task.CreateJobs()
-      #align_task.Submit()
-      #align_task.CheckIfTaskFinished()
+      align_task = AlignmentTask(self.settings)
+      align_task.CreateJobs()
+      align_task.Submit()
+      align_task.CheckIfTaskFinished()
 
       print '#'*80
       print '\t\t\tPostprocessing...\n'