Parse runfolders generated with IPAR 1.3 and pipeline 1.3.2

I'm still parsing the Summary.htm file, though it appears they added
an xml file with the same information in it.

Also the s_matrix.txt file seems to have gone away.

This also adds a full Summary.htm into pipelines/test/testdata

diff --git a/htsworkflow/pipelines/eland.py b/htsworkflow/pipelines/eland.py
index edd9036336f3972c52a222187c2884a42563c009..08d3d415c6d4052a8cef291b03cb12caafd1e34e 100644 (file)
--- a/htsworkflow/pipelines/eland.py
+++ b/htsworkflow/pipelines/eland.py
@@ -355,24 +355,30 @@ def check_for_eland_file(basedir, pattern, lane_id, end):
    basename = pattern % (full_lane_id,)
    pathname = os.path.join(basedir, basename)
    if os.path.exists(pathname):
+       logging.info('found eland file in %s' % (pathname,))
        return pathname
    else:
+       logging.info('no eland file in %s' % (pathname,))
        return None
 
-def eland(basedir, gerald=None, genome_maps=None):
+def eland(gerald_dir, gerald=None, genome_maps=None):
     e = ELAND()
 
+    lane_ids = range(1,9)
+    ends = [None, 1, 2]
+
+    basedirs = [gerald_dir]
+
     # if there is a basedir/Temp change basedir to point to the temp
     # directory, as 1.1rc1 moves most of the files we've historically
     # cared about to that subdirectory.
     # we should look into what the official 'result' files are.
-    basedir_temp = os.path.join(basedir, 'Temp')
+    # and 1.3 moves them back
+    basedir_temp = os.path.join(gerald_dir, 'Temp')
     if os.path.isdir(basedir_temp):
-        basedir = basedir_temp
+        basedirs.append(basedir_temp)
 
-    lane_ids = range(1,9)
-    ends = [None, 1, 2]
-    
+   
     # the order in patterns determines the preference for what
     # will be found.
     patterns = ['s_%s_eland_result.txt',
@@ -385,37 +391,38 @@ def eland(basedir, gerald=None, genome_maps=None):
                 's_%s_eland_multi.txt.bz2',
                 's_%s_eland_multi.txt.gz',]
 
-    for end in ends:
-        for lane_id in lane_ids:
-            for p in patterns:
-                pathname = check_for_eland_file(basedir, p, lane_id, end)
-                if pathname is not None:
-                  break
-            else:
-                continue
-            # yes the lane_id is also being computed in ElandLane._update
-            # I didn't want to clutter up my constructor
-            # but I needed to persist the sample_name/lane_id for
-            # runfolder summary_report
-            path, name = os.path.split(pathname)
-            logging.info("Adding eland file %s" %(name,))
-            # split_name = name.split('_')
-            # lane_id = int(split_name[1])
-
-            if genome_maps is not None:
-                genome_map = genome_maps[lane_id]
-            elif gerald is not None:
-                genome_dir = gerald.lanes[lane_id].eland_genome
-                genome_map = build_genome_fasta_map(genome_dir)
-            else:
-                genome_map = {}
-
-            eland_result = ElandLane(pathname, lane_id, end, genome_map)
-            if end is None:
-                effective_end =  0
-            else:
-                effective_end = end - 1
-            e.results[effective_end][lane_id] = eland_result
+    for basedir in basedirs:
+        for end in ends:
+            for lane_id in lane_ids:
+                for p in patterns:
+                    pathname = check_for_eland_file(basedir, p, lane_id, end)
+                    if pathname is not None:
+                      break
+                else:
+                    continue
+                # yes the lane_id is also being computed in ElandLane._update
+                # I didn't want to clutter up my constructor
+                # but I needed to persist the sample_name/lane_id for
+                # runfolder summary_report
+                path, name = os.path.split(pathname)
+                logging.info("Adding eland file %s" %(name,))
+                # split_name = name.split('_')
+                # lane_id = int(split_name[1])
+    
+                if genome_maps is not None:
+                    genome_map = genome_maps[lane_id]
+                elif gerald is not None:
+                    genome_dir = gerald.lanes[lane_id].eland_genome
+                    genome_map = build_genome_fasta_map(genome_dir)
+                else:
+                    genome_map = {}
+
+                eland_result = ElandLane(pathname, lane_id, end, genome_map)
+                if end is None:
+                    effective_end =  0
+                else:
+                    effective_end = end - 1
+                e.results[effective_end][lane_id] = eland_result
     return e
 
 def build_genome_fasta_map(genome_dir):

diff --git a/htsworkflow/pipelines/ipar.py b/htsworkflow/pipelines/ipar.py
index 9113d6f1717fd6fd2d27fe9c2bd1e848766f2b5d..a9544203afb7ee255c834f07e83ecbed91c39e67 100644 (file)
--- a/htsworkflow/pipelines/ipar.py
+++ b/htsworkflow/pipelines/ipar.py
@@ -12,6 +12,7 @@ fromxml
 __docformat__ = "restructuredtext en"
 
 import datetime
+from glob import glob
 import logging
 import os
 import re
@@ -195,11 +196,17 @@ def ipar(pathname):
     if groups[0] != 'IPAR':
       raise ValueError('ipar can only process IPAR directories')
 
+    bustard_pattern = os.path.join(pathname, 'Bustard*')
     # contents of the matrix file?
     matrix_pathname = os.path.join(pathname, 'Matrix', 's_matrix.txt')
-    if not os.path.exists(matrix_pathname):
+    if os.path.exists(matrix_pathname):
+        # this is IPAR_1.01
+        i.matrix = open(matrix_pathname, 'r').read()
+    elif glob(bustard_pattern) > 0:
+        i.matrix = None
+        # its still live.
+    else:
         return None
-    i.matrix = open(matrix_pathname, 'r').read()
 
     # look for parameter xml file
     paramfile = os.path.join(path, '.params')

diff --git a/htsworkflow/pipelines/summary.py b/htsworkflow/pipelines/summary.py
index ca5fa5002fc057f7b26d0bd80cc10f1ac75a0fa0..bce8184b7bd126978de6ab8800adf28981cd7a17 100644 (file)
--- a/htsworkflow/pipelines/summary.py
+++ b/htsworkflow/pipelines/summary.py
@@ -2,6 +2,7 @@
 Analyze the Summary.htm file produced by GERALD
 """
 import types
+from pprint import pprint
 
 from htsworkflow.pipelines.runfolder import ElementTree
 from htsworkflow.util.ethelp import indent, flatten
@@ -51,7 +52,7 @@ class Summary(object):
 
         def set_elements_from_html(self, data):
             if not len(data) in (8,10):
-                raise RuntimeError("Summary.htm file format changed")
+                raise RuntimeError("Summary.htm file format changed, len(data)=%d" % (len(data),))
 
             # same in pre-0.3.0 Summary file and 0.3 summary file
             self.lane = int(data[0])
@@ -197,8 +198,9 @@ class Summary(object):
             # grab the lane by lane data
             lane_summary = lane_summary[1:]
 
-        # this is version 2 of the summary file
-        if len(lane_summary[-1]) == 10:
+        # len(lane_summary[-1] = 10 is version 2 of the summary file
+        #                      = 9  is version 3 of the Summary.htm file
+        elif len(lane_summary[-1]) in (9, 10):
             # lane_summary[0] is a different less specific header row
             headers = lane_summary[1]
             lane_summary = lane_summary[2:10]

diff --git a/htsworkflow/pipelines/test/simulate_runfolder.py b/htsworkflow/pipelines/test/simulate_runfolder.py
index 72c1a1a5958ffc9dd93034a0434f13938954d8e6..5354cfbac1e691e97e67caed9e35d23e52cfc493 100644 (file)
--- a/htsworkflow/pipelines/test/simulate_runfolder.py
+++ b/htsworkflow/pipelines/test/simulate_runfolder.py
@@ -3,6 +3,7 @@ Create simulated solexa/illumina runfolders for testing
 """
 
 import os
+import shutil
 
 def make_firecrest_dir(data_dir, version="1.9.2", start=1, stop=37):
     firecrest_dir = os.path.join(data_dir, 
@@ -1899,6 +1900,13 @@ def make_summary_paired_htm(gerald_dir):
     f.write(summary_htm)
     f.close()
 
+def make_summary_ipar130_htm(gerald_dir):
+     test_dir = os.path.split(__file__)[0]
+     testdata_dir = os.path.join(test_dir, 'testdata')
+     summary_htm = os.path.join(testdata_dir, 'Summary-ipar130.htm')
+     dest = os.path.join(gerald_dir, 'Summary.htm')
+     shutil.copy(summary_htm, dest)
+
 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_ipar130.py b/htsworkflow/pipelines/test/test_runfolder_ipar130.py
new file mode 100644 (file)
index 0000000..77f7f90
--- /dev/null
+++ b/htsworkflow/pipelines/test/test_runfolder_ipar130.py
@@ -0,0 +1,286 @@
+#!/usr/bin/env python
+
+from datetime import datetime, date
+import os
+import tempfile
+import shutil
+import unittest
+
+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,
+                                 '090313_HWI-EAS229_0101_3021JAAXX')
+    os.mkdir(runfolder_dir)
+
+    data_dir = os.path.join(runfolder_dir, 'Data')
+    os.mkdir(data_dir)
+
+    ipar_dir = make_ipar_dir(data_dir)
+
+    bustard_dir = os.path.join(ipar_dir,
+                               'Bustard1.3.2_15-03-2008_diane')
+    os.mkdir(bustard_dir)
+    make_phasing_params(bustard_dir)
+
+    gerald_dir = os.path.join(bustard_dir,
+                              'GERALD_15-03-2008_diane')
+    os.mkdir(gerald_dir)
+    make_gerald_config(gerald_dir)
+    make_summary_ipar130_htm(gerald_dir)
+    make_eland_multi(gerald_dir)
+
+    if obj is not None:
+        obj.temp_dir = temp_dir
+        obj.runfolder_dir = runfolder_dir
+        obj.data_dir = data_dir
+        obj.image_analysis_dir = ipar_dir
+        obj.bustard_dir = bustard_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)
+
+    def test_ipar(self):
+        """
+        Construct a firecrest object
+        """
+        i = ipar.ipar(self.image_analysis_dir)
+        self.failUnlessEqual(i.version, '2.01.192.0')
+        self.failUnlessEqual(i.start, 1)
+        self.failUnlessEqual(i.stop, 37)
+
+        xml = i.get_elements()
+        # just make sure that element tree can serialize the tree
+        xml_str = ElementTree.tostring(xml)
+
+        i2 = ipar.IPAR(xml=xml)
+        self.failUnlessEqual(i.version, i2.version)
+        self.failUnlessEqual(i.start,   i2.start)
+        self.failUnlessEqual(i.stop,    i2.stop)
+        self.failUnlessEqual(i.date,    i2.date)
+        self.failUnlessEqual(i.file_list(), i2.file_list())
+
+    def test_bustard(self):
+        """
+        construct a bustard object
+        """
+        b = bustard.bustard(self.bustard_dir)
+        self.failUnlessEqual(b.version, '1.3.2')
+        self.failUnlessEqual(b.date,    date(2008,3,15))
+        self.failUnlessEqual(b.user,    'diane')
+        self.failUnlessEqual(len(b.phasing), 8)
+        self.failUnlessAlmostEqual(b.phasing[8].phasing, 0.0099)
+
+        xml = b.get_elements()
+        b2 = bustard.Bustard(xml=xml)
+        self.failUnlessEqual(b.version, b2.version)
+        self.failUnlessEqual(b.date,    b2.date )
+        self.failUnlessEqual(b.user,    b2.user)
+        self.failUnlessEqual(len(b.phasing), len(b2.phasing))
+        for key in b.phasing.keys():
+            self.failUnlessEqual(b.phasing[key].lane,
+                                 b2.phasing[key].lane)
+            self.failUnlessEqual(b.phasing[key].phasing,
+                                 b2.phasing[key].phasing)
+            self.failUnlessEqual(b.phasing[key].prephasing,
+                                 b2.phasing[key].prephasing)
+
+    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 1.68.2.2 2007/06/13 11:08:49 km Exp')
+        self.failUnlessEqual(g.date, datetime(2008,4,19,19,8,30))
+        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/dm3', '/g/equcab1', '/g/equcab1', '/g/canfam2',
+                         '/g/hg18', '/g/hg18', '/g/hg18', '/g/hg18', ]
+
+        # 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')
+            self.failUnlessEqual(cur_lane.eland_genome, genomes[i])
+            self.failUnlessEqual(cur_lane.read_length, '32')
+            self.failUnlessEqual(cur_lane.use_bases, 'Y'*32)
+
+        # I want to be able to use a simple iterator
+        for l in g.lanes.values():
+          self.failUnlessEqual(l.analysis, 'eland')
+          self.failUnlessEqual(l.read_length, '32')
+          self.failUnlessEqual(l.use_bases, 'Y'*32)
+
+        # test data extracted from summary file
+        clusters = [None,
+                    (126910, 4300), (165739, 6792),
+                    (196565, 8216), (153897, 8501),
+                    (135536, 3908), (154083, 9315),
+                    (159991, 9292), (198479, 17671),]
+
+        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)
+
+        # 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)
+                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):
+        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 = gerald.eland(self.gerald_dir, genome_maps=genome_maps)
+
+        for i in range(1,9):
+            lane = eland.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)
+
+        xml = eland.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.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)
+            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])
+
+    def test_runfolder(self):
+        runs = runfolder.get_runs(self.runfolder_dir)
+
+        # do we get the flowcell id from the filename?
+        self.failUnlessEqual(len(runs), 1)
+        name = 'run_3021JAAXX_%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-ipar130.htm b/htsworkflow/pipelines/test/testdata/Summary-ipar130.htm
new file mode 100644 (file)
index 0000000..c9eaca9
--- /dev/null
+++ b/htsworkflow/pipelines/test/testdata/Summary-ipar130.htm
@@ -0,0 +1,9325 @@
+<html><body>
+<h1>Summary Information For Experiment 090313_HWI-EAS229_0101_3021JAAXX</h1>
+<h2>Chip Summary</h2>
+<table border="1" cellpadding="5">
+<tr>
+<td>Machine</td>
+<td>HWI-EAS229</td>
+</tr>
+<tr>
+<td>Run Folder</td>
+<td>090313_HWI-EAS229_0101_3021JAAXX</td>
+</tr>
+<tr>
+<td>Chip ID</td>
+<td>unknown</td>
+</tr>
+</table>
+<h2>Chip Results Summary</h2>
+<table border="1" cellpadding="5">
+<tr>
+<td>Clusters</td>
+<td>Clusters (PF)</td>
+<td>Yield (kbases)</td>
+</tr>
+<tr>
+<td>128921986</td>
+<td>21940781</td>
+<td>811809</td>
+</tr>
+</table>
+<h2>Lane Parameter Summary</h2>
+<table border="1" cellpadding="5">
+<tr>
+<td>Lane </td>
+<td>Sample ID</td>
+<td>Sample Target</td>
+<td>Sample Type</td>
+<td>Length</td>
+<td>Filter</td>
+<td>Chast. Thresh.</td>
+<td>Num Tiles</td>
+<td>Tiles</td>
+</tr>
+<tr>
+<td>1</td>
+<td>unknown</td>
+<td>mm9</td>
+<td>ELAND_EXTENDED</td>
+<td>37</td>
+<td>'((FAILED_CHASTITY&lt;=1.000000))'</td>
+<td>0.600000</td>
+<td>100</td>
+<td><a href="#Lane1">
+        Lane 1</a></td>
+</tr>
+<tr>
+<td>2</td>
+<td>unknown</td>
+<td>elegans190</td>
+<td>ELAND_EXTENDED</td>
+<td>37</td>
+<td>'((FAILED_CHASTITY&lt;=1.000000))'</td>
+<td>0.600000</td>
+<td>100</td>
+<td><a href="#Lane2">
+        Lane 2</a></td>
+</tr>
+<tr>
+<td>3</td>
+<td>unknown</td>
+<td>elegans190</td>
+<td>ELAND_EXTENDED</td>
+<td>37</td>
+<td>'((FAILED_CHASTITY&lt;=1.000000))'</td>
+<td>0.600000</td>
+<td>100</td>
+<td><a href="#Lane3">
+        Lane 3</a></td>
+</tr>
+<tr>
+<td>4</td>
+<td>unknown</td>
+<td>elegans190</td>
+<td>ELAND_EXTENDED</td>
+<td>37</td>
+<td>'((FAILED_CHASTITY&lt;=1.000000))'</td>
+<td>0.600000</td>
+<td>100</td>
+<td><a href="#Lane4">
+        Lane 4</a></td>
+</tr>
+<tr>
+<td>5</td>
+<td>unknown</td>
+<td>elegans190</td>
+<td>ELAND_EXTENDED</td>
+<td>37</td>
+<td>'((FAILED_CHASTITY&lt;=1.000000))'</td>
+<td>0.600000</td>
+<td>100</td>
+<td><a href="#Lane5">
+        Lane 5</a></td>
+</tr>
+<tr>
+<td>6</td>
+<td>unknown</td>
+<td>elegans190</td>
+<td>ELAND_EXTENDED</td>
+<td>37</td>
+<td>'((FAILED_CHASTITY&lt;=1.000000))'</td>
+<td>0.600000</td>
+<td>100</td>
+<td><a href="#Lane6">
+        Lane 6</a></td>
+</tr>
+<tr>
+<td>7</td>
+<td>unknown</td>
+<td>elegans190</td>
+<td>ELAND_EXTENDED</td>
+<td>37</td>
+<td>'((FAILED_CHASTITY&lt;=1.000000))'</td>
+<td>0.600000</td>
+<td>100</td>
+<td><a href="#Lane7">
+        Lane 7</a></td>
+</tr>
+<tr>
+<td>8</td>
+<td>unknown</td>
+<td>mm9</td>
+<td>ELAND_EXTENDED</td>
+<td>37</td>
+<td>'((FAILED_CHASTITY&lt;=1.000000))'</td>
+<td>0.600000</td>
+<td>100</td>
+<td><a href="#Lane8">
+        Lane 8</a></td>
+</tr>
+</table>
+<h2>Lane Results Summary</h2>
+<table border="1" cellpadding="5">
+<tr>
+<td colspan="2">Lane Info</td>
+<td colspan="8">Tile Mean +/- SD for Lane</td>
+</tr>
+<tr>
+<td>Lane </td>
+<td>Lane Yield (kbases) </td>
+<td>Clusters (raw)</td>
+<td>Clusters (PF) </td>
+<td>1st Cycle Int (PF) </td>
+<td>% intensity after 20 cycles (PF) </td>
+<td>% PF Clusters </td>
+<td>% Align (PF) </td>
+<td>Alignment Score (PF) </td>
+<td>% Error Rate (PF) </td>
+</tr>
+<tr>
+<td>1</td>
+<td>57684</td>
+<td>126910 +/- 4300</td>
+<td>15590 +/- 4874</td>
+<td>54 +/- 14</td>
+<td>124.36 +/- 27.05</td>
+<td>12.29 +/- 3.82</td>
+<td>7.36 +/- 1.84</td>
+<td>5.92 +/- 1.64</td>
+<td>4.52 +/- 0.58</td>
+</tr>
+<tr>
+<td>2</td>
+<td>78644</td>
+<td>165739 +/- 6792</td>
+<td>21255 +/- 5034</td>
+<td>76 +/- 16</td>
+<td>88.44 +/- 20.59</td>
+<td>12.94 +/- 3.52</td>
+<td>13.55 +/- 0.83</td>
+<td>12.33 +/- 0.84</td>
+<td>5.05 +/- 0.24</td>
+</tr>
+<tr>
+<td>3</td>
+<td>68671</td>
+<td>196565 +/- 8216</td>
+<td>18559 +/- 5413</td>
+<td>106 +/- 19</td>
+<td>78.51 +/- 19.65</td>
+<td>9.41 +/- 2.64</td>
+<td>1.07 +/- 0.10</td>
+<td>0.81 +/- 0.08</td>
+<td>6.27 +/- 0.46</td>
+</tr>
+<tr>
+<td>4</td>
+<td>126273</td>
+<td>153897 +/- 8501</td>
+<td>34128 +/- 7984</td>
+<td>75 +/- 12</td>
+<td>110.44 +/- 26.03</td>
+<td>22.13 +/- 4.82</td>
+<td>4.53 +/- 0.36</td>
+<td>4.51 +/- 0.38</td>
+<td>3.58 +/- 0.22</td>
+</tr>
+<tr>
+<td>5</td>
+<td>116257</td>
+<td>135536 +/- 3908</td>
+<td>31420 +/- 5039</td>
+<td>70 +/- 10</td>
+<td>116.68 +/- 24.46</td>
+<td>23.21 +/- 3.84</td>
+<td>4.25 +/- 0.39</td>
+<td>4.19 +/- 0.41</td>
+<td>3.62 +/- 0.26</td>
+</tr>
+<tr>
+<td>6</td>
+<td>159230</td>
+<td>154083 +/- 9315</td>
+<td>43035 +/- 10193</td>
+<td>79 +/- 14</td>
+<td>123.00 +/- 28.80</td>
+<td>27.76 +/- 5.59</td>
+<td>3.64 +/- 0.30</td>
+<td>3.53 +/- 0.31</td>
+<td>3.48 +/- 0.30</td>
+</tr>
+<tr>
+<td>7</td>
+<td>180779</td>
+<td>159991 +/- 9292</td>
+<td>48859 +/- 8420</td>
+<td>82 +/- 14</td>
+<td>121.76 +/- 24.75</td>
+<td>30.47 +/- 4.53</td>
+<td>0.86 +/- 0.10</td>
+<td>0.58 +/- 0.07</td>
+<td>3.11 +/- 0.36</td>
+</tr>
+<tr>
+<td>8</td>
+<td>24267</td>
+<td>198479 +/- 17671</td>
+<td>6625 +/- 1773</td>
+<td>122 +/- 14</td>
+<td>73.80 +/- 14.41</td>
+<td>3.30 +/- 0.68</td>
+<td>48.65 +/- 5.61</td>
+<td>43.07 +/- 6.50</td>
+<td>3.28 +/- 0.29</td>
+</tr>
+<tr><td colspan="13">Tile mean across chip</td></tr>
+<tr>
+<td colspan="2">Average</td>
+<td>161400</td>
+<td>27434</td>
+<td>83</td>
+<td>104.62</td>
+<td>17.69</td>
+<td>10.49</td>
+<td>9.37</td>
+<td>4.11</td>
+</tr>
+</table>
+<h2>Expanded Lane Summary</h2>
+<table border="1" cellpadding="5">
+<tr>
+<td colspan="2">Lane Info</td>
+<td colspan="2">Phasing Info</td>
+<td colspan="2">Raw Data (tile mean)</td>
+<td colspan="7">Filtered Data (tile mean)</td>
+</tr>
+<tr>
+<td>Lane </td>
+<td> Clusters (tile mean) (raw) </td>
+<td>% Phasing </td>
+<td>% Prephasing </td>
+<td>% Error Rate (raw) </td>
+<td> Equiv Perfect Clusters (raw) </td>
+<td>% retained </td>
+<td>Cycle 2-4 Av Int (PF) </td>
+<td>Cycle 2-10 Av % Loss (PF) </td>
+<td>Cycle 10-20 Av % Loss (PF) </td>
+<td>% Align (PF) </td>
+<td> % Error Rate (PF) </td>
+<td> Equiv Perfect Clusters (PF) </td>
+</tr>
+<tr>
+<td>1</td>
+<td>126911</td>
+<td>0.4800</td>
+<td>0.3000</td>
+<td>5.57</td>
+<td>3076</td>
+<td>12.29</td>
+<td>65.81 +/- 15.68</td>
+<td>-0.47 +/- 1.84</td>
+<td>2.12 +/- 1.13</td>
+<td>7.36</td>
+<td>4.52</td>
+<td>1075</td>
+</tr>
+<tr>
+<td>2</td>
+<td>165740</td>
+<td>0.4800</td>
+<td>0.3000</td>
+<td>5.96</td>
+<td>6076</td>
+<td>12.94</td>
+<td>76.73 +/- 12.15</td>
+<td>1.47 +/- 1.92</td>
+<td>1.21 +/- 1.90</td>
+<td>13.55</td>
+<td>5.05</td>
+<td>2541</td>
+</tr>
+<tr>
+<td>3</td>
+<td>196566</td>
+<td>0.4800</td>
+<td>0.3000</td>
+<td>7.47</td>
+<td>488</td>
+<td>9.41</td>
+<td>103.17 +/- 13.33</td>
+<td>2.71 +/- 1.99</td>
+<td>1.26 +/- 1.88</td>
+<td>1.07</td>
+<td>6.27</td>
+<td>173</td>
+</tr>
+<tr>
+<td>4</td>
+<td>153898</td>
+<td>0.4800</td>
+<td>0.3000</td>
+<td>4.79</td>
+<td>3322</td>
+<td>22.13</td>
+<td>95.07 +/- 11.42</td>
+<td>1.28 +/- 2.12</td>
+<td>2.69 +/- 1.55</td>
+<td>4.53</td>
+<td>3.58</td>
+<td>1402</td>
+</tr>
+<tr>
+<td>5</td>
+<td>135536</td>
+<td>0.4800</td>
+<td>0.3000</td>
+<td>4.75</td>
+<td>2582</td>
+<td>23.21</td>
+<td>87.51 +/- 10.87</td>
+<td>1.01 +/- 2.07</td>
+<td>2.00 +/- 1.53</td>
+<td>4.25</td>
+<td>3.62</td>
+<td>1221</td>
+</tr>
+<tr>
+<td>6</td>
+<td>154084</td>
+<td>0.4800</td>
+<td>0.3000</td>
+<td>4.76</td>
+<td>3270</td>
+<td>27.76</td>
+<td>109.23 +/- 14.97</td>
+<td>1.17 +/- 2.12</td>
+<td>3.01 +/- 1.73</td>
+<td>3.64</td>
+<td>3.48</td>
+<td>1422</td>
+</tr>
+<tr>
+<td>7</td>
+<td>159991</td>
+<td>0.4800</td>
+<td>0.3000</td>
+<td>4.36</td>
+<td>1001</td>
+<td>30.47</td>
+<td>109.94 +/- 12.23</td>
+<td>1.48 +/- 2.32</td>
+<td>2.30 +/- 2.47</td>
+<td>0.86</td>
+<td>3.11</td>
+<td>394</td>
+</tr>
+<tr>
+<td>8</td>
+<td>198479</td>
+<td>0.4800</td>
+<td>0.3000</td>
+<td>5.16</td>
+<td>10876</td>
+<td>3.30</td>
+<td>115.81 +/- 11.51</td>
+<td>2.09 +/- 1.67</td>
+<td>1.19 +/- 2.91</td>
+<td>48.65</td>
+<td>3.28</td>
+<td>2958</td>
+</tr>
+</table>
+<a name="Lane"><h2>Lane 1</h2></a><table border="1" cellpadding="5">
+<tr>
+<td colspan="1">Lane </td>
+<td colspan="1">Tile</td>
+<td colspan="1">Clusters (raw)</td>
+<td colspan="1">Av 1st Cycle Int (PF)</td>
+<td colspan="1">Av % intensity after 20 cycles (PF)</td>
+<td colspan="1">% PF Clusters </td>
+<td colspan="1">% Align (PF) </td>
+<td colspan="1">Av Alignment Score (PF)</td>
+<td colspan="1">% Error Rate (PF) </td>
+</tr>
+<tr>
+<td>1</td>
+<td>0001</td>
+<td>137187</td>
+<td>30.60</td>
+<td>217.24</td>
+<td>5.91</td>
+<td>4.17</td>
+<td>2.48</td>
+<td>5.11</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0002</td>
+<td>128576</td>
+<td>29.07</td>
+<td>106.36</td>
+<td>5.06</td>
+<td>3.23</td>
+<td>2.29</td>
+<td>6.25</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0003</td>
+<td>131374</td>
+<td>26.72</td>
+<td>120.11</td>
+<td>5.04</td>
+<td>3.34</td>
+<td>2.44</td>
+<td>5.43</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0004</td>
+<td>129258</td>
+<td>28.42</td>
+<td>121.90</td>
+<td>4.57</td>
+<td>3.10</td>
+<td>2.05</td>
+<td>6.42</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0005</td>
+<td>127183</td>
+<td>31.92</td>
+<td>128.19</td>
+<td>5.23</td>
+<td>3.66</td>
+<td>2.62</td>
+<td>5.68</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0006</td>
+<td>126810</td>
+<td>35.93</td>
+<td>107.59</td>
+<td>5.37</td>
+<td>3.54</td>
+<td>2.44</td>
+<td>5.83</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0007</td>
+<td>127600</td>
+<td>34.18</td>
+<td>128.16</td>
+<td>5.69</td>
+<td>3.68</td>
+<td>2.62</td>
+<td>5.90</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0008</td>
+<td>127404</td>
+<td>45.38</td>
+<td>81.27</td>
+<td>6.10</td>
+<td>3.47</td>
+<td>2.47</td>
+<td>5.65</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0009</td>
+<td>126616</td>
+<td>36.17</td>
+<td>126.68</td>
+<td>7.14</td>
+<td>4.21</td>
+<td>3.05</td>
+<td>5.36</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0010</td>
+<td>126528</td>
+<td>33.95</td>
+<td>143.96</td>
+<td>7.82</td>
+<td>4.62</td>
+<td>3.49</td>
+<td>5.31</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0011</td>
+<td>127241</td>
+<td>42.65</td>
+<td>104.63</td>
+<td>8.20</td>
+<td>4.81</td>
+<td>3.39</td>
+<td>5.50</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0012</td>
+<td>130637</td>
+<td>33.40</td>
+<td>151.65</td>
+<td>8.72</td>
+<td>4.43</td>
+<td>3.43</td>
+<td>4.97</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0013</td>
+<td>134557</td>
+<td>29.95</td>
+<td>152.84</td>
+<td>8.36</td>
+<td>4.69</td>
+<td>3.52</td>
+<td>5.34</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0014</td>
+<td>126519</td>
+<td>33.45</td>
+<td>124.07</td>
+<td>8.65</td>
+<td>5.17</td>
+<td>3.93</td>
+<td>5.14</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0015</td>
+<td>132462</td>
+<td>31.15</td>
+<td>167.26</td>
+<td>8.33</td>
+<td>5.70</td>
+<td>4.42</td>
+<td>4.71</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0016</td>
+<td>130705</td>
+<td>57.93</td>
+<td>75.96</td>
+<td>7.87</td>
+<td>6.65</td>
+<td>4.97</td>
+<td>5.12</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0017</td>
+<td>134599</td>
+<td>40.48</td>
+<td>139.53</td>
+<td>9.01</td>
+<td>6.53</td>
+<td>5.17</td>
+<td>4.46</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0018</td>
+<td>127550</td>
+<td>41.45</td>
+<td>130.16</td>
+<td>8.48</td>
+<td>6.34</td>
+<td>4.95</td>
+<td>4.89</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0019</td>
+<td>126880</td>
+<td>54.35</td>
+<td>108.00</td>
+<td>9.76</td>
+<td>6.03</td>
+<td>4.59</td>
+<td>4.71</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0020</td>
+<td>126811</td>
+<td>38.05</td>
+<td>122.40</td>
+<td>8.72</td>
+<td>6.23</td>
+<td>4.88</td>
+<td>4.88</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0021</td>
+<td>130562</td>
+<td>34.10</td>
+<td>140.98</td>
+<td>8.79</td>
+<td>5.43</td>
+<td>4.18</td>
+<td>5.19</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0022</td>
+<td>126586</td>
+<td>42.05</td>
+<td>99.64</td>
+<td>8.31</td>
+<td>5.63</td>
+<td>4.16</td>
+<td>5.41</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0023</td>
+<td>124746</td>
+<td>38.08</td>
+<td>108.08</td>
+<td>8.96</td>
+<td>5.85</td>
+<td>4.35</td>
+<td>5.07</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0024</td>
+<td>125450</td>
+<td>45.17</td>
+<td>109.24</td>
+<td>8.75</td>
+<td>5.39</td>
+<td>3.84</td>
+<td>5.37</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0025</td>
+<td>124739</td>
+<td>45.58</td>
+<td>109.05</td>
+<td>9.17</td>
+<td>6.51</td>
+<td>5.04</td>
+<td>4.72</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0026</td>
+<td>120845</td>
+<td>47.38</td>
+<td>104.96</td>
+<td>9.93</td>
+<td>5.87</td>
+<td>4.63</td>
+<td>4.63</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0027</td>
+<td>122292</td>
+<td>47.65</td>
+<td>104.56</td>
+<td>8.95</td>
+<td>6.27</td>
+<td>4.75</td>
+<td>4.91</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0028</td>
+<td>125133</td>
+<td>48.95</td>
+<td>128.50</td>
+<td>9.63</td>
+<td>7.69</td>
+<td>5.96</td>
+<td>4.74</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0029</td>
+<td>121876</td>
+<td>47.25</td>
+<td>123.44</td>
+<td>9.56</td>
+<td>7.61</td>
+<td>6.02</td>
+<td>4.53</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0030</td>
+<td>129067</td>
+<td>36.33</td>
+<td>140.61</td>
+<td>8.70</td>
+<td>7.21</td>
+<td>5.50</td>
+<td>4.83</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0031</td>
+<td>126997</td>
+<td>39.03</td>
+<td>148.37</td>
+<td>9.88</td>
+<td>7.62</td>
+<td>6.08</td>
+<td>4.44</td>
+</tr>
+<tr>
+<td>1</td>
+<td>0032</td>
+<td>123097</td>
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+</tr>
+</table>
+<a name="Lane"><h2>Lane 2</h2></a><table border="1" cellpadding="5">
+<tr>
+<td colspan="1">Lane </td>
+<td colspan="1">Tile</td>
+<td colspan="1">Clusters (raw)</td>
+<td colspan="1">Av 1st Cycle Int (PF)</td>
+<td colspan="1">Av % intensity after 20 cycles (PF)</td>
+<td colspan="1">% PF Clusters </td>
+<td colspan="1">% Align (PF) </td>
+<td colspan="1">Av Alignment Score (PF)</td>
+<td colspan="1">% Error Rate (PF) </td>
+</tr>
+<tr>
+<td>2</td>
+<td>0001</td>
+<td>170860</td>
+<td>62.62</td>
+<td>102.59</td>
+<td>8.71</td>
+<td>11.68</td>
+<td>10.42</td>
+<td>5.37</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0002</td>
+<td>168047</td>
+<td>58.73</td>
+<td>101.28</td>
+<td>8.09</td>
+<td>12.01</td>
+<td>11.15</td>
+<td>4.68</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0003</td>
+<td>165611</td>
+<td>55.85</td>
+<td>114.91</td>
+<td>8.35</td>
+<td>12.79</td>
+<td>11.92</td>
+<td>4.72</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0004</td>
+<td>171147</td>
+<td>54.33</td>
+<td>89.00</td>
+<td>7.76</td>
+<td>12.51</td>
+<td>11.06</td>
+<td>5.30</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0005</td>
+<td>166702</td>
+<td>51.80</td>
+<td>98.41</td>
+<td>7.98</td>
+<td>12.93</td>
+<td>11.85</td>
+<td>4.94</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0006</td>
+<td>171530</td>
+<td>56.83</td>
+<td>93.14</td>
+<td>9.66</td>
+<td>13.46</td>
+<td>12.56</td>
+<td>4.80</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0007</td>
+<td>175956</td>
+<td>63.45</td>
+<td>86.25</td>
+<td>9.49</td>
+<td>13.15</td>
+<td>12.13</td>
+<td>4.82</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0008</td>
+<td>178220</td>
+<td>70.48</td>
+<td>77.55</td>
+<td>10.04</td>
+<td>12.91</td>
+<td>11.64</td>
+<td>5.18</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0009</td>
+<td>172420</td>
+<td>54.58</td>
+<td>107.88</td>
+<td>9.77</td>
+<td>12.79</td>
+<td>11.62</td>
+<td>5.02</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0010</td>
+<td>174119</td>
+<td>48.60</td>
+<td>103.65</td>
+<td>8.70</td>
+<td>12.46</td>
+<td>11.47</td>
+<td>4.93</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0011</td>
+<td>173224</td>
+<td>64.15</td>
+<td>83.09</td>
+<td>9.38</td>
+<td>12.71</td>
+<td>11.42</td>
+<td>5.11</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0012</td>
+<td>168945</td>
+<td>56.97</td>
+<td>76.92</td>
+<td>8.67</td>
+<td>13.60</td>
+<td>12.00</td>
+<td>5.37</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0013</td>
+<td>169698</td>
+<td>54.75</td>
+<td>110.09</td>
+<td>8.75</td>
+<td>12.89</td>
+<td>11.82</td>
+<td>4.99</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0014</td>
+<td>176430</td>
+<td>56.88</td>
+<td>76.44</td>
+<td>9.00</td>
+<td>12.51</td>
+<td>11.30</td>
+<td>5.14</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0015</td>
+<td>173392</td>
+<td>54.30</td>
+<td>109.39</td>
+<td>10.27</td>
+<td>13.47</td>
+<td>12.19</td>
+<td>5.16</td>
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+<td>2</td>
+<td>0016</td>
+<td>173752</td>
+<td>86.25</td>
+<td>51.80</td>
+<td>11.49</td>
+<td>13.88</td>
+<td>12.43</td>
+<td>5.24</td>
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+<tr>
+<td>2</td>
+<td>0017</td>
+<td>172890</td>
+<td>50.90</td>
+<td>95.19</td>
+<td>10.72</td>
+<td>12.90</td>
+<td>11.72</td>
+<td>5.07</td>
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+<tr>
+<td>2</td>
+<td>0018</td>
+<td>173359</td>
+<td>60.30</td>
+<td>87.69</td>
+<td>10.50</td>
+<td>13.45</td>
+<td>11.98</td>
+<td>5.21</td>
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+<tr>
+<td>2</td>
+<td>0019</td>
+<td>172871</td>
+<td>80.22</td>
+<td>49.80</td>
+<td>11.16</td>
+<td>12.86</td>
+<td>11.12</td>
+<td>5.26</td>
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+<td>2</td>
+<td>0020</td>
+<td>167500</td>
+<td>61.65</td>
+<td>109.57</td>
+<td>10.62</td>
+<td>13.95</td>
+<td>12.73</td>
+<td>4.99</td>
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+<td>0021</td>
+<td>173061</td>
+<td>64.42</td>
+<td>86.03</td>
+<td>11.19</td>
+<td>13.38</td>
+<td>11.79</td>
+<td>5.27</td>
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+<td>2</td>
+<td>0022</td>
+<td>171387</td>
+<td>64.05</td>
+<td>96.57</td>
+<td>10.12</td>
+<td>13.48</td>
+<td>12.34</td>
+<td>5.01</td>
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+<td>2</td>
+<td>0023</td>
+<td>178113</td>
+<td>52.73</td>
+<td>81.41</td>
+<td>10.64</td>
+<td>12.86</td>
+<td>11.90</td>
+<td>4.82</td>
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+<td>2</td>
+<td>0024</td>
+<td>172379</td>
+<td>65.38</td>
+<td>74.99</td>
+<td>9.79</td>
+<td>13.20</td>
+<td>12.05</td>
+<td>5.08</td>
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+<td>2</td>
+<td>0025</td>
+<td>170661</td>
+<td>59.25</td>
+<td>90.97</td>
+<td>10.09</td>
+<td>13.21</td>
+<td>12.08</td>
+<td>5.06</td>
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+<td>2</td>
+<td>0026</td>
+<td>169053</td>
+<td>73.67</td>
+<td>66.98</td>
+<td>10.58</td>
+<td>13.00</td>
+<td>11.86</td>
+<td>4.82</td>
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+<tr>
+<td>2</td>
+<td>0027</td>
+<td>166006</td>
+<td>69.58</td>
+<td>99.82</td>
+<td>9.55</td>
+<td>13.00</td>
+<td>12.04</td>
+<td>4.98</td>
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+<td>2</td>
+<td>0028</td>
+<td>165399</td>
+<td>79.20</td>
+<td>83.78</td>
+<td>11.65</td>
+<td>13.70</td>
+<td>12.73</td>
+<td>4.79</td>
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+<td>0029</td>
+<td>164659</td>
+<td>79.80</td>
+<td>59.65</td>
+<td>9.48</td>
+<td>14.10</td>
+<td>12.84</td>
+<td>5.11</td>
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+<td>163419</td>
+<td>74.82</td>
+<td>84.20</td>
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+<td>161881</td>
+<td>70.33</td>
+<td>74.65</td>
+<td>12.62</td>
+<td>14.05</td>
+<td>12.82</td>
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+<td>162247</td>
+<td>63.85</td>
+<td>78.19</td>
+<td>13.17</td>
+<td>14.19</td>
+<td>13.00</td>
+<td>5.15</td>
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+<td>2</td>
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+<td>163268</td>
+<td>93.03</td>
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+<td>14.16</td>
+<td>12.69</td>
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+<td>161905</td>
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+<td>74.01</td>
+<td>13.85</td>
+<td>14.15</td>
+<td>13.25</td>
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+<td>160276</td>
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+<td>159586</td>
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+<td>12.84</td>
+<td>13.92</td>
+<td>12.45</td>
+<td>5.41</td>
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+<td>158070</td>
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+<td>2</td>
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+<td>158382</td>
+<td>78.83</td>
+<td>110.37</td>
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+<td>13.18</td>
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+<td>13.60</td>
+<td>12.44</td>
+<td>4.93</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0071</td>
+<td>161780</td>
+<td>74.80</td>
+<td>90.91</td>
+<td>13.03</td>
+<td>14.13</td>
+<td>12.81</td>
+<td>5.38</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0072</td>
+<td>165226</td>
+<td>100.00</td>
+<td>96.10</td>
+<td>12.45</td>
+<td>14.19</td>
+<td>13.05</td>
+<td>5.12</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0073</td>
+<td>167863</td>
+<td>79.80</td>
+<td>83.36</td>
+<td>15.37</td>
+<td>14.03</td>
+<td>13.12</td>
+<td>4.85</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0074</td>
+<td>165913</td>
+<td>101.12</td>
+<td>54.68</td>
+<td>13.22</td>
+<td>14.19</td>
+<td>12.85</td>
+<td>5.15</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0075</td>
+<td>164802</td>
+<td>87.07</td>
+<td>93.11</td>
+<td>11.53</td>
+<td>13.77</td>
+<td>12.31</td>
+<td>5.10</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0076</td>
+<td>166823</td>
+<td>77.25</td>
+<td>66.38</td>
+<td>10.96</td>
+<td>13.49</td>
+<td>12.54</td>
+<td>4.87</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0077</td>
+<td>163962</td>
+<td>76.15</td>
+<td>69.63</td>
+<td>14.11</td>
+<td>14.17</td>
+<td>13.26</td>
+<td>4.85</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0078</td>
+<td>168087</td>
+<td>100.95</td>
+<td>81.40</td>
+<td>12.41</td>
+<td>13.36</td>
+<td>12.18</td>
+<td>4.90</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0079</td>
+<td>165569</td>
+<td>89.40</td>
+<td>58.89</td>
+<td>11.73</td>
+<td>13.73</td>
+<td>12.41</td>
+<td>5.03</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0080</td>
+<td>171955</td>
+<td>74.95</td>
+<td>102.03</td>
+<td>13.60</td>
+<td>12.95</td>
+<td>12.11</td>
+<td>4.78</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0081</td>
+<td>166480</td>
+<td>83.35</td>
+<td>96.94</td>
+<td>12.50</td>
+<td>12.76</td>
+<td>11.20</td>
+<td>5.42</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0082</td>
+<td>171089</td>
+<td>80.08</td>
+<td>64.10</td>
+<td>12.31</td>
+<td>12.98</td>
+<td>11.77</td>
+<td>5.16</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0083</td>
+<td>168658</td>
+<td>69.50</td>
+<td>122.05</td>
+<td>10.71</td>
+<td>13.22</td>
+<td>12.13</td>
+<td>4.99</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0084</td>
+<td>166958</td>
+<td>87.22</td>
+<td>68.13</td>
+<td>10.29</td>
+<td>12.87</td>
+<td>11.46</td>
+<td>5.13</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0085</td>
+<td>168593</td>
+<td>58.42</td>
+<td>82.97</td>
+<td>8.82</td>
+<td>12.17</td>
+<td>11.34</td>
+<td>4.81</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0086</td>
+<td>170048</td>
+<td>94.28</td>
+<td>60.33</td>
+<td>8.64</td>
+<td>12.48</td>
+<td>10.67</td>
+<td>5.22</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0087</td>
+<td>173518</td>
+<td>107.08</td>
+<td>51.27</td>
+<td>9.22</td>
+<td>12.73</td>
+<td>11.01</td>
+<td>5.26</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0088</td>
+<td>168833</td>
+<td>63.73</td>
+<td>131.42</td>
+<td>12.05</td>
+<td>12.97</td>
+<td>12.16</td>
+<td>4.62</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0089</td>
+<td>176200</td>
+<td>82.85</td>
+<td>63.79</td>
+<td>9.28</td>
+<td>12.75</td>
+<td>11.23</td>
+<td>5.20</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0090</td>
+<td>172411</td>
+<td>72.20</td>
+<td>121.95</td>
+<td>14.19</td>
+<td>13.10</td>
+<td>12.33</td>
+<td>4.75</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0091</td>
+<td>171660</td>
+<td>78.12</td>
+<td>82.91</td>
+<td>11.41</td>
+<td>13.20</td>
+<td>12.45</td>
+<td>4.73</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0092</td>
+<td>168257</td>
+<td>79.22</td>
+<td>66.93</td>
+<td>10.41</td>
+<td>13.04</td>
+<td>11.88</td>
+<td>4.96</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0093</td>
+<td>171960</td>
+<td>81.53</td>
+<td>75.80</td>
+<td>10.00</td>
+<td>12.92</td>
+<td>12.23</td>
+<td>4.62</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0094</td>
+<td>171243</td>
+<td>88.10</td>
+<td>68.13</td>
+<td>9.17</td>
+<td>12.34</td>
+<td>11.26</td>
+<td>5.08</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0095</td>
+<td>173740</td>
+<td>70.65</td>
+<td>90.59</td>
+<td>10.44</td>
+<td>12.90</td>
+<td>12.14</td>
+<td>4.76</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0096</td>
+<td>174659</td>
+<td>90.72</td>
+<td>90.88</td>
+<td>12.69</td>
+<td>12.94</td>
+<td>12.15</td>
+<td>4.56</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0097</td>
+<td>177182</td>
+<td>71.08</td>
+<td>114.70</td>
+<td>12.39</td>
+<td>12.77</td>
+<td>12.36</td>
+<td>4.43</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0098</td>
+<td>173849</td>
+<td>63.10</td>
+<td>131.34</td>
+<td>11.79</td>
+<td>12.33</td>
+<td>11.69</td>
+<td>4.50</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0099</td>
+<td>176016</td>
+<td>93.23</td>
+<td>92.01</td>
+<td>11.95</td>
+<td>12.38</td>
+<td>11.23</td>
+<td>4.99</td>
+</tr>
+<tr>
+<td>2</td>
+<td>0100</td>
+<td>174587</td>
+<td>87.28</td>
+<td>95.79</td>
+<td>10.07</td>
+<td>11.09</td>
+<td>10.10</td>
+<td>4.89</td>
+</tr>
+</table>
+<a name="Lane"><h2>Lane 3</h2></a><table border="1" cellpadding="5">
+<tr>
+<td colspan="1">Lane </td>
+<td colspan="1">Tile</td>
+<td colspan="1">Clusters (raw)</td>
+<td colspan="1">Av 1st Cycle Int (PF)</td>
+<td colspan="1">Av % intensity after 20 cycles (PF)</td>
+<td colspan="1">% PF Clusters </td>
+<td colspan="1">% Align (PF) </td>
+<td colspan="1">Av Alignment Score (PF)</td>
+<td colspan="1">% Error Rate (PF) </td>
+</tr>
+<tr>
+<td>3</td>
+<td>0001</td>
+<td>203758</td>
+<td>88.12</td>
+<td>74.13</td>
+<td>6.39</td>
+<td>1.00</td>
+<td>0.70</td>
+<td>6.55</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0002</td>
+<td>186222</td>
+<td>94.22</td>
+<td>65.83</td>
+<td>6.73</td>
+<td>1.07</td>
+<td>0.95</td>
+<td>4.76</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0003</td>
+<td>182117</td>
+<td>83.00</td>
+<td>100.81</td>
+<td>7.14</td>
+<td>1.32</td>
+<td>0.95</td>
+<td>6.20</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0004</td>
+<td>185998</td>
+<td>71.10</td>
+<td>107.91</td>
+<td>7.06</td>
+<td>1.19</td>
+<td>0.95</td>
+<td>5.98</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0005</td>
+<td>190532</td>
+<td>74.88</td>
+<td>104.04</td>
+<td>6.79</td>
+<td>1.00</td>
+<td>0.73</td>
+<td>6.18</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0006</td>
+<td>180425</td>
+<td>73.30</td>
+<td>104.33</td>
+<td>6.62</td>
+<td>0.90</td>
+<td>0.75</td>
+<td>5.91</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0007</td>
+<td>186680</td>
+<td>93.67</td>
+<td>81.10</td>
+<td>6.50</td>
+<td>1.04</td>
+<td>0.84</td>
+<td>5.98</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0008</td>
+<td>194001</td>
+<td>89.23</td>
+<td>61.39</td>
+<td>5.86</td>
+<td>1.04</td>
+<td>0.81</td>
+<td>6.44</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0009</td>
+<td>193360</td>
+<td>94.65</td>
+<td>85.16</td>
+<td>6.54</td>
+<td>0.99</td>
+<td>0.71</td>
+<td>6.31</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0010</td>
+<td>181288</td>
+<td>83.68</td>
+<td>78.67</td>
+<td>6.56</td>
+<td>1.07</td>
+<td>0.84</td>
+<td>6.66</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0011</td>
+<td>187873</td>
+<td>94.00</td>
+<td>82.45</td>
+<td>6.23</td>
+<td>1.01</td>
+<td>0.75</td>
+<td>5.91</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0012</td>
+<td>185405</td>
+<td>70.35</td>
+<td>80.56</td>
+<td>6.46</td>
+<td>1.09</td>
+<td>0.78</td>
+<td>6.35</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0013</td>
+<td>162638</td>
+<td>64.42</td>
+<td>80.87</td>
+<td>4.88</td>
+<td>1.06</td>
+<td>0.83</td>
+<td>6.76</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0014</td>
+<td>197166</td>
+<td>95.83</td>
+<td>60.68</td>
+<td>6.70</td>
+<td>1.05</td>
+<td>0.83</td>
+<td>6.14</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0015</td>
+<td>193488</td>
+<td>83.35</td>
+<td>69.86</td>
+<td>7.03</td>
+<td>1.07</td>
+<td>0.84</td>
+<td>6.26</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0016</td>
+<td>205537</td>
+<td>79.45</td>
+<td>116.65</td>
+<td>8.99</td>
+<td>1.08</td>
+<td>0.85</td>
+<td>6.17</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0017</td>
+<td>192552</td>
+<td>108.75</td>
+<td>55.95</td>
+<td>7.38</td>
+<td>0.89</td>
+<td>0.63</td>
+<td>7.00</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0018</td>
+<td>200383</td>
+<td>103.70</td>
+<td>59.23</td>
+<td>7.50</td>
+<td>1.00</td>
+<td>0.75</td>
+<td>5.93</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0019</td>
+<td>201926</td>
+<td>81.47</td>
+<td>66.40</td>
+<td>7.42</td>
+<td>0.85</td>
+<td>0.69</td>
+<td>5.70</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0020</td>
+<td>182571</td>
+<td>81.12</td>
+<td>70.39</td>
+<td>7.16</td>
+<td>1.13</td>
+<td>0.83</td>
+<td>6.48</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0021</td>
+<td>186698</td>
+<td>82.67</td>
+<td>80.04</td>
+<td>7.10</td>
+<td>1.00</td>
+<td>0.77</td>
+<td>5.77</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0022</td>
+<td>189532</td>
+<td>84.53</td>
+<td>80.95</td>
+<td>7.75</td>
+<td>1.00</td>
+<td>0.77</td>
+<td>6.23</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0023</td>
+<td>188323</td>
+<td>88.72</td>
+<td>73.12</td>
+<td>7.83</td>
+<td>1.15</td>
+<td>0.85</td>
+<td>6.81</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0024</td>
+<td>178383</td>
+<td>83.00</td>
+<td>119.94</td>
+<td>6.88</td>
+<td>0.97</td>
+<td>0.74</td>
+<td>5.47</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0025</td>
+<td>192049</td>
+<td>97.12</td>
+<td>73.72</td>
+<td>8.40</td>
+<td>0.97</td>
+<td>0.77</td>
+<td>5.42</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0026</td>
+<td>197028</td>
+<td>98.62</td>
+<td>92.19</td>
+<td>7.99</td>
+<td>0.85</td>
+<td>0.60</td>
+<td>6.68</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0027</td>
+<td>190899</td>
+<td>92.38</td>
+<td>69.01</td>
+<td>8.77</td>
+<td>1.18</td>
+<td>0.96</td>
+<td>5.56</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0028</td>
+<td>189399</td>
+<td>89.20</td>
+<td>96.30</td>
+<td>8.04</td>
+<td>1.05</td>
+<td>0.73</td>
+<td>6.62</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0029</td>
+<td>184662</td>
+<td>78.98</td>
+<td>108.67</td>
+<td>8.07</td>
+<td>1.08</td>
+<td>0.82</td>
+<td>5.86</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0030</td>
+<td>190688</td>
+<td>92.43</td>
+<td>66.08</td>
+<td>7.91</td>
+<td>0.95</td>
+<td>0.71</td>
+<td>6.76</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0031</td>
+<td>196290</td>
+<td>97.80</td>
+<td>110.61</td>
+<td>9.39</td>
+<td>1.04</td>
+<td>0.82</td>
+<td>5.42</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0032</td>
+<td>196748</td>
+<td>103.58</td>
+<td>108.28</td>
+<td>9.20</td>
+<td>0.99</td>
+<td>0.79</td>
+<td>5.86</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0033</td>
+<td>201464</td>
+<td>133.70</td>
+<td>54.15</td>
+<td>10.83</td>
+<td>1.09</td>
+<td>0.76</td>
+<td>7.01</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0034</td>
+<td>201778</td>
+<td>98.12</td>
+<td>115.41</td>
+<td>12.76</td>
+<td>1.09</td>
+<td>0.81</td>
+<td>6.42</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0035</td>
+<td>195338</td>
+<td>101.52</td>
+<td>65.77</td>
+<td>11.60</td>
+<td>1.16</td>
+<td>0.85</td>
+<td>6.61</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0036</td>
+<td>205839</td>
+<td>137.80</td>
+<td>74.55</td>
+<td>11.89</td>
+<td>1.14</td>
+<td>0.82</td>
+<td>6.16</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0037</td>
+<td>195386</td>
+<td>94.28</td>
+<td>112.54</td>
+<td>11.24</td>
+<td>1.03</td>
+<td>0.80</td>
+<td>6.15</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0038</td>
+<td>192726</td>
+<td>89.83</td>
+<td>67.16</td>
+<td>11.22</td>
+<td>1.02</td>
+<td>0.75</td>
+<td>6.73</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0039</td>
+<td>192642</td>
+<td>88.30</td>
+<td>126.64</td>
+<td>11.39</td>
+<td>1.13</td>
+<td>0.90</td>
+<td>6.10</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0040</td>
+<td>203095</td>
+<td>123.53</td>
+<td>85.10</td>
+<td>13.23</td>
+<td>1.00</td>
+<td>0.78</td>
+<td>6.57</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0041</td>
+<td>201324</td>
+<td>126.23</td>
+<td>77.20</td>
+<td>13.61</td>
+<td>1.23</td>
+<td>0.96</td>
+<td>6.23</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0042</td>
+<td>194426</td>
+<td>102.30</td>
+<td>75.07</td>
+<td>11.98</td>
+<td>0.91</td>
+<td>0.67</td>
+<td>6.30</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0043</td>
+<td>197512</td>
+<td>104.62</td>
+<td>72.88</td>
+<td>12.63</td>
+<td>1.15</td>
+<td>0.82</td>
+<td>6.74</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0044</td>
+<td>196006</td>
+<td>126.12</td>
+<td>61.63</td>
+<td>12.48</td>
+<td>1.15</td>
+<td>0.89</td>
+<td>6.33</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0045</td>
+<td>197592</td>
+<td>129.05</td>
+<td>71.48</td>
+<td>14.25</td>
+<td>1.30</td>
+<td>1.00</td>
+<td>6.77</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0046</td>
+<td>195797</td>
+<td>118.45</td>
+<td>88.64</td>
+<td>14.49</td>
+<td>1.14</td>
+<td>0.84</td>
+<td>6.48</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0047</td>
+<td>194741</td>
+<td>128.00</td>
+<td>78.38</td>
+<td>12.62</td>
+<td>1.19</td>
+<td>0.87</td>
+<td>6.64</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0048</td>
+<td>197436</td>
+<td>124.15</td>
+<td>79.68</td>
+<td>14.71</td>
+<td>1.20</td>
+<td>0.84</td>
+<td>6.80</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0049</td>
+<td>196566</td>
+<td>120.55</td>
+<td>84.55</td>
+<td>15.84</td>
+<td>1.24</td>
+<td>0.91</td>
+<td>6.51</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0050</td>
+<td>196255</td>
+<td>107.57</td>
+<td>89.36</td>
+<td>15.93</td>
+<td>1.15</td>
+<td>0.93</td>
+<td>5.66</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0051</td>
+<td>198773</td>
+<td>129.72</td>
+<td>95.86</td>
+<td>15.54</td>
+<td>1.19</td>
+<td>0.86</td>
+<td>6.44</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0052</td>
+<td>197968</td>
+<td>134.00</td>
+<td>79.46</td>
+<td>13.94</td>
+<td>1.07</td>
+<td>0.78</td>
+<td>6.47</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0053</td>
+<td>198600</td>
+<td>134.55</td>
+<td>57.32</td>
+<td>13.10</td>
+<td>1.16</td>
+<td>0.89</td>
+<td>6.35</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0054</td>
+<td>197173</td>
+<td>113.25</td>
+<td>83.20</td>
+<td>14.05</td>
+<td>1.14</td>
+<td>0.82</td>
+<td>6.53</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0055</td>
+<td>195506</td>
+<td>104.75</td>
+<td>79.33</td>
+<td>11.14</td>
+<td>1.06</td>
+<td>0.76</td>
+<td>6.73</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0056</td>
+<td>193200</td>
+<td>105.30</td>
+<td>88.82</td>
+<td>11.17</td>
+<td>1.08</td>
+<td>0.78</td>
+<td>6.80</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0057</td>
+<td>194216</td>
+<td>121.02</td>
+<td>73.06</td>
+<td>10.95</td>
+<td>1.09</td>
+<td>0.77</td>
+<td>6.45</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0058</td>
+<td>199530</td>
+<td>139.80</td>
+<td>67.01</td>
+<td>12.72</td>
+<td>1.19</td>
+<td>0.90</td>
+<td>6.09</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0059</td>
+<td>202163</td>
+<td>127.15</td>
+<td>68.93</td>
+<td>12.50</td>
+<td>1.08</td>
+<td>0.83</td>
+<td>5.87</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0060</td>
+<td>200721</td>
+<td>130.50</td>
+<td>53.98</td>
+<td>11.62</td>
+<td>1.11</td>
+<td>0.88</td>
+<td>6.27</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0061</td>
+<td>198041</td>
+<td>106.85</td>
+<td>65.79</td>
+<td>12.35</td>
+<td>1.07</td>
+<td>0.79</td>
+<td>6.75</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0062</td>
+<td>197475</td>
+<td>105.67</td>
+<td>74.92</td>
+<td>10.84</td>
+<td>1.13</td>
+<td>0.80</td>
+<td>7.21</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0063</td>
+<td>196647</td>
+<td>102.38</td>
+<td>88.62</td>
+<td>11.75</td>
+<td>1.03</td>
+<td>0.72</td>
+<td>6.89</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0064</td>
+<td>198472</td>
+<td>128.30</td>
+<td>83.94</td>
+<td>13.02</td>
+<td>1.13</td>
+<td>0.80</td>
+<td>7.11</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0065</td>
+<td>207784</td>
+<td>112.43</td>
+<td>91.97</td>
+<td>11.58</td>
+<td>0.96</td>
+<td>0.74</td>
+<td>6.31</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0066</td>
+<td>192575</td>
+<td>92.03</td>
+<td>105.92</td>
+<td>9.69</td>
+<td>1.13</td>
+<td>0.86</td>
+<td>6.44</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0067</td>
+<td>195245</td>
+<td>103.50</td>
+<td>91.38</td>
+<td>10.02</td>
+<td>1.08</td>
+<td>0.86</td>
+<td>6.17</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0068</td>
+<td>198723</td>
+<td>106.42</td>
+<td>81.54</td>
+<td>8.83</td>
+<td>1.25</td>
+<td>0.90</td>
+<td>7.09</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0069</td>
+<td>198214</td>
+<td>118.15</td>
+<td>58.99</td>
+<td>8.65</td>
+<td>0.97</td>
+<td>0.74</td>
+<td>5.99</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0070</td>
+<td>190770</td>
+<td>86.25</td>
+<td>73.45</td>
+<td>7.75</td>
+<td>0.91</td>
+<td>0.75</td>
+<td>5.41</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0071</td>
+<td>196479</td>
+<td>105.03</td>
+<td>52.37</td>
+<td>8.18</td>
+<td>1.13</td>
+<td>0.86</td>
+<td>5.94</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0072</td>
+<td>200551</td>
+<td>113.72</td>
+<td>54.80</td>
+<td>8.51</td>
+<td>1.01</td>
+<td>0.73</td>
+<td>6.82</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0073</td>
+<td>211144</td>
+<td>140.78</td>
+<td>46.47</td>
+<td>10.17</td>
+<td>0.98</td>
+<td>0.75</td>
+<td>6.30</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0074</td>
+<td>213445</td>
+<td>141.62</td>
+<td>47.27</td>
+<td>9.63</td>
+<td>1.10</td>
+<td>0.85</td>
+<td>6.29</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0075</td>
+<td>212336</td>
+<td>138.68</td>
+<td>43.63</td>
+<td>8.82</td>
+<td>0.98</td>
+<td>0.77</td>
+<td>6.20</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0076</td>
+<td>201544</td>
+<td>114.78</td>
+<td>55.11</td>
+<td>8.91</td>
+<td>0.92</td>
+<td>0.70</td>
+<td>5.75</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0077</td>
+<td>193099</td>
+<td>93.45</td>
+<td>130.82</td>
+<td>9.94</td>
+<td>1.03</td>
+<td>0.82</td>
+<td>5.86</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0078</td>
+<td>192730</td>
+<td>113.38</td>
+<td>57.53</td>
+<td>7.04</td>
+<td>0.83</td>
+<td>0.72</td>
+<td>5.07</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0079</td>
+<td>208296</td>
+<td>146.50</td>
+<td>83.21</td>
+<td>9.01</td>
+<td>1.06</td>
+<td>0.75</td>
+<td>7.18</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0080</td>
+<td>198441</td>
+<td>126.60</td>
+<td>46.54</td>
+<td>7.66</td>
+<td>1.07</td>
+<td>0.78</td>
+<td>6.69</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0081</td>
+<td>198612</td>
+<td>125.23</td>
+<td>101.20</td>
+<td>8.25</td>
+<td>1.07</td>
+<td>0.73</td>
+<td>6.21</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0082</td>
+<td>211383</td>
+<td>135.93</td>
+<td>69.38</td>
+<td>9.92</td>
+<td>1.09</td>
+<td>0.82</td>
+<td>5.97</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0083</td>
+<td>205392</td>
+<td>120.15</td>
+<td>49.52</td>
+<td>8.89</td>
+<td>1.06</td>
+<td>0.78</td>
+<td>7.11</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0084</td>
+<td>209166</td>
+<td>132.12</td>
+<td>48.00</td>
+<td>7.41</td>
+<td>1.02</td>
+<td>0.79</td>
+<td>5.75</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0085</td>
+<td>199610</td>
+<td>106.67</td>
+<td>107.97</td>
+<td>7.22</td>
+<td>1.03</td>
+<td>0.84</td>
+<td>5.81</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0086</td>
+<td>200884</td>
+<td>88.35</td>
+<td>63.95</td>
+<td>6.51</td>
+<td>1.22</td>
+<td>1.00</td>
+<td>5.74</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0087</td>
+<td>202820</td>
+<td>122.62</td>
+<td>88.34</td>
+<td>7.90</td>
+<td>1.07</td>
+<td>0.84</td>
+<td>6.27</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0088</td>
+<td>212070</td>
+<td>106.88</td>
+<td>72.70</td>
+<td>7.90</td>
+<td>1.09</td>
+<td>0.81</td>
+<td>5.97</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0089</td>
+<td>198992</td>
+<td>113.72</td>
+<td>78.19</td>
+<td>7.01</td>
+<td>1.03</td>
+<td>0.78</td>
+<td>5.93</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0090</td>
+<td>210462</td>
+<td>100.10</td>
+<td>123.98</td>
+<td>9.42</td>
+<td>1.05</td>
+<td>0.80</td>
+<td>5.99</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0091</td>
+<td>198131</td>
+<td>85.18</td>
+<td>78.02</td>
+<td>7.49</td>
+<td>1.02</td>
+<td>0.75</td>
+<td>6.21</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0092</td>
+<td>195888</td>
+<td>99.88</td>
+<td>76.57</td>
+<td>7.10</td>
+<td>1.08</td>
+<td>0.82</td>
+<td>6.54</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0093</td>
+<td>204882</td>
+<td>113.30</td>
+<td>58.27</td>
+<td>8.77</td>
+<td>1.13</td>
+<td>0.83</td>
+<td>6.31</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0094</td>
+<td>194269</td>
+<td>103.75</td>
+<td>66.31</td>
+<td>7.25</td>
+<td>1.00</td>
+<td>0.79</td>
+<td>6.23</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0095</td>
+<td>210191</td>
+<td>120.05</td>
+<td>55.10</td>
+<td>8.58</td>
+<td>1.10</td>
+<td>0.83</td>
+<td>6.06</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0096</td>
+<td>199862</td>
+<td>111.05</td>
+<td>59.70</td>
+<td>5.99</td>
+<td>1.08</td>
+<td>0.76</td>
+<td>6.31</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0097</td>
+<td>190392</td>
+<td>95.53</td>
+<td>69.33</td>
+<td>8.08</td>
+<td>1.37</td>
+<td>1.04</td>
+<td>6.40</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0098</td>
+<td>201697</td>
+<td>138.38</td>
+<td>74.92</td>
+<td>6.12</td>
+<td>1.02</td>
+<td>0.78</td>
+<td>6.28</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0099</td>
+<td>190314</td>
+<td>90.58</td>
+<td>92.58</td>
+<td>7.91</td>
+<td>1.21</td>
+<td>0.84</td>
+<td>6.13</td>
+</tr>
+<tr>
+<td>3</td>
+<td>0100</td>
+<td>211178</td>
+<td>123.43</td>
+<td>69.78</td>
+<td>8.24</td>
+<td>0.98</td>
+<td>0.78</td>
+<td>5.90</td>
+</tr>
+</table>
+<a name="Lane"><h2>Lane 4</h2></a><table border="1" cellpadding="5">
+<tr>
+<td colspan="1">Lane </td>
+<td colspan="1">Tile</td>
+<td colspan="1">Clusters (raw)</td>
+<td colspan="1">Av 1st Cycle Int (PF)</td>
+<td colspan="1">Av % intensity after 20 cycles (PF)</td>
+<td colspan="1">% PF Clusters </td>
+<td colspan="1">% Align (PF) </td>
+<td colspan="1">Av Alignment Score (PF)</td>
+<td colspan="1">% Error Rate (PF) </td>
+</tr>
+<tr>
+<td>4</td>
+<td>0001</td>
+<td>159972</td>
+<td>82.67</td>
+<td>89.45</td>
+<td>18.60</td>
+<td>4.24</td>
+<td>4.08</td>
+<td>3.55</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0002</td>
+<td>154345</td>
+<td>74.00</td>
+<td>102.60</td>
+<td>17.12</td>
+<td>4.43</td>
+<td>4.40</td>
+<td>3.29</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0003</td>
+<td>149038</td>
+<td>68.75</td>
+<td>107.49</td>
+<td>16.52</td>
+<td>4.70</td>
+<td>4.77</td>
+<td>3.29</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0004</td>
+<td>154208</td>
+<td>67.67</td>
+<td>96.27</td>
+<td>17.36</td>
+<td>4.16</td>
+<td>4.05</td>
+<td>3.58</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0005</td>
+<td>159793</td>
+<td>61.13</td>
+<td>119.88</td>
+<td>14.93</td>
+<td>4.15</td>
+<td>4.16</td>
+<td>3.49</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0006</td>
+<td>160185</td>
+<td>51.15</td>
+<td>122.09</td>
+<td>13.63</td>
+<td>4.29</td>
+<td>4.37</td>
+<td>3.45</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0007</td>
+<td>149174</td>
+<td>56.97</td>
+<td>168.06</td>
+<td>15.41</td>
+<td>4.60</td>
+<td>4.77</td>
+<td>3.43</td>
+</tr>
+<tr>
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+<td>4.03</td>
+<td>4.05</td>
+<td>3.61</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0079</td>
+<td>160311</td>
+<td>100.62</td>
+<td>76.52</td>
+<td>24.02</td>
+<td>4.63</td>
+<td>4.45</td>
+<td>3.70</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0080</td>
+<td>147552</td>
+<td>99.77</td>
+<td>122.15</td>
+<td>26.94</td>
+<td>4.32</td>
+<td>4.40</td>
+<td>3.18</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0081</td>
+<td>175728</td>
+<td>67.38</td>
+<td>147.01</td>
+<td>20.25</td>
+<td>3.89</td>
+<td>4.07</td>
+<td>3.24</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0082</td>
+<td>159621</td>
+<td>100.60</td>
+<td>73.86</td>
+<td>24.94</td>
+<td>4.54</td>
+<td>4.54</td>
+<td>3.50</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0083</td>
+<td>151723</td>
+<td>83.72</td>
+<td>97.13</td>
+<td>20.83</td>
+<td>4.60</td>
+<td>4.67</td>
+<td>3.38</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0084</td>
+<td>149291</td>
+<td>75.22</td>
+<td>114.46</td>
+<td>22.02</td>
+<td>4.83</td>
+<td>4.87</td>
+<td>3.48</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0085</td>
+<td>158640</td>
+<td>86.82</td>
+<td>86.55</td>
+<td>19.36</td>
+<td>4.26</td>
+<td>4.10</td>
+<td>3.60</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0086</td>
+<td>149953</td>
+<td>73.95</td>
+<td>111.43</td>
+<td>19.77</td>
+<td>5.06</td>
+<td>5.07</td>
+<td>3.66</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0087</td>
+<td>159982</td>
+<td>99.40</td>
+<td>103.14</td>
+<td>16.81</td>
+<td>4.60</td>
+<td>4.52</td>
+<td>3.48</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0088</td>
+<td>184055</td>
+<td>58.55</td>
+<td>114.90</td>
+<td>24.89</td>
+<td>3.46</td>
+<td>3.52</td>
+<td>3.55</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0089</td>
+<td>159972</td>
+<td>73.67</td>
+<td>104.24</td>
+<td>17.24</td>
+<td>4.25</td>
+<td>4.08</td>
+<td>3.85</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0090</td>
+<td>164917</td>
+<td>101.95</td>
+<td>109.44</td>
+<td>18.70</td>
+<td>4.84</td>
+<td>4.69</td>
+<td>3.52</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0091</td>
+<td>152453</td>
+<td>71.45</td>
+<td>112.07</td>
+<td>18.89</td>
+<td>4.41</td>
+<td>4.37</td>
+<td>3.50</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0092</td>
+<td>155219</td>
+<td>80.20</td>
+<td>93.52</td>
+<td>17.64</td>
+<td>4.49</td>
+<td>4.48</td>
+<td>3.60</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0093</td>
+<td>163900</td>
+<td>87.65</td>
+<td>70.68</td>
+<td>16.48</td>
+<td>4.30</td>
+<td>4.13</td>
+<td>3.60</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0094</td>
+<td>145355</td>
+<td>82.10</td>
+<td>82.86</td>
+<td>15.25</td>
+<td>4.45</td>
+<td>4.34</td>
+<td>3.59</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0095</td>
+<td>138112</td>
+<td>83.02</td>
+<td>123.49</td>
+<td>17.94</td>
+<td>4.70</td>
+<td>4.65</td>
+<td>3.55</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0096</td>
+<td>154654</td>
+<td>57.23</td>
+<td>169.77</td>
+<td>13.99</td>
+<td>4.48</td>
+<td>4.59</td>
+<td>3.47</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0097</td>
+<td>167709</td>
+<td>80.40</td>
+<td>87.13</td>
+<td>18.50</td>
+<td>4.09</td>
+<td>3.98</td>
+<td>3.77</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0098</td>
+<td>160175</td>
+<td>69.58</td>
+<td>131.30</td>
+<td>20.00</td>
+<td>3.95</td>
+<td>3.89</td>
+<td>3.57</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0099</td>
+<td>165398</td>
+<td>73.87</td>
+<td>121.93</td>
+<td>22.00</td>
+<td>3.91</td>
+<td>3.97</td>
+<td>3.17</td>
+</tr>
+<tr>
+<td>4</td>
+<td>0100</td>
+<td>156998</td>
+<td>78.80</td>
+<td>133.66</td>
+<td>23.09</td>
+<td>4.11</td>
+<td>4.14</td>
+<td>3.33</td>
+</tr>
+</table>
+<a name="Lane"><h2>Lane 5</h2></a><table border="1" cellpadding="5">
+<tr>
+<td colspan="1">Lane </td>
+<td colspan="1">Tile</td>
+<td colspan="1">Clusters (raw)</td>
+<td colspan="1">Av 1st Cycle Int (PF)</td>
+<td colspan="1">Av % intensity after 20 cycles (PF)</td>
+<td colspan="1">% PF Clusters </td>
+<td colspan="1">% Align (PF) </td>
+<td colspan="1">Av Alignment Score (PF)</td>
+<td colspan="1">% Error Rate (PF) </td>
+</tr>
+<tr>
+<td>5</td>
+<td>0001</td>
+<td>139658</td>
+<td>72.90</td>
+<td>128.74</td>
+<td>21.85</td>
+<td>3.75</td>
+<td>3.67</td>
+<td>3.28</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0002</td>
+<td>140964</td>
+<td>66.42</td>
+<td>101.92</td>
+<td>21.29</td>
+<td>3.73</td>
+<td>3.58</td>
+<td>3.77</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0003</td>
+<td>139085</td>
+<td>68.25</td>
+<td>102.42</td>
+<td>17.78</td>
+<td>4.29</td>
+<td>4.22</td>
+<td>3.33</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0004</td>
+<td>138352</td>
+<td>69.20</td>
+<td>110.30</td>
+<td>21.41</td>
+<td>3.83</td>
+<td>3.71</td>
+<td>3.75</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0005</td>
+<td>139444</td>
+<td>74.35</td>
+<td>106.86</td>
+<td>19.88</td>
+<td>4.11</td>
+<td>3.98</td>
+<td>3.56</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0006</td>
+<td>139621</td>
+<td>59.90</td>
+<td>104.13</td>
+<td>18.86</td>
+<td>3.67</td>
+<td>3.51</td>
+<td>3.93</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0007</td>
+<td>139048</td>
+<td>64.80</td>
+<td>103.74</td>
+<td>19.50</td>
+<td>3.72</td>
+<td>3.48</td>
+<td>3.69</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0008</td>
+<td>141160</td>
+<td>64.67</td>
+<td>98.76</td>
+<td>18.08</td>
+<td>3.59</td>
+<td>3.41</td>
+<td>3.78</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0009</td>
+<td>139245</td>
+<td>62.23</td>
+<td>94.09</td>
+<td>18.47</td>
+<td>3.89</td>
+<td>3.54</td>
+<td>3.97</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0010</td>
+<td>138211</td>
+<td>57.35</td>
+<td>94.42</td>
+<td>18.35</td>
+<td>3.83</td>
+<td>3.71</td>
+<td>3.57</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0011</td>
+<td>136975</td>
+<td>58.27</td>
+<td>93.22</td>
+<td>18.29</td>
+<td>4.04</td>
+<td>4.04</td>
+<td>3.34</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0012</td>
+<td>135992</td>
+<td>57.35</td>
+<td>86.09</td>
+<td>17.51</td>
+<td>4.11</td>
+<td>4.00</td>
+<td>3.85</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0013</td>
+<td>140930</td>
+<td>53.18</td>
+<td>140.48</td>
+<td>18.62</td>
+<td>3.97</td>
+<td>3.85</td>
+<td>3.73</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0014</td>
+<td>140313</td>
+<td>63.65</td>
+<td>93.05</td>
+<td>17.48</td>
+<td>3.75</td>
+<td>3.45</td>
+<td>3.85</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0015</td>
+<td>141815</td>
+<td>74.38</td>
+<td>81.45</td>
+<td>17.77</td>
+<td>3.92</td>
+<td>3.73</td>
+<td>3.70</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0016</td>
+<td>138206</td>
+<td>77.85</td>
+<td>109.34</td>
+<td>19.30</td>
+<td>3.82</td>
+<td>3.59</td>
+<td>3.65</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0017</td>
+<td>140661</td>
+<td>87.75</td>
+<td>105.41</td>
+<td>18.56</td>
+<td>4.57</td>
+<td>4.38</td>
+<td>3.42</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0018</td>
+<td>139133</td>
+<td>64.83</td>
+<td>77.82</td>
+<td>21.37</td>
+<td>4.05</td>
+<td>4.04</td>
+<td>3.54</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0019</td>
+<td>138919</td>
+<td>77.03</td>
+<td>122.36</td>
+<td>22.40</td>
+<td>4.04</td>
+<td>3.92</td>
+<td>3.62</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0020</td>
+<td>136707</td>
+<td>67.12</td>
+<td>98.32</td>
+<td>19.01</td>
+<td>4.60</td>
+<td>4.51</td>
+<td>3.41</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0021</td>
+<td>140284</td>
+<td>59.67</td>
+<td>110.98</td>
+<td>18.31</td>
+<td>4.13</td>
+<td>4.04</td>
+<td>3.82</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0022</td>
+<td>135887</td>
+<td>54.60</td>
+<td>97.71</td>
+<td>16.94</td>
+<td>3.72</td>
+<td>3.65</td>
+<td>3.73</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0023</td>
+<td>136065</td>
+<td>56.40</td>
+<td>125.98</td>
+<td>19.93</td>
+<td>4.12</td>
+<td>4.22</td>
+<td>3.49</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0024</td>
+<td>137793</td>
+<td>62.90</td>
+<td>131.28</td>
+<td>20.66</td>
+<td>4.12</td>
+<td>4.02</td>
+<td>3.70</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0025</td>
+<td>134349</td>
+<td>62.10</td>
+<td>115.34</td>
+<td>23.38</td>
+<td>4.07</td>
+<td>3.96</td>
+<td>3.62</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0026</td>
+<td>138802</td>
+<td>54.20</td>
+<td>131.55</td>
+<td>20.52</td>
+<td>4.05</td>
+<td>4.11</td>
+<td>3.44</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0027</td>
+<td>134191</td>
+<td>56.70</td>
+<td>167.20</td>
+<td>21.61</td>
+<td>4.64</td>
+<td>4.70</td>
+<td>3.55</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0028</td>
+<td>135596</td>
+<td>57.73</td>
+<td>151.93</td>
+<td>25.57</td>
+<td>4.54</td>
+<td>4.54</td>
+<td>3.61</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0029</td>
+<td>132493</td>
+<td>78.58</td>
+<td>126.44</td>
+<td>22.19</td>
+<td>5.03</td>
+<td>4.83</td>
+<td>3.77</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0030</td>
+<td>132570</td>
+<td>64.17</td>
+<td>157.65</td>
+<td>24.17</td>
+<td>4.67</td>
+<td>4.52</td>
+<td>4.17</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0031</td>
+<td>137864</td>
+<td>84.25</td>
+<td>111.90</td>
+<td>31.20</td>
+<td>4.31</td>
+<td>4.15</td>
+<td>3.87</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0032</td>
+<td>136471</td>
+<td>54.62</td>
+<td>150.30</td>
+<td>28.33</td>
+<td>4.67</td>
+<td>4.67</td>
+<td>3.68</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0033</td>
+<td>133065</td>
+<td>82.12</td>
+<td>107.40</td>
+<td>22.89</td>
+<td>4.72</td>
+<td>4.36</td>
+<td>4.29</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0034</td>
+<td>137713</td>
+<td>66.05</td>
+<td>126.57</td>
+<td>26.09</td>
+<td>4.33</td>
+<td>4.21</td>
+<td>3.91</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0035</td>
+<td>130155</td>
+<td>59.40</td>
+<td>83.16</td>
+<td>21.50</td>
+<td>4.69</td>
+<td>4.87</td>
+<td>3.50</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0036</td>
+<td>135783</td>
+<td>86.45</td>
+<td>90.66</td>
+<td>21.28</td>
+<td>4.70</td>
+<td>4.50</td>
+<td>3.77</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0037</td>
+<td>131641</td>
+<td>67.30</td>
+<td>80.79</td>
+<td>22.36</td>
+<td>4.29</td>
+<td>4.22</td>
+<td>3.68</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0038</td>
+<td>130728</td>
+<td>64.15</td>
+<td>98.99</td>
+<td>20.05</td>
+<td>5.02</td>
+<td>4.93</td>
+<td>3.91</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0039</td>
+<td>132090</td>
+<td>60.83</td>
+<td>99.75</td>
+<td>19.36</td>
+<td>4.95</td>
+<td>4.81</td>
+<td>4.01</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0040</td>
+<td>134211</td>
+<td>51.10</td>
+<td>136.94</td>
+<td>14.11</td>
+<td>4.84</td>
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+<td>4.03</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0041</td>
+<td>133102</td>
+<td>93.62</td>
+<td>66.57</td>
+<td>21.27</td>
+<td>4.60</td>
+<td>4.43</td>
+<td>3.70</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0042</td>
+<td>132925</td>
+<td>72.62</td>
+<td>104.23</td>
+<td>24.15</td>
+<td>4.12</td>
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+<td>3.89</td>
+</tr>
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+<td>5</td>
+<td>0043</td>
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+<td>76.58</td>
+<td>129.02</td>
+<td>22.13</td>
+<td>4.71</td>
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+</tr>
+<tr>
+<td>5</td>
+<td>0044</td>
+<td>131413</td>
+<td>66.65</td>
+<td>108.93</td>
+<td>25.85</td>
+<td>4.47</td>
+<td>4.34</td>
+<td>3.85</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0045</td>
+<td>129925</td>
+<td>82.50</td>
+<td>115.24</td>
+<td>21.63</td>
+<td>4.65</td>
+<td>4.38</td>
+<td>4.19</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0046</td>
+<td>131687</td>
+<td>61.55</td>
+<td>108.57</td>
+<td>23.72</td>
+<td>4.24</td>
+<td>4.35</td>
+<td>3.43</td>
+</tr>
+<tr>
+<td>5</td>
+<td>0047</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>4.10</td>
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+<tr>
+<td>5</td>
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+<td>5</td>
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+<td>102.68</td>
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+<td>3.83</td>
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+<td>5</td>
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+<td>3.53</td>
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+<td>5</td>
+<td>0066</td>
+<td>134827</td>
+<td>64.57</td>
+<td>103.60</td>
+<td>19.55</td>
+<td>5.25</td>
+<td>5.16</td>
+<td>3.94</td>
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+<td>5</td>
+<td>0067</td>
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+<td>105.92</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
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+<td>5</td>
+<td>0078</td>
+<td>140665</td>
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+<td>3.52</td>
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+<td>5</td>
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+<td>134728</td>
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+<td>145.93</td>
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+<td>5</td>
+<td>0083</td>
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+<td>5</td>
+<td>0084</td>
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+<td>5</td>
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+<td>5</td>
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+<td>99.58</td>
+<td>22.23</td>
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+<td>5</td>
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+<td>25.59</td>
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+<td>5</td>
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+<td>3.15</td>
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+</tr>
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+<td>5</td>
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+</tr>
+<tr>
+<td>5</td>
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+</tr>
+<tr>
+<td>5</td>
+<td>0099</td>
+<td>145817</td>
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+<td>110.93</td>
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+<td>3.16</td>
+</tr>
+<tr>
+<td>5</td>
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+<td>3.72</td>
+<td>3.21</td>
+</tr>
+</table>
+<a name="Lane"><h2>Lane 6</h2></a><table border="1" cellpadding="5">
+<tr>
+<td colspan="1">Lane </td>
+<td colspan="1">Tile</td>
+<td colspan="1">Clusters (raw)</td>
+<td colspan="1">Av 1st Cycle Int (PF)</td>
+<td colspan="1">Av % intensity after 20 cycles (PF)</td>
+<td colspan="1">% PF Clusters </td>
+<td colspan="1">% Align (PF) </td>
+<td colspan="1">Av Alignment Score (PF)</td>
+<td colspan="1">% Error Rate (PF) </td>
+</tr>
+<tr>
+<td>6</td>
+<td>0001</td>
+<td>153653</td>
+<td>79.58</td>
+<td>136.00</td>
+<td>23.86</td>
+<td>3.57</td>
+<td>3.46</td>
+<td>3.57</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0002</td>
+<td>160739</td>
+<td>56.15</td>
+<td>162.78</td>
+<td>17.73</td>
+<td>3.21</td>
+<td>3.13</td>
+<td>3.67</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0003</td>
+<td>147132</td>
+<td>63.32</td>
+<td>127.79</td>
+<td>17.49</td>
+<td>3.75</td>
+<td>3.65</td>
+<td>3.56</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0004</td>
+<td>151602</td>
+<td>65.30</td>
+<td>109.95</td>
+<td>18.04</td>
+<td>3.53</td>
+<td>3.42</td>
+<td>3.54</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0005</td>
+<td>156959</td>
+<td>61.57</td>
+<td>109.87</td>
+<td>20.25</td>
+<td>3.16</td>
+<td>3.08</td>
+<td>3.64</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0006</td>
+<td>146995</td>
+<td>75.43</td>
+<td>108.25</td>
+<td>19.92</td>
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+<td>3.55</td>
+</tr>
+<tr>
+<td>6</td>
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+<td>147619</td>
+<td>84.72</td>
+<td>103.16</td>
+<td>20.12</td>
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+<td>3.62</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0008</td>
+<td>157868</td>
+<td>73.75</td>
+<td>139.22</td>
+<td>26.32</td>
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+<td>3.66</td>
+</tr>
+<tr>
+<td>6</td>
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+<td>126.27</td>
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+</tr>
+<tr>
+<td>6</td>
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+<td>3.62</td>
+</tr>
+<tr>
+<td>6</td>
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+<td>153943</td>
+<td>71.40</td>
+<td>106.06</td>
+<td>19.50</td>
+<td>3.47</td>
+<td>3.38</td>
+<td>3.37</td>
+</tr>
+<tr>
+<td>6</td>
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+<td>148858</td>
+<td>81.22</td>
+<td>86.43</td>
+<td>19.51</td>
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+<td>3.65</td>
+</tr>
+<tr>
+<td>6</td>
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+<td>144234</td>
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+</tr>
+<tr>
+<td>6</td>
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+<td>143006</td>
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+<td>17.26</td>
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+</tr>
+<tr>
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+<td>154.47</td>
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+<td>6</td>
+<td>0086</td>
+<td>138270</td>
+<td>68.20</td>
+<td>127.97</td>
+<td>27.14</td>
+<td>3.83</td>
+<td>3.85</td>
+<td>3.13</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0087</td>
+<td>164771</td>
+<td>71.25</td>
+<td>147.44</td>
+<td>29.57</td>
+<td>3.16</td>
+<td>3.19</td>
+<td>2.98</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0088</td>
+<td>131121</td>
+<td>77.50</td>
+<td>134.52</td>
+<td>27.09</td>
+<td>3.87</td>
+<td>3.91</td>
+<td>3.14</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0089</td>
+<td>150558</td>
+<td>89.00</td>
+<td>81.35</td>
+<td>25.53</td>
+<td>3.63</td>
+<td>3.57</td>
+<td>3.25</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0090</td>
+<td>162180</td>
+<td>89.12</td>
+<td>102.92</td>
+<td>25.09</td>
+<td>4.06</td>
+<td>3.88</td>
+<td>3.31</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0091</td>
+<td>161673</td>
+<td>101.30</td>
+<td>108.96</td>
+<td>30.88</td>
+<td>3.20</td>
+<td>3.03</td>
+<td>3.33</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0092</td>
+<td>158822</td>
+<td>93.17</td>
+<td>117.12</td>
+<td>32.93</td>
+<td>3.69</td>
+<td>3.65</td>
+<td>2.97</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0093</td>
+<td>167698</td>
+<td>100.77</td>
+<td>111.51</td>
+<td>35.19</td>
+<td>3.48</td>
+<td>3.40</td>
+<td>2.87</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0094</td>
+<td>169111</td>
+<td>88.40</td>
+<td>117.02</td>
+<td>30.23</td>
+<td>3.32</td>
+<td>3.24</td>
+<td>3.01</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0095</td>
+<td>177848</td>
+<td>103.12</td>
+<td>117.89</td>
+<td>36.78</td>
+<td>3.33</td>
+<td>3.26</td>
+<td>3.07</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0096</td>
+<td>166133</td>
+<td>95.85</td>
+<td>110.67</td>
+<td>32.00</td>
+<td>3.53</td>
+<td>3.46</td>
+<td>3.08</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0097</td>
+<td>178023</td>
+<td>83.72</td>
+<td>145.63</td>
+<td>31.05</td>
+<td>3.19</td>
+<td>3.15</td>
+<td>2.88</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0098</td>
+<td>180358</td>
+<td>84.95</td>
+<td>151.68</td>
+<td>29.70</td>
+<td>3.08</td>
+<td>3.07</td>
+<td>2.89</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0099</td>
+<td>170568</td>
+<td>93.78</td>
+<td>102.45</td>
+<td>34.36</td>
+<td>3.46</td>
+<td>3.41</td>
+<td>2.97</td>
+</tr>
+<tr>
+<td>6</td>
+<td>0100</td>
+<td>159704</td>
+<td>94.20</td>
+<td>119.96</td>
+<td>29.87</td>
+<td>3.44</td>
+<td>3.36</td>
+<td>3.07</td>
+</tr>
+</table>
+<a name="Lane"><h2>Lane 7</h2></a><table border="1" cellpadding="5">
+<tr>
+<td colspan="1">Lane </td>
+<td colspan="1">Tile</td>
+<td colspan="1">Clusters (raw)</td>
+<td colspan="1">Av 1st Cycle Int (PF)</td>
+<td colspan="1">Av % intensity after 20 cycles (PF)</td>
+<td colspan="1">% PF Clusters </td>
+<td colspan="1">% Align (PF) </td>
+<td colspan="1">Av Alignment Score (PF)</td>
+<td colspan="1">% Error Rate (PF) </td>
+</tr>
+<tr>
+<td>7</td>
+<td>0001</td>
+<td>167397</td>
+<td>69.27</td>
+<td>175.60</td>
+<td>33.31</td>
+<td>0.90</td>
+<td>0.61</td>
+<td>2.48</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0002</td>
+<td>160455</td>
+<td>74.47</td>
+<td>139.78</td>
+<td>29.55</td>
+<td>0.88</td>
+<td>0.59</td>
+<td>2.91</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0003</td>
+<td>161412</td>
+<td>82.17</td>
+<td>112.08</td>
+<td>28.64</td>
+<td>0.85</td>
+<td>0.55</td>
+<td>3.00</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0004</td>
+<td>163624</td>
+<td>101.35</td>
+<td>84.07</td>
+<td>26.10</td>
+<td>0.87</td>
+<td>0.53</td>
+<td>3.76</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0005</td>
+<td>164021</td>
+<td>92.78</td>
+<td>113.18</td>
+<td>26.89</td>
+<td>0.96</td>
+<td>0.65</td>
+<td>3.07</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0006</td>
+<td>156806</td>
+<td>75.62</td>
+<td>117.16</td>
+<td>27.59</td>
+<td>0.86</td>
+<td>0.60</td>
+<td>2.80</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0007</td>
+<td>162038</td>
+<td>73.40</td>
+<td>114.44</td>
+<td>27.61</td>
+<td>0.86</td>
+<td>0.60</td>
+<td>2.45</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0008</td>
+<td>165511</td>
+<td>107.40</td>
+<td>105.26</td>
+<td>35.62</td>
+<td>0.86</td>
+<td>0.56</td>
+<td>2.85</td>
+</tr>
+<tr>
+<td>7</td>
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+<td>163282</td>
+<td>87.62</td>
+<td>129.24</td>
+<td>30.11</td>
+<td>0.81</td>
+<td>0.57</td>
+<td>3.20</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0010</td>
+<td>156992</td>
+<td>68.35</td>
+<td>119.09</td>
+<td>22.63</td>
+<td>0.81</td>
+<td>0.53</td>
+<td>2.96</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0011</td>
+<td>150552</td>
+<td>72.62</td>
+<td>118.73</td>
+<td>25.24</td>
+<td>0.87</td>
+<td>0.60</td>
+<td>2.60</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0012</td>
+<td>160140</td>
+<td>68.35</td>
+<td>105.56</td>
+<td>22.16</td>
+<td>0.72</td>
+<td>0.47</td>
+<td>3.23</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0013</td>
+<td>149302</td>
+<td>85.85</td>
+<td>100.93</td>
+<td>21.86</td>
+<td>0.79</td>
+<td>0.50</td>
+<td>3.30</td>
+</tr>
+<tr>
+<td>7</td>
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+<td>143740</td>
+<td>84.30</td>
+<td>87.37</td>
+<td>23.58</td>
+<td>0.82</td>
+<td>0.57</td>
+<td>2.92</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0015</td>
+<td>139427</td>
+<td>58.95</td>
+<td>157.80</td>
+<td>24.12</td>
+<td>0.92</td>
+<td>0.63</td>
+<td>3.22</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0016</td>
+<td>136177</td>
+<td>72.90</td>
+<td>146.60</td>
+<td>26.28</td>
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+<td>2.91</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0017</td>
+<td>132237</td>
+<td>69.00</td>
+<td>151.45</td>
+<td>25.13</td>
+<td>1.10</td>
+<td>0.79</td>
+<td>2.68</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0018</td>
+<td>142869</td>
+<td>66.30</td>
+<td>171.00</td>
+<td>23.94</td>
+<td>0.99</td>
+<td>0.75</td>
+<td>2.93</td>
+</tr>
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+<td>7</td>
+<td>0019</td>
+<td>142456</td>
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+<td>165.86</td>
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+<td>2.91</td>
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+<td>7</td>
+<td>0020</td>
+<td>155029</td>
+<td>60.87</td>
+<td>100.82</td>
+<td>28.64</td>
+<td>0.75</td>
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+<td>3.65</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0021</td>
+<td>154783</td>
+<td>62.23</td>
+<td>154.28</td>
+<td>24.08</td>
+<td>0.73</td>
+<td>0.54</td>
+<td>3.34</td>
+</tr>
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+<td>7</td>
+<td>0022</td>
+<td>140060</td>
+<td>54.48</td>
+<td>172.10</td>
+<td>20.64</td>
+<td>1.05</td>
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+</tr>
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+<td>7</td>
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+<td>2.65</td>
+</tr>
+<tr>
+<td>7</td>
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+<td>148929</td>
+<td>60.38</td>
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+<td>26.97</td>
+<td>0.80</td>
+<td>0.59</td>
+<td>2.99</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0025</td>
+<td>150950</td>
+<td>67.45</td>
+<td>106.30</td>
+<td>25.61</td>
+<td>0.90</td>
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+<td>3.29</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0026</td>
+<td>150448</td>
+<td>67.12</td>
+<td>151.32</td>
+<td>26.18</td>
+<td>0.83</td>
+<td>0.60</td>
+<td>3.44</td>
+</tr>
+<tr>
+<td>7</td>
+<td>0027</td>
+<td>157343</td>
+<td>80.08</td>
+<td>155.89</td>
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+<td>7</td>
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+<td>154919</td>
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+<td>169.70</td>
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+<td>0.90</td>
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+<td>3.35</td>
+</tr>
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+<td>155916</td>
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+<td>7</td>
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+<td>153327</td>
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+<td>7</td>
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+<td>167377</td>
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+<td>156070</td>
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+<td>0.72</td>
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+</tr>
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+<td>7</td>
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+<td>145454</td>
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+</tr>
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+</tr>
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+<td>7</td>
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+<td>150892</td>
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+<td>7</td>
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+</tr>
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+<td>159579</td>
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+<td>7</td>
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+</tr>
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+</tr>
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+<td>7</td>
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+</tr>
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+<td>7</td>
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+<td>78.40</td>
+<td>109.38</td>
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+</tr>
+</table>
+<a name="Lane"><h2>Lane 8</h2></a><table border="1" cellpadding="5">
+<tr>
+<td colspan="1">Lane </td>
+<td colspan="1">Tile</td>
+<td colspan="1">Clusters (raw)</td>
+<td colspan="1">Av 1st Cycle Int (PF)</td>
+<td colspan="1">Av % intensity after 20 cycles (PF)</td>
+<td colspan="1">% PF Clusters </td>
+<td colspan="1">% Align (PF) </td>
+<td colspan="1">Av Alignment Score (PF)</td>
+<td colspan="1">% Error Rate (PF) </td>
+</tr>
+<tr>
+<td>8</td>
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+<td>181113</td>
+<td>113.72</td>
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+<td>37.02</td>
+<td>2.90</td>
+</tr>
+<tr>
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+</tr>
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+</tr>
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+<td>44.14</td>
+<td>39.45</td>
+<td>3.29</td>
+</tr>
+<tr>
+<td>8</td>
+<td>0094</td>
+<td>177924</td>
+<td>125.05</td>
+<td>71.75</td>
+<td>2.19</td>
+<td>37.05</td>
+<td>29.90</td>
+<td>3.82</td>
+</tr>
+<tr>
+<td>8</td>
+<td>0095</td>
+<td>175164</td>
+<td>103.00</td>
+<td>88.76</td>
+<td>2.79</td>
+<td>40.67</td>
+<td>29.89</td>
+<td>3.75</td>
+</tr>
+<tr>
+<td>8</td>
+<td>0096</td>
+<td>170691</td>
+<td>109.40</td>
+<td>74.77</td>
+<td>1.27</td>
+<td>37.54</td>
+<td>31.24</td>
+<td>4.00</td>
+</tr>
+<tr>
+<td>8</td>
+<td>0097</td>
+<td>177715</td>
+<td>0.00</td>
+<td>N/A</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+</tr>
+<tr>
+<td>8</td>
+<td>0098</td>
+<td>168260</td>
+<td>121.38</td>
+<td>64.30</td>
+<td>2.14</td>
+<td>36.20</td>
+<td>20.99</td>
+<td>3.23</td>
+</tr>
+<tr>
+<td>8</td>
+<td>0099</td>
+<td>202629</td>
+<td>149.72</td>
+<td>72.23</td>
+<td>2.51</td>
+<td>41.13</td>
+<td>34.74</td>
+<td>3.56</td>
+</tr>
+<tr>
+<td>8</td>
+<td>0100</td>
+<td>162617</td>
+<td>125.72</td>
+<td>66.75</td>
+<td>2.53</td>
+<td>41.07</td>
+<td>31.79</td>
+<td>3.48</td>
+</tr>
+</table>
+<li><h3>IVC Plots</h3></li>
+<a href="../IVC.htm"> click here 
+   </a><h3><li>All Intensity Plots</li></h3>
+<a href="../All.htm"> click here
+   </a><h3><li>Error Graphs</li></h3>
+<a href="Error.htm"> click here
+   </a><h3><li>Error Curves</li></h3>
+<a href="Perfect.htm"> click here
+   </a>
+</body></html>