Update summary script to read from the GERALD Summary.xml file
authorDiane Trout <diane@caltech.edu>
Fri, 23 Apr 2010 22:21:46 +0000 (22:21 +0000)
committerDiane Trout <diane@caltech.edu>
Fri, 23 Apr 2010 22:21:46 +0000 (22:21 +0000)
instead of depending on randomly changign html code.

htsworkflow/pipelines/gerald.py
htsworkflow/pipelines/summary.py
htsworkflow/pipelines/test/simulate_runfolder.py
htsworkflow/pipelines/test/test_runfolder_rta160.py [new file with mode: 0644]
htsworkflow/pipelines/test/testdata/Summary-rta160.xml [new file with mode: 0644]

index 980c6616231623d1e0677bbdb2306052241ea2a4..35bc037f3c2db7c5fa867e0270be607eb1f8d4fd 100644 (file)
@@ -194,8 +194,12 @@ def gerald(pathname):
     g.tree = ElementTree.parse(config_pathname).getroot()
 
     # parse Summary.htm file
-    logging.info("Parsing Summary.htm")
-    summary_pathname = os.path.join(g.pathname, 'Summary.htm')
+    summary_pathname = os.path.join(g.pathname, 'Summary.xml')
+    if os.path.exists(summary_pathname):
+        logging.info("Parsing Summary.xml")
+    else:
+        summary_pathname = os.path.join(g.pathname, 'Summary.htm')
+        logging.info("Parsing Summary.htm")
     g.summary = Summary(summary_pathname)
     # parse eland files
     g.eland_results = eland(g.pathname, g)
index c5db2bc5e1bb04de24c99704174809a8b9634bac..414609905d26e82e98ad8f3c3244f65c821b8c8a 100644 (file)
@@ -34,6 +34,18 @@ class Summary(object):
           'AverageAlignmentScore': 'average_alignment_score',
           'PercentErrorRate': 'percent_error_rate'
         }
+        # These are tags that have mean/stdev as found in the GERALD Summary.xml file
+        GERALD_TAGS = {
+          #'laneYield': 'lane_yield', #this is just a number
+          'clusterCountRaw': 'cluster', # Raw
+          'clusterCountPF': 'cluster_pass_filter',
+          'oneSig': 'average_first_cycle_intensity',
+          'signal20AsPctOf1': 'percent_intensity_after_20_cycles',
+          'percentClustersPF': 'percent_pass_filter_clusters',
+          'percentUniquelyAlignedPF': 'percent_pass_filter_align',
+          'averageAlignScorePF': 'average_alignment_score',
+          'errorPF': 'percent_error_rate'
+        }
 
         def __init__(self, html=None, xml=None):
             self.lane = None
@@ -83,10 +95,21 @@ class Summary(object):
                 self.average_alignment_score = parsed_data[6]
                 self.percent_error_rate = parsed_data[7]
 
+        def set_elements_from_gerald_xml(self, read, element):
+            self.lane = int(element.find('laneNumber').text)
+            self.end = read
+            self.lane_yield = int(element.find('laneYield').text)
+
+            for GeraldName, LRSName in Summary.LaneResultSummary.GERALD_TAGS.items():
+                node = element.find(GeraldName)
+                if node is None:
+                    logging.info("Couldn't find %s" % (GeraldName))
+                setattr(self, LRSName, parse_xml_mean_range(node))
+                                                                      
         def get_elements(self):
             lane_result = ElementTree.Element(
                             Summary.LaneResultSummary.LANE_RESULT_SUMMARY,
-                            {'lane': str(self.lane), 'end': str(self.end)})
+                            {'lane': unicode(self.lane), 'end': unicode(self.end)})
             for tag, variable_name in Summary.LaneResultSummary.TAGS.items():
                 value = getattr(self, variable_name)
                 if value is None:
@@ -100,7 +123,7 @@ class Summary(object):
                     )
                 else:
                     element = ElementTree.SubElement(lane_result, tag)
-                    element.text = value
+                    element.text = unicode(value)
             return lane_result
 
         def set_elements(self, tree):
@@ -154,6 +177,15 @@ class Summary(object):
             data.append(self._flattened_row(r))
         return data
 
+    def _extract_lane_results(self, pathname):
+        """
+        Extract just the lane results.
+        Currently those are the only ones we care about.
+        """
+        
+        tables = self._extract_named_tables(pathname)
+
+
     def _extract_named_tables(self, pathname):
         """
         extract all the 'named' tables from a Summary.htm file
@@ -168,6 +200,48 @@ class Summary(object):
         file_body = open(pathname).read()
         file_body = file_body.replace('CHASTITY<=', 'CHASTITY&lt;=')
         tree = ElementTree.fromstring(file_body)
+
+        # are we reading the xml or the html version of the Summary file?
+        if tree.tag.lower() == 'summary':
+            # summary version
+            tables = self._extract_named_tables_from_gerald_xml(tree)
+        elif tree.tag.lower() == 'html':
+            # html version
+            tables = self._extract_named_tables_from_html(tree)
+            table_names = [ ('Lane Results Summary', 0),
+                            ('Lane Results Summary : Read 1', 0),
+                            ('Lane Results Summary : Read 2', 1),]
+            for name, end in table_names:
+                if tables.has_key(name):
+                    self._extract_lane_results_for_end(tables, name, end)
+
+        if len(self.lane_results[0])  == 0:
+            logging.warning("No Lane Results Summary Found in %s" % (pathname,))
+
+    def _extract_named_tables_from_gerald_xml(self, tree):
+        """
+        Extract useful named tables from a gerald created Summary.xml file
+        """
+        # using the function to convert to lower instead of just writing it
+        # makes the tag easier to read (IMO)
+        useful_tables = ['LaneResultsSummary'.lower(),]
+
+        tables ={}
+        for child in tree.getchildren():
+            if child.tag.lower() in  useful_tables:
+                read_tree = child.find('Read')
+                # we want 0 based.
+                read = int(read_tree.find('readNumber').text)-1
+                for element in read_tree.getchildren():
+                    if element.tag.lower() == "lane":
+                        lrs = Summary.LaneResultSummary()
+                        lrs.set_elements_from_gerald_xml(read, element)
+                        self.lane_results[lrs.end][lrs.lane] = lrs
+        # probably not useful
+        return tables
+        
+    ###### START HTML Table Extraction ########
+    def _extract_named_tables_from_html(self, tree):
         body = tree.find('body')
         tables = {}
         for i in range(len(body)):
@@ -179,17 +253,6 @@ class Summary(object):
                 tables[name] = data
         return tables
 
-    def _extract_lane_results(self, pathname):
-        tables = self._extract_named_tables(pathname)
-        table_names = [ ('Lane Results Summary', 0),
-                        ('Lane Results Summary : Read 1', 0),
-                        ('Lane Results Summary : Read 2', 1),]
-        for name, end in table_names:
-          if tables.has_key(name):
-            self._extract_lane_results_for_end(tables, name, end)
-        else:
-            logging.warning("No Lane Results Summary Found in %s" % (pathname,))
-
     def _extract_lane_results_for_end(self, tables, table_name, end):
         """
         extract the Lane Results Summary table
@@ -219,6 +282,7 @@ class Summary(object):
             lrs = Summary.LaneResultSummary(html=r)
             lrs.end = end
             self.lane_results[lrs.end][lrs.lane] = lrs
+    ###### END HTML Table Extraction ########
 
     def get_elements(self):
         summary = ElementTree.Element(Summary.SUMMARY,
@@ -248,7 +312,8 @@ class Summary(object):
         """
         Debugging function, report current object
         """
-        pass
+        tree = self.get_elements()
+        print ElementTree.tostring(tree)
 
 def tonumber(v):
     """
@@ -303,3 +368,29 @@ def parse_summary_element(element):
         return parse_mean_range_element(element)
     else:
         return element.text
+
+def parse_xml_mean_range(element):
+    """
+    Extract mean/stddev children from an element as a tuple
+    """
+    if element is None:
+        return None
+    
+    mean = element.find('mean')
+    stddev = element.find('stdev')
+    if mean is None or stddev is None:
+        raise RuntimeError("Summary.xml file format changed, expected mean/stddev tags")
+
+    return (tonumber(mean.text), tonumber(stddev.text))
+
+if __name__ == "__main__":
+    # test code
+    from optparse import OptionParser
+    parser = OptionParser('%prog [Summary.xml/Summary.htm]+')
+    opts, args = parser.parse_args()
+    if len(args) == 0:
+        parser.error('need at least one xml/html file')
+    for fname in args:
+        s = Summary(fname)
+        s.dump()
+        
index 555e58f8196399c38ec644067a06e6ab3f339f7e..855912dded3beeef168d6563a80f4ec337a170d4 100644 (file)
@@ -207,6 +207,11 @@ def make_summary_ipar130_htm(gerald_dir):
     destination = os.path.join(gerald_dir, 'Summary.htm')
     shutil.copy(source, destination)
 
+def make_summary_rta160_xml(gerald_dir):
+    source = os.path.join(TESTDATA_DIR, 'Summary-rta160.xml')
+    destination = os.path.join(gerald_dir, 'Summary.xml')
+    shutil.copy(source, destination)
+
 def make_eland_results(gerald_dir):
     eland_result = """>HWI-EAS229_24_207BTAAXX:1:7:599:759    ACATAGNCACAGACATAAACATAGACATAGAC U0      1       1       3       chrUextra.fa    28189829        R       D.
 >HWI-EAS229_24_207BTAAXX:1:7:205:842    AAACAANNCTCCCAAACACGTAAACTGGAAAA  U1      0       1       0       chr2L.fa        8796855 R       DD      24T
diff --git a/htsworkflow/pipelines/test/test_runfolder_rta160.py b/htsworkflow/pipelines/test/test_runfolder_rta160.py
new file mode 100644 (file)
index 0000000..d446e49
--- /dev/null
@@ -0,0 +1,277 @@
+#!/usr/bin/env python
+
+from datetime import datetime, date
+import os
+import tempfile
+import shutil
+import unittest
+
+from htsworkflow.pipelines import eland
+from htsworkflow.pipelines import ipar
+from htsworkflow.pipelines import bustard
+from htsworkflow.pipelines import gerald
+from htsworkflow.pipelines import runfolder
+from htsworkflow.pipelines.runfolder import ElementTree
+
+from htsworkflow.pipelines.test.simulate_runfolder import *
+
+
+def make_runfolder(obj=None):
+    """
+    Make a fake runfolder, attach all the directories to obj if defined
+    """
+    # make a fake runfolder directory
+    temp_dir = tempfile.mkdtemp(prefix='tmp_runfolder_')
+
+    runfolder_dir = os.path.join(temp_dir,
+                                 '090608_HWI-EAS229_0117_4286GAAXX')
+    os.mkdir(runfolder_dir)
+
+    data_dir = os.path.join(runfolder_dir, 'Data')
+    os.mkdir(data_dir)
+
+    intensities_dir = make_rta_intensities_1460(data_dir)
+
+    basecalls_dir = make_rta_basecalls_1460(intensities_dir)
+
+    #make_phasing_params(bustard_dir)
+    #make_bustard_config132(bustard_dir)
+
+    gerald_dir = os.path.join(basecalls_dir,
+                              'GERALD_16-06-2009_diane')
+    os.mkdir(gerald_dir)
+    make_gerald_config_100(gerald_dir)
+    make_summary_rta160_xml(gerald_dir)
+    make_eland_multi(gerald_dir, lane_list=[1,2,3,4,5,6,])
+    make_scarf(gerald_dir, lane_list=[7,])
+    make_fastq(gerald_dir, lane_list=[8,])
+
+    if obj is not None:
+        obj.temp_dir = temp_dir
+        obj.runfolder_dir = runfolder_dir
+        obj.data_dir = data_dir
+        obj.image_analysis_dir = intensities_dir
+        obj.bustard_dir = basecalls_dir
+        obj.gerald_dir = gerald_dir
+
+
+class RunfolderTests(unittest.TestCase):
+    """
+    Test components of the runfolder processing code
+    which includes firecrest, bustard, and gerald
+    """
+    def setUp(self):
+        # attaches all the directories to the object passed in
+        make_runfolder(self)
+
+    def tearDown(self):
+        shutil.rmtree(self.temp_dir)
+
+    # The only thing different from the previous RTA version is
+    # I'm processing the Summary.xml file
+
+    
+    def test_gerald(self):
+        # need to update gerald and make tests for it
+        g = gerald.gerald(self.gerald_dir)
+
+        self.failUnlessEqual(g.version,
+            '@(#) Id: GERALD.pl,v 1.171 2008/05/19 17:36:14 mzerara Exp')
+        self.failUnlessEqual(g.date, datetime(2009,2,22,21,15,59))
+        self.failUnlessEqual(len(g.lanes), len(g.lanes.keys()))
+        self.failUnlessEqual(len(g.lanes), len(g.lanes.items()))
+
+
+        # list of genomes, matches what was defined up in
+        # make_gerald_config.
+        # the first None is to offset the genomes list to be 1..9
+        # instead of pythons default 0..8
+        genomes = [None, 
+                   '/g/mm9', 
+                   '/g/mm9', 
+                   '/g/elegans190', 
+                   '/g/arabidopsis01222004',
+                   '/g/mm9', 
+                   '/g/mm9', 
+                   '/g/mm9', 
+                   '/g/mm9', ]
+
+        # test lane specific parameters from gerald config file
+        for i in range(1,9):
+            cur_lane = g.lanes[i]
+            self.failUnlessEqual(cur_lane.analysis, 'eland_extended')
+            self.failUnlessEqual(cur_lane.eland_genome, genomes[i])
+            self.failUnlessEqual(cur_lane.read_length, '37')
+            self.failUnlessEqual(cur_lane.use_bases, 'Y'*37)
+
+        # I want to be able to use a simple iterator
+        for l in g.lanes.values():
+          self.failUnlessEqual(l.analysis, 'eland_extended')
+          self.failUnlessEqual(l.read_length, '37')
+          self.failUnlessEqual(l.use_bases, 'Y'*37)
+
+        # test data extracted from summary file
+        clusters = [None,
+                    (281331, 11169), (203841, 13513),
+                    (220889, 15653), (137294, 14666),
+                    (129388, 14525), (262092, 10751),
+                    (185754, 13503), (233765, 9537),]
+
+        self.failUnlessEqual(len(g.summary), 1)
+        for i in range(1,9):
+            summary_lane = g.summary[0][i]
+            self.failUnlessEqual(summary_lane.cluster, clusters[i])
+            self.failUnlessEqual(summary_lane.lane, i)
+
+        xml = g.get_elements()
+        # just make sure that element tree can serialize the tree
+        xml_str = ElementTree.tostring(xml)
+        g2 = gerald.Gerald(xml=xml)
+        return
+
+        # do it all again after extracting from the xml file
+        self.failUnlessEqual(g.version, g2.version)
+        self.failUnlessEqual(g.date, g2.date)
+        self.failUnlessEqual(len(g.lanes.keys()), len(g2.lanes.keys()))
+        self.failUnlessEqual(len(g.lanes.items()), len(g2.lanes.items()))
+
+        # test lane specific parameters from gerald config file
+        for i in range(1,9):
+            g_lane = g.lanes[i]
+            g2_lane = g2.lanes[i]
+            self.failUnlessEqual(g_lane.analysis, g2_lane.analysis)
+            self.failUnlessEqual(g_lane.eland_genome, g2_lane.eland_genome)
+            self.failUnlessEqual(g_lane.read_length, g2_lane.read_length)
+            self.failUnlessEqual(g_lane.use_bases, g2_lane.use_bases)
+
+        # test (some) summary elements
+        self.failUnlessEqual(len(g.summary), 1)
+        for i in range(1,9):
+            g_summary = g.summary[0][i]
+            g2_summary = g2.summary[0][i]
+            self.failUnlessEqual(g_summary.cluster, g2_summary.cluster)
+            self.failUnlessEqual(g_summary.lane, g2_summary.lane)
+
+            g_eland = g.eland_results
+            g2_eland = g2.eland_results
+            for lane in g_eland.results[0].keys():
+                g_results = g_eland.results[0][lane]
+                g2_results = g2_eland.results[0][lane]
+                self.failUnlessEqual(g_results.reads,
+                                     g2_results.reads)
+                if isinstance(g_results, eland.ElandLane):
+                  self.failUnlessEqual(len(g_results.mapped_reads),
+                                       len(g2_results.mapped_reads))
+                  for k in g_results.mapped_reads.keys():
+                      self.failUnlessEqual(g_results.mapped_reads[k],
+                                           g2_results.mapped_reads[k])
+
+                  self.failUnlessEqual(len(g_results.match_codes),
+                                       len(g2_results.match_codes))
+                  for k in g_results.match_codes.keys():
+                      self.failUnlessEqual(g_results.match_codes[k],
+                                           g2_results.match_codes[k])
+
+
+    def test_eland(self):
+        return
+        hg_map = {'Lambda.fa': 'Lambda.fa'}
+        for i in range(1,22):
+          short_name = 'chr%d.fa' % (i,)
+          long_name = 'hg18/chr%d.fa' % (i,)
+          hg_map[short_name] = long_name
+
+        genome_maps = { 1:hg_map, 2:hg_map, 3:hg_map, 4:hg_map,
+                        5:hg_map, 6:hg_map, 7:hg_map, 8:hg_map }
+        eland_container = gerald.eland(self.gerald_dir, genome_maps=genome_maps)
+
+        # I added sequence lanes to the last 2 lanes of this test case
+        for i in range(1,7):
+            lane = eland_container.results[0][i]
+            self.failUnlessEqual(lane.reads, 6)
+            self.failUnlessEqual(lane.sample_name, "s")
+            self.failUnlessEqual(lane.lane_id, i)
+            self.failUnlessEqual(len(lane.mapped_reads), 17)
+            self.failUnlessEqual(lane.mapped_reads['hg18/chr5.fa'], 4)
+            self.failUnlessEqual(lane.match_codes['U0'], 3)
+            self.failUnlessEqual(lane.match_codes['R0'], 2)
+            self.failUnlessEqual(lane.match_codes['U1'], 1)
+            self.failUnlessEqual(lane.match_codes['R1'], 9)
+            self.failUnlessEqual(lane.match_codes['U2'], 0)
+            self.failUnlessEqual(lane.match_codes['R2'], 12)
+            self.failUnlessEqual(lane.match_codes['NM'], 1)
+            self.failUnlessEqual(lane.match_codes['QC'], 0)
+
+        # test scarf
+        lane = eland_container.results[0][7]
+        self.failUnlessEqual(lane.reads, 5)
+        self.failUnlessEqual(lane.sample_name, 's')
+        self.failUnlessEqual(lane.lane_id, 7)
+        self.failUnlessEqual(lane.sequence_type, eland.SequenceLane.SCARF_TYPE)
+
+        # test fastq
+        lane = eland_container.results[0][8]
+        self.failUnlessEqual(lane.reads, 3)
+        self.failUnlessEqual(lane.sample_name, 's')
+        self.failUnlessEqual(lane.lane_id, 8)
+        self.failUnlessEqual(lane.sequence_type, eland.SequenceLane.FASTQ_TYPE)
+
+        xml = eland_container.get_elements()
+        # just make sure that element tree can serialize the tree
+        xml_str = ElementTree.tostring(xml)
+        e2 = gerald.ELAND(xml=xml)
+
+        for i in range(1,9):
+            l1 = eland_container.results[0][i]
+            l2 = e2.results[0][i]
+            self.failUnlessEqual(l1.reads, l2.reads)
+            self.failUnlessEqual(l1.sample_name, l2.sample_name)
+            self.failUnlessEqual(l1.lane_id, l2.lane_id)
+            if isinstance(l1, eland.ElandLane):
+              self.failUnlessEqual(len(l1.mapped_reads), len(l2.mapped_reads))
+              self.failUnlessEqual(len(l1.mapped_reads), 17)
+              for k in l1.mapped_reads.keys():
+                  self.failUnlessEqual(l1.mapped_reads[k],
+                                       l2.mapped_reads[k])
+
+              self.failUnlessEqual(len(l1.match_codes), 9)
+              self.failUnlessEqual(len(l1.match_codes), len(l2.match_codes))
+              for k in l1.match_codes.keys():
+                  self.failUnlessEqual(l1.match_codes[k],
+                                       l2.match_codes[k])
+            elif isinstance(l1, eland.SequenceLane):
+                self.failUnlessEqual(l1.sequence_type, l2.sequence_type)
+
+    def test_runfolder(self):
+        return
+        runs = runfolder.get_runs(self.runfolder_dir)
+
+        # do we get the flowcell id from the filename?
+        self.failUnlessEqual(len(runs), 1)
+        name = 'run_4286GAAXX_%s.xml' % ( date.today().strftime('%Y-%m-%d'),)
+        self.failUnlessEqual(runs[0].name, name)
+
+        # do we get the flowcell id from the FlowcellId.xml file
+        make_flowcell_id(self.runfolder_dir, '207BTAAXY')
+        runs = runfolder.get_runs(self.runfolder_dir)
+        self.failUnlessEqual(len(runs), 1)
+        name = 'run_207BTAAXY_%s.xml' % ( date.today().strftime('%Y-%m-%d'),)
+        self.failUnlessEqual(runs[0].name, name)
+
+        r1 = runs[0]
+        xml = r1.get_elements()
+        xml_str = ElementTree.tostring(xml)
+
+        r2 = runfolder.PipelineRun(xml=xml)
+        self.failUnlessEqual(r1.name, r2.name)
+        self.failIfEqual(r2.image_analysis, None)
+        self.failIfEqual(r2.bustard, None)
+        self.failIfEqual(r2.gerald, None)
+
+
+def suite():
+    return unittest.makeSuite(RunfolderTests,'test')
+
+if __name__ == "__main__":
+    unittest.main(defaultTest="suite")
+
diff --git a/htsworkflow/pipelines/test/testdata/Summary-rta160.xml b/htsworkflow/pipelines/test/testdata/Summary-rta160.xml
new file mode 100644 (file)
index 0000000..833126b
--- /dev/null
@@ -0,0 +1,20378 @@
+<?xml version="1.0" ?>
+<?xml-stylesheet type="text/xsl"
+       href="Summary.xsl" ?>
+
+<Summary>
+  <ChipResultsSummary>
+    <clusterCountPF>139492159</clusterCountPF>
+    <clusterCountRaw>165435470</clusterCountRaw>
+    <yield>5300699000</yield>
+  </ChipResultsSummary>
+  <ChipSummary>
+    <ChipID>Unknown</ChipID>
+    <Machine>HWI-EAS229</Machine>
+    <RunFolder>100414_HWI-EAS229_0002_6009PAAXX</RunFolder>
+  </ChipSummary>
+  <Date>Wed Apr 21 00:23:00 2010</Date>
+  <ExpandedLaneSummary>
+    <Read>
+      <readNumber>1</readNumber>
+      <Lane>
+        <laneNumber>1</laneNumber>
+        <clusterCountRaw>
+          <mean>281331.30</mean>
+          <stdev>11168.77</stdev>
+          <sumsq>12474150000.00</sumsq>
+        </clusterCountRaw>
+        <errorPF>
+          <mean>0.39</mean>
+          <stdev>0.32</stdev>
+          <sumsq>25.36</sumsq>
+        </errorPF>
+        <errorRaw>
+          <mean>0.54</mean>
+          <stdev>0.31</stdev>
+          <sumsq>38.79</sumsq>
+        </errorRaw>
+        <infoContentPF>
+          <mean>168901</mean>
+          <stdev>5058</stdev>
+          <sumsq>2855300157583</sumsq>
+        </infoContentPF>
+        <infoContentRaw>
+          <mean>184360</mean>
+          <stdev>6215</stdev>
+          <sumsq>3402691726361</sumsq>
+        </infoContentRaw>
+        <percentClustersPF>
+          <mean>78.60</mean>
+          <stdev>2.15</stdev>
+          <sumsq>462.85</sumsq>
+        </percentClustersPF>
+        <percentUniquelyAlignedPF>
+          <mean>77.71</mean>
+          <stdev>0.29</stdev>
+          <sumsq>603899.07</sumsq>
+        </percentUniquelyAlignedPF>
+        <phasingApplied>0.2007</phasingApplied>
+        <prephasingApplied>0.2212</prephasingApplied>
+        <signalAverage1to3>
+          <mean>221.69</mean>
+          <stdev>16.26</stdev>
+          <sumsq>26447.40</sumsq>
+        </signalAverage1to3>
+        <signalAverage2to4>
+          <mean>210.43</mean>
+          <stdev>16.94</stdev>
+          <sumsq>28688.55</sumsq>
+        </signalAverage2to4>
+        <signalLoss10to20>
+          <mean>0.66</mean>
+          <stdev>0.47</stdev>
+          <sumsq>22.51</sumsq>
+        </signalLoss10to20>
+        <signalLoss2to10>
+          <mean>1.03</mean>
+          <stdev>0.80</stdev>
+          <sumsq>63.61</sumsq>
+        </signalLoss2to10>
+      </Lane>
+      <Lane>
+        <laneNumber>2</laneNumber>
+        <clusterCountRaw>
+          <mean>203841.10</mean>
+          <stdev>13513.34</stdev>
+          <sumsq>18261040000.00</sumsq>
+        </clusterCountRaw>
+        <errorPF>
+          <mean>0.19</mean>
+          <stdev>0.00</stdev>
+          <sumsq>3.61</sumsq>
+        </errorPF>
+        <errorRaw>
+          <mean>0.26</mean>
+          <stdev>0.01</stdev>
+          <sumsq>6.88</sumsq>
+        </errorRaw>
+        <infoContentPF>
+          <mean>142157</mean>
+          <stdev>7901</stdev>
+          <sumsq>2027051792820</sumsq>
+        </infoContentPF>
+        <infoContentRaw>
+          <mean>148303</mean>
+          <stdev>8872</stdev>
+          <sumsq>2207171728372</sumsq>
+        </infoContentRaw>
+        <percentClustersPF>
+          <mean>89.13</mean>
+          <stdev>1.12</stdev>
+          <sumsq>125.88</sumsq>
+        </percentClustersPF>
+        <percentUniquelyAlignedPF>
+          <mean>79.19</mean>
+          <stdev>0.11</stdev>
+          <sumsq>627167.02</sumsq>
+        </percentUniquelyAlignedPF>
+        <phasingApplied>0.2007</phasingApplied>
+        <prephasingApplied>0.2212</prephasingApplied>
+        <signalAverage1to3>
+          <mean>270.66</mean>
+          <stdev>7.00</stdev>
+          <sumsq>4900.87</sumsq>
+        </signalAverage1to3>
+        <signalAverage2to4>
+          <mean>264.76</mean>
+          <stdev>7.73</stdev>
+          <sumsq>5973.98</sumsq>
+        </signalAverage2to4>
+        <signalLoss10to20>
+          <mean>0.73</mean>
+          <stdev>0.32</stdev>
+          <sumsq>10.55</sumsq>
+        </signalLoss10to20>
+        <signalLoss2to10>
+          <mean>0.97</mean>
+          <stdev>0.45</stdev>
+          <sumsq>20.00</sumsq>
+        </signalLoss2to10>
+      </Lane>
+      <Lane>
+        <laneNumber>3</laneNumber>
+        <clusterCountRaw>
+          <mean>220889.20</mean>
+          <stdev>15653.21</stdev>
+          <sumsq>24502310000.00</sumsq>
+        </clusterCountRaw>
+        <percentClustersPF>
+          <mean>83.05</mean>
+          <stdev>1.72</stdev>
+          <sumsq>297.30</sumsq>
+        </percentClustersPF>
+        <phasingApplied>0.2007</phasingApplied>
+        <prephasingApplied>0.2212</prephasingApplied>
+        <signalAverage1to3>
+          <mean>146.35</mean>
+          <stdev>4.56</stdev>
+          <sumsq>2079.34</sumsq>
+        </signalAverage1to3>
+        <signalAverage2to4>
+          <mean>142.05</mean>
+          <stdev>4.81</stdev>
+          <sumsq>2312.11</sumsq>
+        </signalAverage2to4>
+        <signalLoss10to20>
+          <mean>0.99</mean>
+          <stdev>0.47</stdev>
+          <sumsq>21.64</sumsq>
+        </signalLoss10to20>
+        <signalLoss2to10>
+          <mean>0.35</mean>
+          <stdev>0.47</stdev>
+          <sumsq>21.66</sumsq>
+        </signalLoss2to10>
+      </Lane>
+      <Lane>
+        <laneNumber>4</laneNumber>
+        <clusterCountRaw>
+          <mean>137293.70</mean>
+          <stdev>14666.22</stdev>
+          <sumsq>21509810000.00</sumsq>
+        </clusterCountRaw>
+        <percentClustersPF>
+          <mean>87.40</mean>
+          <stdev>3.22</stdev>
+          <sumsq>1035.79</sumsq>
+        </percentClustersPF>
+        <phasingApplied>0.2007</phasingApplied>
+        <prephasingApplied>0.2212</prephasingApplied>
+        <signalAverage1to3>
+          <mean>132.95</mean>
+          <stdev>5.22</stdev>
+          <sumsq>2724.53</sumsq>
+        </signalAverage1to3>
+        <signalAverage2to4>
+          <mean>128.64</mean>
+          <stdev>5.54</stdev>
+          <sumsq>3064.05</sumsq>
+        </signalAverage2to4>
+        <signalLoss10to20>
+          <mean>1.06</mean>
+          <stdev>0.34</stdev>
+          <sumsq>11.53</sumsq>
+        </signalLoss10to20>
+        <signalLoss2to10>
+          <mean>0.45</mean>
+          <stdev>0.57</stdev>
+          <sumsq>32.22</sumsq>
+        </signalLoss2to10>
+      </Lane>
+      <Lane>
+        <laneNumber>5</laneNumber>
+        <clusterCountRaw>
+          <mean>129388.10</mean>
+          <stdev>14524.71</stdev>
+          <sumsq>21096720000.00</sumsq>
+        </clusterCountRaw>
+        <percentClustersPF>
+          <mean>89.99</mean>
+          <stdev>0.80</stdev>
+          <sumsq>64.51</sumsq>
+        </percentClustersPF>
+        <phasingApplied>0.2007</phasingApplied>
+        <prephasingApplied>0.2212</prephasingApplied>
+        <signalAverage1to3>
+          <mean>116.21</mean>
+          <stdev>4.24</stdev>
+          <sumsq>1795.40</sumsq>
+        </signalAverage1to3>
+        <signalAverage2to4>
+          <mean>112.07</mean>
+          <stdev>3.99</stdev>
+          <sumsq>1589.89</sumsq>
+        </signalAverage2to4>
+        <signalLoss10to20>
+          <mean>1.33</mean>
+          <stdev>0.34</stdev>
+          <sumsq>11.57</sumsq>
+        </signalLoss10to20>
+        <signalLoss2to10>
+          <mean>0.21</mean>
+          <stdev>0.48</stdev>
+          <sumsq>22.71</sumsq>
+        </signalLoss2to10>
+      </Lane>
+      <Lane>
+        <laneNumber>6</laneNumber>
+        <clusterCountRaw>
+          <mean>262092.20</mean>
+          <stdev>10750.85</stdev>
+          <sumsq>11558080000.00</sumsq>
+        </clusterCountRaw>
+        <errorPF>
+          <mean>0.25</mean>
+          <stdev>0.02</stdev>
+          <sumsq>6.19</sumsq>
+        </errorPF>
+        <errorRaw>
+          <mean>0.40</mean>
+          <stdev>0.03</stdev>
+          <sumsq>15.84</sumsq>
+        </errorRaw>
+        <infoContentPF>
+          <mean>174594</mean>
+          <stdev>5116</stdev>
+          <sumsq>3050898969318</sumsq>
+        </infoContentPF>
+        <infoContentRaw>
+          <mean>188502</mean>
+          <stdev>7212</stdev>
+          <sumsq>3558472673840</sumsq>
+        </infoContentRaw>
+        <percentClustersPF>
+          <mean>81.79</mean>
+          <stdev>2.05</stdev>
+          <sumsq>418.82</sumsq>
+        </percentClustersPF>
+        <percentUniquelyAlignedPF>
+          <mean>82.51</mean>
+          <stdev>0.73</stdev>
+          <sumsq>680852.48</sumsq>
+        </percentUniquelyAlignedPF>
+        <phasingApplied>0.2007</phasingApplied>
+        <prephasingApplied>0.2212</prephasingApplied>
+        <signalAverage1to3>
+          <mean>204.64</mean>
+          <stdev>10.54</stdev>
+          <sumsq>11117.37</sumsq>
+        </signalAverage1to3>
+        <signalAverage2to4>
+          <mean>199.43</mean>
+          <stdev>6.81</stdev>
+          <sumsq>4640.09</sumsq>
+        </signalAverage2to4>
+        <signalLoss10to20>
+          <mean>0.79</mean>
+          <stdev>0.29</stdev>
+          <sumsq>8.32</sumsq>
+        </signalLoss10to20>
+        <signalLoss2to10>
+          <mean>0.92</mean>
+          <stdev>0.41</stdev>
+          <sumsq>16.99</sumsq>
+        </signalLoss2to10>
+      </Lane>
+      <Lane>
+        <laneNumber>7</laneNumber>
+        <clusterCountRaw>
+          <mean>185753.90</mean>
+          <stdev>13502.66</stdev>
+          <sumsq>18232180000.00</sumsq>
+        </clusterCountRaw>
+        <errorPF>
+          <mean>0.30</mean>
+          <stdev>0.08</stdev>
+          <sumsq>9.70</sumsq>
+        </errorPF>
+        <errorRaw>
+          <mean>0.38</mean>
+          <stdev>0.08</stdev>
+          <sumsq>15.21</sumsq>
+        </errorRaw>
+        <infoContentPF>
+          <mean>116265</mean>
+          <stdev>8028</stdev>
+          <sumsq>1358158580433</sumsq>
+        </infoContentPF>
+        <infoContentRaw>
+          <mean>121504</mean>
+          <stdev>8822</stdev>
+          <sumsq>1484037419811</sumsq>
+        </infoContentRaw>
+        <percentClustersPF>
+          <mean>88.55</mean>
+          <stdev>1.25</stdev>
+          <sumsq>156.63</sumsq>
+        </percentClustersPF>
+        <percentUniquelyAlignedPF>
+          <mean>71.81</mean>
+          <stdev>0.27</stdev>
+          <sumsq>515656.11</sumsq>
+        </percentUniquelyAlignedPF>
+        <phasingApplied>0.2007</phasingApplied>
+        <prephasingApplied>0.2212</prephasingApplied>
+        <signalAverage1to3>
+          <mean>233.15</mean>
+          <stdev>11.33</stdev>
+          <sumsq>12843.12</sumsq>
+        </signalAverage1to3>
+        <signalAverage2to4>
+          <mean>227.58</mean>
+          <stdev>9.63</stdev>
+          <sumsq>9264.58</sumsq>
+        </signalAverage2to4>
+        <signalLoss10to20>
+          <mean>0.73</mean>
+          <stdev>0.28</stdev>
+          <sumsq>7.91</sumsq>
+        </signalLoss10to20>
+        <signalLoss2to10>
+          <mean>0.97</mean>
+          <stdev>0.41</stdev>
+          <sumsq>16.71</sumsq>
+        </signalLoss2to10>
+      </Lane>
+      <Lane>
+        <laneNumber>8</laneNumber>
+        <clusterCountRaw>
+          <mean>233765.20</mean>
+          <stdev>9537.48</stdev>
+          <sumsq>9096348000.00</sumsq>
+        </clusterCountRaw>
+        <errorPF>
+          <mean>0.35</mean>
+          <stdev>0.14</stdev>
+          <sumsq>14.04</sumsq>
+        </errorPF>
+        <errorRaw>
+          <mean>0.48</mean>
+          <stdev>0.15</stdev>
+          <sumsq>24.98</sumsq>
+        </errorRaw>
+        <infoContentPF>
+          <mean>133204</mean>
+          <stdev>4800</stdev>
+          <sumsq>1776612211287</sumsq>
+        </infoContentPF>
+        <infoContentRaw>
+          <mean>142956</mean>
+          <stdev>5955</stdev>
+          <sumsq>2047164367120</sumsq>
+        </infoContentRaw>
+        <percentClustersPF>
+          <mean>83.21</mean>
+          <stdev>2.13</stdev>
+          <sumsq>453.95</sumsq>
+        </percentClustersPF>
+        <percentUniquelyAlignedPF>
+          <mean>69.72</mean>
+          <stdev>0.49</stdev>
+          <sumsq>486122.68</sumsq>
+        </percentUniquelyAlignedPF>
+        <phasingApplied>0.2007</phasingApplied>
+        <prephasingApplied>0.2212</prephasingApplied>
+        <signalAverage1to3>
+          <mean>208.01</mean>
+          <stdev>9.81</stdev>
+          <sumsq>9618.85</sumsq>
+        </signalAverage1to3>
+        <signalAverage2to4>
+          <mean>204.47</mean>
+          <stdev>7.05</stdev>
+          <sumsq>4976.25</sumsq>
+        </signalAverage2to4>
+        <signalLoss10to20>
+          <mean>0.77</mean>
+          <stdev>0.30</stdev>
+          <sumsq>8.72</sumsq>
+        </signalLoss10to20>
+        <signalLoss2to10>
+          <mean>0.98</mean>
+          <stdev>0.69</stdev>
+          <sumsq>47.13</sumsq>
+        </signalLoss2to10>
+      </Lane>
+    </Read>
+  </ExpandedLaneSummary>
+  <LaneParameterSummary>
+    <Lane>
+      <laneNumber>1</laneNumber>
+      <alignedFlag>1</alignedFlag>
+      <chastityThreshold>0.6</chastityThreshold>
+      <eland>/zfs1/wold-lab/diane/sequencer/genomes/mm9</eland>
+      <length>unknown</length>
+      <lengthsList>38</lengthsList>
+      <originalReadLength>38</originalReadLength>
+      <purity>'((FAILED_CHASTITY&lt;=1.0))'</purity>
+      <sample>unknown</sample>
+      <template>mm9</template>
+      <tileCountPF>100</tileCountPF>
+      <tileCountRaw>100</tileCountRaw>
+      <type>ELAND_EXTENDED</type>
+    </Lane>
+    <Lane>
+      <laneNumber>2</laneNumber>
+      <alignedFlag>1</alignedFlag>
+      <chastityThreshold>0.6</chastityThreshold>
+      <eland>/zfs1/wold-lab/diane/sequencer/genomes/hg18</eland>
+      <length>unknown</length>
+      <lengthsList>38</lengthsList>
+      <originalReadLength>38</originalReadLength>
+      <purity>'((FAILED_CHASTITY&lt;=1.0))'</purity>
+      <sample>unknown</sample>
+      <template>hg18</template>
+      <tileCountPF>100</tileCountPF>
+      <tileCountRaw>100</tileCountRaw>
+      <type>ELAND_EXTENDED</type>
+    </Lane>
+    <Lane>
+      <laneNumber>3</laneNumber>
+      <alignedFlag>0</alignedFlag>
+      <chastityThreshold>0.6</chastityThreshold>
+      <eland>Need_to_specify_ELAND_genome_directory</eland>
+      <length>unknown</length>
+      <lengthsList>38</lengthsList>
+      <originalReadLength>38</originalReadLength>
+      <purity>'((FAILED_CHASTITY&lt;=1.0))'</purity>
+      <sample>unknown</sample>
+      <template>unknown</template>
+      <tileCountPF>100</tileCountPF>
+      <tileCountRaw>100</tileCountRaw>
+      <type>SEQUENCE</type>
+    </Lane>
+    <Lane>
+      <laneNumber>4</laneNumber>
+      <alignedFlag>0</alignedFlag>
+      <chastityThreshold>0.6</chastityThreshold>
+      <eland>Need_to_specify_ELAND_genome_directory</eland>
+      <length>unknown</length>
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+        </Tile>
+      </Read>
+    </Lane>
+  </TileResultsByLane>
+</Summary>