From: Diane Trout Date: Mon, 16 Mar 2009 22:49:11 +0000 (+0000) Subject: Parse runfolders generated with IPAR 1.3 and pipeline 1.3.2 X-Git-Tag: 0.2.0.2~12 X-Git-Url: http://woldlab.caltech.edu/gitweb/?p=htsworkflow.git;a=commitdiff_plain;h=3424d087614b4873f013bc462898fa1b20a6fd31 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 edd9036..08d3d41 100644 --- 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 9113d6f..a954420 100644 --- 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 ca5fa50..bce8184 100644 --- 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 72c1a1a..5354cfb 100644 --- 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 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 index 0000000..c9eaca9 --- /dev/null +++ b/htsworkflow/pipelines/test/testdata/Summary-ipar130.htm @@ -0,0 +1,9325 @@ + +

Summary Information For Experiment 090313_HWI-EAS229_0101_3021JAAXX

+

Chip Summary

+ + + + + + + + + + + + + +
MachineHWI-EAS229
Run Folder090313_HWI-EAS229_0101_3021JAAXX
Chip IDunknown
+

Chip Results Summary

+ + + + + + + + + + + +
ClustersClusters (PF)Yield (kbases)
12892198621940781811809
+

Lane Parameter Summary

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Lane Sample IDSample TargetSample TypeLengthFilterChast. Thresh.Num TilesTiles
1unknownmm9ELAND_EXTENDED37'((FAILED_CHASTITY<=1.000000))'0.600000100 + Lane 1
2unknownelegans190ELAND_EXTENDED37'((FAILED_CHASTITY<=1.000000))'0.600000100 + Lane 2
3unknownelegans190ELAND_EXTENDED37'((FAILED_CHASTITY<=1.000000))'0.600000100 + Lane 3
4unknownelegans190ELAND_EXTENDED37'((FAILED_CHASTITY<=1.000000))'0.600000100 + Lane 4
5unknownelegans190ELAND_EXTENDED37'((FAILED_CHASTITY<=1.000000))'0.600000100 + Lane 5
6unknownelegans190ELAND_EXTENDED37'((FAILED_CHASTITY<=1.000000))'0.600000100 + Lane 6
7unknownelegans190ELAND_EXTENDED37'((FAILED_CHASTITY<=1.000000))'0.600000100 + Lane 7
8unknownmm9ELAND_EXTENDED37'((FAILED_CHASTITY<=1.000000))'0.600000100 + Lane 8
+

Lane Results Summary

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Lane InfoTile Mean +/- SD for Lane
Lane Lane Yield (kbases) Clusters (raw)Clusters (PF) 1st Cycle Int (PF) % intensity after 20 cycles (PF) % PF Clusters % Align (PF) Alignment Score (PF) % Error Rate (PF)
157684126910 +/- 430015590 +/- 487454 +/- 14124.36 +/- 27.0512.29 +/- 3.827.36 +/- 1.845.92 +/- 1.644.52 +/- 0.58
278644165739 +/- 679221255 +/- 503476 +/- 1688.44 +/- 20.5912.94 +/- 3.5213.55 +/- 0.8312.33 +/- 0.845.05 +/- 0.24
368671196565 +/- 821618559 +/- 5413106 +/- 1978.51 +/- 19.659.41 +/- 2.641.07 +/- 0.100.81 +/- 0.086.27 +/- 0.46
4126273153897 +/- 850134128 +/- 798475 +/- 12110.44 +/- 26.0322.13 +/- 4.824.53 +/- 0.364.51 +/- 0.383.58 +/- 0.22
5116257135536 +/- 390831420 +/- 503970 +/- 10116.68 +/- 24.4623.21 +/- 3.844.25 +/- 0.394.19 +/- 0.413.62 +/- 0.26
6159230154083 +/- 931543035 +/- 1019379 +/- 14123.00 +/- 28.8027.76 +/- 5.593.64 +/- 0.303.53 +/- 0.313.48 +/- 0.30
7180779159991 +/- 929248859 +/- 842082 +/- 14121.76 +/- 24.7530.47 +/- 4.530.86 +/- 0.100.58 +/- 0.073.11 +/- 0.36
824267198479 +/- 176716625 +/- 1773122 +/- 1473.80 +/- 14.413.30 +/- 0.6848.65 +/- 5.6143.07 +/- 6.503.28 +/- 0.29
Tile mean across chip
Average1614002743483104.6217.6910.499.374.11
+

Expanded Lane Summary

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Lane InfoPhasing InfoRaw Data (tile mean)Filtered Data (tile mean)
Lane Clusters (tile mean) (raw) % Phasing % Prephasing % Error Rate (raw) Equiv Perfect Clusters (raw) % retained Cycle 2-4 Av Int (PF) Cycle 2-10 Av % Loss (PF) Cycle 10-20 Av % Loss (PF) % Align (PF) % Error Rate (PF) Equiv Perfect Clusters (PF)
11269110.48000.30005.57307612.2965.81 +/- 15.68-0.47 +/- 1.842.12 +/- 1.137.364.521075
21657400.48000.30005.96607612.9476.73 +/- 12.151.47 +/- 1.921.21 +/- 1.9013.555.052541
31965660.48000.30007.474889.41103.17 +/- 13.332.71 +/- 1.991.26 +/- 1.881.076.27173
41538980.48000.30004.79332222.1395.07 +/- 11.421.28 +/- 2.122.69 +/- 1.554.533.581402
51355360.48000.30004.75258223.2187.51 +/- 10.871.01 +/- 2.072.00 +/- 1.534.253.621221
61540840.48000.30004.76327027.76109.23 +/- 14.971.17 +/- 2.123.01 +/- 1.733.643.481422
71599910.48000.30004.36100130.47109.94 +/- 12.231.48 +/- 2.322.30 +/- 2.470.863.11394
81984790.48000.30005.16108763.30115.81 +/- 11.512.09 +/- 1.671.19 +/- 2.9148.653.282958
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Lane 1

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3000819400189.2361.395.861.040.816.44
3000919336094.6585.166.540.990.716.31
3001018128883.6878.676.561.070.846.66
3001118787394.0082.456.231.010.755.91
3001218540570.3580.566.461.090.786.35
3001316263864.4280.874.881.060.836.76
3001419716695.8360.686.701.050.836.14
3001519348883.3569.867.031.070.846.26
3001620553779.45116.658.991.080.856.17
30017192552108.7555.957.380.890.637.00
30018200383103.7059.237.501.000.755.93
3001920192681.4766.407.420.850.695.70
3002018257181.1270.397.161.130.836.48
3002118669882.6780.047.101.000.775.77
3002218953284.5380.957.751.000.776.23
3002318832388.7273.127.831.150.856.81
3002417838383.00119.946.880.970.745.47
3002519204997.1273.728.400.970.775.42
3002619702898.6292.197.990.850.606.68
3002719089992.3869.018.771.180.965.56
3002818939989.2096.308.041.050.736.62
3002918466278.98108.678.071.080.825.86
3003019068892.4366.087.910.950.716.76
3003119629097.80110.619.391.040.825.42
30032196748103.58108.289.200.990.795.86
30033201464133.7054.1510.831.090.767.01
3003420177898.12115.4112.761.090.816.42
30035195338101.5265.7711.601.160.856.61
30036205839137.8074.5511.891.140.826.16
3003719538694.28112.5411.241.030.806.15
3003819272689.8367.1611.221.020.756.73
3003919264288.30126.6411.391.130.906.10
30040203095123.5385.1013.231.000.786.57
30041201324126.2377.2013.611.230.966.23
30042194426102.3075.0711.980.910.676.30
30043197512104.6272.8812.631.150.826.74
30044196006126.1261.6312.481.150.896.33
30045197592129.0571.4814.251.301.006.77
30046195797118.4588.6414.491.140.846.48
30047194741128.0078.3812.621.190.876.64
30048197436124.1579.6814.711.200.846.80
30049196566120.5584.5515.841.240.916.51
30050196255107.5789.3615.931.150.935.66
30051198773129.7295.8615.541.190.866.44
30052197968134.0079.4613.941.070.786.47
30053198600134.5557.3213.101.160.896.35
30054197173113.2583.2014.051.140.826.53
30055195506104.7579.3311.141.060.766.73
30056193200105.3088.8211.171.080.786.80
30057194216121.0273.0610.951.090.776.45
30058199530139.8067.0112.721.190.906.09
30059202163127.1568.9312.501.080.835.87
30060200721130.5053.9811.621.110.886.27
30061198041106.8565.7912.351.070.796.75
30062197475105.6774.9210.841.130.807.21
30063196647102.3888.6211.751.030.726.89
30064198472128.3083.9413.021.130.807.11
30065207784112.4391.9711.580.960.746.31
3006619257592.03105.929.691.130.866.44
30067195245103.5091.3810.021.080.866.17
30068198723106.4281.548.831.250.907.09
30069198214118.1558.998.650.970.745.99
3007019077086.2573.457.750.910.755.41
30071196479105.0352.378.181.130.865.94
30072200551113.7254.808.511.010.736.82
30073211144140.7846.4710.170.980.756.30
30074213445141.6247.279.631.100.856.29
30075212336138.6843.638.820.980.776.20
30076201544114.7855.118.910.920.705.75
3007719309993.45130.829.941.030.825.86
30078192730113.3857.537.040.830.725.07
30079208296146.5083.219.011.060.757.18
30080198441126.6046.547.661.070.786.69
30081198612125.23101.208.251.070.736.21
30082211383135.9369.389.921.090.825.97
30083205392120.1549.528.891.060.787.11
30084209166132.1248.007.411.020.795.75
30085199610106.67107.977.221.030.845.81
3008620088488.3563.956.511.221.005.74
30087202820122.6288.347.901.070.846.27
30088212070106.8872.707.901.090.815.97
30089198992113.7278.197.011.030.785.93
30090210462100.10123.989.421.050.805.99
3009119813185.1878.027.491.020.756.21
3009219588899.8876.577.101.080.826.54
30093204882113.3058.278.771.130.836.31
30094194269103.7566.317.251.000.796.23
30095210191120.0555.108.581.100.836.06
30096199862111.0559.705.991.080.766.31
3009719039295.5369.338.081.371.046.40
30098201697138.3874.926.121.020.786.28
3009919031490.5892.587.911.210.846.13
30100211178123.4369.788.240.980.785.90
+

Lane 4

 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Lane TileClusters (raw)Av 1st Cycle Int (PF)Av % intensity after 20 cycles (PF)% PF Clusters % Align (PF) Av Alignment Score (PF)% Error Rate (PF)
4000115997282.6789.4518.604.244.083.55
4000215434574.00102.6017.124.434.403.29
4000314903868.75107.4916.524.704.773.29
4000415420867.6796.2717.364.164.053.58
4000515979361.13119.8814.934.154.163.49
4000616018551.15122.0913.634.294.373.45
4000714917456.97168.0615.414.604.773.43
4000813772170.80142.1316.915.385.393.64
4000914136064.75144.0919.124.634.763.44
4001013891258.08103.8713.675.665.603.77
4001115619271.6594.7315.984.574.483.66
4001215148558.5898.8915.455.084.923.94
4001314854278.0073.1416.064.654.703.34
4001414728465.98109.0618.574.864.803.68
4001515229573.3288.7119.264.264.313.42
4001615007360.65137.2617.335.145.143.69
4001714565776.27134.7418.674.724.603.82
4001815227467.92146.2320.684.644.713.51
4001916199886.2861.9521.884.274.093.95
4002013917868.3394.8017.105.075.083.43
4002114425169.20119.8019.675.205.153.57
4002215420264.55150.5018.844.824.973.12
4002315480270.13120.7121.314.394.493.33
4002415561778.33108.9716.724.884.833.42
4002514649757.23141.6819.744.814.883.47
4002614354472.0887.5118.885.035.013.55
4002716461467.3085.1019.324.444.493.56
4002815271473.1281.9118.154.844.913.49
4002915196170.8583.4220.604.434.463.48
4003014975176.0593.2920.815.115.013.74
4003116229596.4596.7325.744.263.964.04
4003216244264.57171.3525.314.254.143.93
4003315659688.9272.2522.774.434.303.92
4003414837659.43174.6319.785.255.323.63
4003514420675.6779.3527.194.804.763.76
4003613898755.15153.9418.634.794.693.85
4003713508366.1085.2921.964.854.634.11
4003815393460.40106.9122.094.955.013.59
4003914813766.30138.7624.024.564.523.76
4004015844384.60141.0228.124.554.533.62
4004115371987.7870.3826.954.694.543.91
4004215086973.0787.6521.314.704.673.77
4004315642570.20107.3423.594.244.313.60
4004415687877.2299.4824.994.664.643.74
4004515513376.70106.9129.124.564.463.80
4004615225080.03101.7226.174.614.473.94
4004714987471.4799.9719.184.985.063.68
4004815401563.02121.9826.894.364.343.75
4004915833580.30119.7733.144.104.113.47
4005015979888.50116.1331.674.274.363.16
4005116007990.17119.4636.324.204.073.25
4005215529081.90131.8429.524.514.533.55
4005315898080.47101.2730.614.144.073.67
4005415842178.83121.4729.544.574.523.52
4005515775067.22112.0126.514.154.253.66
4005615701686.9596.1226.144.384.273.80
4005715179984.08119.9827.604.264.153.83
4005815431672.90153.4325.864.284.203.87
4005915708289.6073.1927.084.404.373.60
4006015302259.10137.9426.124.534.693.38
4006115104371.2590.2123.264.394.533.42
4006215948788.62113.6822.094.564.324.05
40063163188100.53115.7226.634.324.363.45
4006413670163.62120.4333.633.673.693.82
4006513606270.9079.5823.054.784.633.88
4006614883557.35146.3827.344.194.413.17
4006715232578.15131.8023.844.344.303.67
4006815542098.5858.6924.764.444.383.54
4006914880383.63102.9625.184.714.803.46
4007014666675.55138.4527.434.724.723.76
4007116135869.85118.5819.734.104.143.60
4007216375680.7093.8022.764.304.433.27
40073169752100.7597.7927.234.444.313.45
40074153606105.5790.4827.164.764.673.36
4007515584099.6765.3422.834.754.593.70
4007616058077.55129.9826.624.534.573.35
4007713354372.98149.5419.365.165.432.96
4007816387966.4787.0628.074.034.053.61
40079160311100.6276.5224.024.634.453.70
4008014755299.77122.1526.944.324.403.18
4008117572867.38147.0120.253.894.073.24
40082159621100.6073.8624.944.544.543.50
4008315172383.7297.1320.834.604.673.38
4008414929175.22114.4622.024.834.873.48
4008515864086.8286.5519.364.264.103.60
4008614995373.95111.4319.775.065.073.66
4008715998299.40103.1416.814.604.523.48
4008818405558.55114.9024.893.463.523.55
4008915997273.67104.2417.244.254.083.85
40090164917101.95109.4418.704.844.693.52
4009115245371.45112.0718.894.414.373.50
4009215521980.2093.5217.644.494.483.60
4009316390087.6570.6816.484.304.133.60
4009414535582.1082.8615.254.454.343.59
4009513811283.02123.4917.944.704.653.55
4009615465457.23169.7713.994.484.593.47
4009716770980.4087.1318.504.093.983.77
4009816017569.58131.3020.003.953.893.57
4009916539873.87121.9322.003.913.973.17
4010015699878.80133.6623.094.114.143.33
+

Lane 5

 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Lane TileClusters (raw)Av 1st Cycle Int (PF)Av % intensity after 20 cycles (PF)% PF Clusters % Align (PF) Av Alignment Score (PF)% Error Rate (PF)
5000113965872.90128.7421.853.753.673.28
5000214096466.42101.9221.293.733.583.77
5000313908568.25102.4217.784.294.223.33
5000413835269.20110.3021.413.833.713.75
5000513944474.35106.8619.884.113.983.56
5000613962159.90104.1318.863.673.513.93
5000713904864.80103.7419.503.723.483.69
5000814116064.6798.7618.083.593.413.78
5000913924562.2394.0918.473.893.543.97
5001013821157.3594.4218.353.833.713.57
5001113697558.2793.2218.294.044.043.34
5001213599257.3586.0917.514.114.003.85
5001314093053.18140.4818.623.973.853.73
5001414031363.6593.0517.483.753.453.85
5001514181574.3881.4517.773.923.733.70
5001613820677.85109.3419.303.823.593.65
5001714066187.75105.4118.564.574.383.42
5001813913364.8377.8221.374.054.043.54
5001913891977.03122.3622.404.043.923.62
5002013670767.1298.3219.014.604.513.41
5002114028459.67110.9818.314.134.043.82
5002213588754.6097.7116.943.723.653.73
5002313606556.40125.9819.934.124.223.49
5002413779362.90131.2820.664.124.023.70
5002513434962.10115.3423.384.073.963.62
5002613880254.20131.5520.524.054.113.44
5002713419156.70167.2021.614.644.703.55
5002813559657.73151.9325.574.544.543.61
5002913249378.58126.4422.195.034.833.77
5003013257064.17157.6524.174.674.524.17
5003113786484.25111.9031.204.314.153.87
5003213647154.62150.3028.334.674.673.68
5003313306582.12107.4022.894.724.364.29
5003413771366.05126.5726.094.334.213.91
5003513015559.4083.1621.504.694.873.50
5003613578386.4590.6621.284.704.503.77
5003713164167.3080.7922.364.294.223.68
5003813072864.1598.9920.055.024.933.91
5003913209060.8399.7519.364.954.814.01
5004013421151.10136.9414.114.844.714.03
5004113310293.6266.5721.274.604.433.70
5004213292572.62104.2324.154.124.033.89
5004313260276.58129.0222.134.714.493.87
5004413141366.65108.9325.854.474.343.85
5004512992582.50115.2421.634.654.384.19
5004613168761.55108.5723.724.244.353.43
5004712892876.20134.0226.024.514.283.99
5004812933882.2398.2428.084.534.433.53
5004912928059.00149.6228.804.324.183.96
5005012749171.47121.5530.584.264.133.81
5005113049377.25106.0230.244.354.363.34
5005213085262.63157.5228.304.454.463.44
5005312999373.77114.5024.844.844.803.68
5005413207172.70113.5530.554.384.313.65
5005513165273.97109.7027.144.724.643.69
5005613159073.28113.9926.474.244.173.62
5005713227879.7791.1026.244.494.443.56
5005813200668.95114.6821.474.624.593.93
5005913109660.10146.5524.154.324.303.87
5006013835959.00169.9224.174.294.343.45
5006113047264.83124.8418.734.324.173.86
5006213211064.70105.4121.424.454.304.10
5006313692666.60115.2423.774.113.963.84
5006413294880.10102.6826.123.643.523.83
5006513263080.0290.8224.464.504.543.53
5006613482764.57103.6019.555.255.163.94
5006713193065.08105.9219.204.875.013.46
5006813397588.0088.3220.494.404.403.39
5006913232256.48119.6524.404.374.443.43
5007013182664.92142.3625.014.184.113.85
5007113185956.25163.0723.434.714.683.57
5007213271378.30124.2722.725.165.213.22
5007313512597.5398.7727.414.584.473.51
5007413331978.48169.3232.464.224.293.25
5007513577883.6598.8328.144.504.662.93
5007613310955.02125.8530.993.683.613.76
5007713162765.42140.7323.914.564.732.95
5007814066594.4381.7324.253.913.933.36
5007913271084.3383.9023.844.033.903.52
5008013472880.50145.9330.494.084.043.45
5008114369274.45113.3627.334.024.063.57
5008213609282.20149.8228.864.354.303.50
5008313543078.88141.5826.774.834.793.37
5008413800070.45145.5624.054.524.463.36
5008513747461.35168.9926.203.803.833.54
5008613360765.4799.5822.233.733.653.71
5008713537070.42147.6420.614.043.993.39
5008813608277.0291.0721.903.623.633.55
5008913618771.58110.2025.594.013.903.72
5009013499459.78195.9825.624.134.243.15
5009113483578.85101.3318.804.174.143.48
5009214059968.4898.8020.403.533.583.39
5009314221971.75133.3821.964.094.123.30
5009414220575.38135.0623.883.613.603.47
5009513711073.45102.1123.224.144.113.57
5009614043183.15105.9525.344.284.333.12
5009714049780.83123.3526.313.753.653.45
5009814248677.9296.8226.413.703.683.50
5009914581785.30110.9327.923.853.933.16
5010013960474.98135.9824.923.713.723.21
+

Lane 6

 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Lane TileClusters (raw)Av 1st Cycle Int (PF)Av % intensity after 20 cycles (PF)% PF Clusters % Align (PF) Av Alignment Score (PF)% Error Rate (PF)
6000115365379.58136.0023.863.573.463.57
6000216073956.15162.7817.733.213.133.67
6000314713263.32127.7917.493.753.653.56
6000415160265.30109.9518.043.533.423.54
6000515695961.57109.8720.253.163.083.64
6000614699575.43108.2519.923.683.573.55
6000714761984.72103.1620.123.923.643.62
6000815786873.75139.2226.323.443.303.66
6000915500372.53126.2723.623.373.253.32
6001014801568.85141.5420.083.683.513.62
6001115394371.40106.0619.503.473.383.37
6001214885881.2286.4319.513.363.253.65
6001314423474.6090.4217.023.593.463.55
6001414300669.7091.3217.263.783.623.63
6001516633052.00154.4720.502.772.703.69
6001614463258.68215.2524.253.593.464.03
6001713624575.1298.8022.864.314.153.81
6001814542869.75143.5121.983.683.613.65
6001913691467.10160.8023.124.204.153.46
6002014817166.60162.0521.473.733.504.06
6002113491473.75128.8516.653.903.614.09
6002213865182.2076.0917.874.143.833.75
6002314942362.85105.4920.753.753.593.61
6002414345183.30104.1720.224.244.103.46
6002514754072.5091.8319.863.743.693.28
6002614115472.90100.7920.734.064.063.25
6002716078268.42127.9122.543.523.503.22
6002814813470.67121.0123.533.483.373.61
6002915446569.50109.9326.043.613.693.26
6003015176769.12187.7022.593.964.023.28
6003116414791.0096.9230.613.142.903.97
6003215327565.38174.2328.833.633.603.56
6003315372298.0889.1227.303.923.563.76
6003415219376.95102.4027.203.903.793.54
6003515346374.17107.9930.033.523.353.75
6003615089565.65166.8332.033.853.713.55
6003714574562.45116.4926.603.773.633.81
6003815128464.00181.2526.093.813.743.52
6003915142458.40199.4026.273.493.393.83
6004015764975.05119.0532.593.052.893.62
6004115106260.13208.9426.713.523.443.68
6004214721362.43124.9925.033.793.653.87
6004315312368.88123.4125.473.553.373.77
6004415009977.20115.0329.383.733.573.46
6004515295288.53108.3932.193.433.213.53
6004615098182.27104.9830.683.823.673.54
6004715350582.67139.4032.673.893.713.62
6004815383490.45119.0233.133.873.623.64
6004915222283.83121.3832.214.083.873.47
6005015179870.42130.8832.013.893.773.43
6005115443095.3391.4032.173.613.443.52
6005215787386.33102.0030.043.523.323.76
6005315248974.80127.6132.863.803.743.43
6005415440274.40132.0231.733.963.843.72
6005515191275.0396.4333.813.973.863.42
6005615546076.15111.4635.003.873.813.45
6005715411477.83138.1936.393.703.573.66
6005815636979.62125.8727.394.043.873.57
6005915615179.38106.2432.373.293.123.89
6006015510984.15108.5332.093.383.154.09
6006114235171.72140.5429.123.923.813.84
6006214914084.9095.4727.323.503.393.86
6006315984866.10158.8925.373.463.304.01
6006415935263.03119.2826.664.043.993.69
6006516244194.40128.8730.653.663.563.35
6006614179861.55109.6322.383.943.993.42
6006716068686.90121.6327.803.623.403.98
6006815253967.95125.4633.743.603.443.78
6006915525387.2578.6527.313.283.123.79
6007015808278.6092.1428.453.353.263.74
6007114864172.60160.7428.053.783.803.25
6007215182174.60146.1527.264.194.263.19
60073159347103.45121.8233.033.823.793.19
6007415259868.88190.0932.134.184.143.25
6007515801788.73137.8136.733.733.633.36
60076168580109.37119.4334.663.253.013.49
60077161724110.4078.6036.103.533.443.10
6007816831599.8882.2534.853.293.163.28
6007916048195.53124.5531.563.733.643.13
6008016399278.00120.3233.983.243.203.01
60081153658100.70101.4935.123.703.593.04
60082171593126.27113.3238.703.153.033.29
60083162775119.7065.2936.563.443.313.08
6008413324867.38133.4031.503.893.783.26
60085152741108.30119.3932.763.353.153.27
6008613827068.20127.9727.143.833.853.13
6008716477171.25147.4429.573.163.192.98
6008813112177.50134.5227.093.873.913.14
6008915055889.0081.3525.533.633.573.25
6009016218089.12102.9225.094.063.883.31
60091161673101.30108.9630.883.203.033.33
6009215882293.17117.1232.933.693.652.97
60093167698100.77111.5135.193.483.402.87
6009416911188.40117.0230.233.323.243.01
60095177848103.12117.8936.783.333.263.07
6009616613395.85110.6732.003.533.463.08
6009717802383.72145.6331.053.193.152.88
6009818035884.95151.6829.703.083.072.89
6009917056893.78102.4534.363.463.412.97
6010015970494.20119.9629.873.443.363.07
+

Lane 7

 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Lane TileClusters (raw)Av 1st Cycle Int (PF)Av % intensity after 20 cycles (PF)% PF Clusters % Align (PF) Av Alignment Score (PF)% Error Rate (PF)
7000116739769.27175.6033.310.900.612.48
7000216045574.47139.7829.550.880.592.91
7000316141282.17112.0828.640.850.553.00
70004163624101.3584.0726.100.870.533.76
7000516402192.78113.1826.890.960.653.07
7000615680675.62117.1627.590.860.602.80
7000716203873.40114.4427.610.860.602.45
70008165511107.40105.2635.620.860.562.85
7000916328287.62129.2430.110.810.573.20
7001015699268.35119.0922.630.810.532.96
7001115055272.62118.7325.240.870.602.60
7001216014068.35105.5622.160.720.473.23
7001314930285.85100.9321.860.790.503.30
7001414374084.3087.3723.580.820.572.92
7001513942758.95157.8024.120.920.633.22
7001613617772.90146.6026.280.930.632.91
7001713223769.00151.4525.131.100.792.68
7001814286966.30171.0023.940.990.752.93
7001914245669.05165.8631.970.970.632.91
7002015502960.87100.8228.640.750.523.65
7002115478362.23154.2824.080.730.543.34
7002214006054.48172.1020.641.050.712.94
7002314834771.9595.5529.020.830.582.65
7002414892960.38114.8726.970.800.592.99
7002515095067.45106.3025.610.900.633.29
7002615044867.12151.3226.180.830.603.44
7002715734380.08155.8926.530.820.583.90
7002815491976.90169.7027.220.900.623.35
7002915591677.33153.2829.530.860.603.52
7003015332774.3398.7929.260.830.623.44
70031167377101.38100.9626.850.890.593.32
7003215860080.6586.7333.060.930.623.56
7003315607071.68169.0633.610.940.652.99
7003414799159.85160.9034.161.110.723.15
7003514545476.00130.0031.631.020.682.89
7003616460069.8889.9134.740.700.443.41
7003715089273.33136.9931.620.790.543.98
7003815874071.50126.5031.620.740.583.22
7003915957976.23117.7434.900.820.563.78
7004015300973.10124.4226.481.010.703.34
7004116312978.7269.5835.200.660.433.48
7004215665287.90121.4229.830.790.533.56
7004315675783.33126.7936.070.940.633.16
7004415982281.50114.7235.180.880.592.99
7004515776196.48106.9234.860.980.623.11
7004616355082.83155.0636.161.030.642.59
70047161738108.3583.7135.480.970.603.02
7004815751789.12125.8338.040.970.623.51
7004915626884.37120.3935.930.930.633.00
7005016198592.23115.9438.210.940.613.05
7005116423692.08118.2737.060.960.592.93
7005216203287.82125.7038.330.920.573.20
7005316198592.42106.9838.000.910.573.14
7005415650778.40109.3833.590.910.632.56
7005516099199.3590.0931.610.750.543.26
7005616282594.60105.8435.810.910.603.07
7005716313595.25103.1536.030.880.552.93
7005815969385.17115.1228.950.790.573.45
7005915405277.88137.1737.450.880.583.13
7006016119889.47108.3032.870.790.553.51
7006115678276.92114.4032.300.810.573.58
7006215997080.27126.2829.280.870.603.59
7006316069686.85105.0128.670.740.543.37
7006416108289.83128.1931.120.780.552.98
70065173381111.25122.3625.050.840.573.98
7006616008593.4086.2427.820.820.602.96
7006716030180.0898.6326.390.830.613.07
70068152691101.3091.1929.110.880.593.66
7006914832570.78150.8331.020.880.633.20
7007016212585.8592.0530.620.680.483.06
7007116596268.72103.5632.700.660.463.51
7007216653593.93124.5734.010.800.523.13
70073173264106.55126.5631.720.790.503.45
70074168644101.98125.5529.960.760.543.06
7007515619971.35151.2331.660.890.593.52
7007616559470.38157.1931.080.930.633.48
7007717607293.1273.8034.360.800.503.20
70078174206115.9296.7434.010.800.503.53
7007916306592.30111.2435.110.860.612.78
7008017236759.48116.8634.711.000.643.07
70081160853106.10116.7133.530.930.602.77
7008217080967.78153.1930.680.800.542.83
7008317125069.68153.5028.970.820.552.95
7008417252390.85116.6229.600.890.572.94
7008517208397.45107.5231.450.800.522.91
7008615887990.08108.6025.960.950.633.05
70087169708104.50114.4530.170.770.503.53
7008814945168.62147.8328.240.780.493.16
7008917337086.58119.5526.950.910.632.82
7009016805985.48160.1634.640.900.573.04
7009117001889.40120.4732.400.810.522.71
7009216793377.98104.1730.920.870.612.71
7009316930567.32132.2730.500.900.632.64
7009416420975.47143.1929.490.890.612.61
7009517623976.33160.8338.910.830.562.70
70096170534109.0082.6132.381.000.632.80
70097174053113.3590.0513.000.490.352.45
70098175159110.0898.0929.740.980.652.79
70099179218100.78130.6927.530.940.642.59
7010015550088.42121.5431.600.950.632.33
+

Lane 8

 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Lane TileClusters (raw)Av 1st Cycle Int (PF)Av % intensity after 20 cycles (PF)% PF Clusters % Align (PF) Av Alignment Score (PF)% Error Rate (PF)
80001181113113.7271.640.8142.0737.022.90
80002167526104.8359.462.2136.2531.473.80
8000316213195.0871.291.9938.3532.693.67
80004174931122.2056.302.1839.6429.883.55
80005188914111.8873.432.7140.5535.063.68
8000617657495.4375.373.0043.1737.843.46
80007177213109.3259.392.4338.5633.273.59
8000817914899.5293.773.1648.0244.062.89
80009184260102.1294.663.0246.6142.163.10
8001016172188.9565.912.6142.6438.683.11
80011181269102.8558.072.5735.9730.414.29
80012182179105.4764.922.9644.4340.583.19
8001316705898.9772.422.9543.8238.673.18
80014181070103.4569.432.9944.7239.393.40
80015192314128.2252.412.7840.5033.403.57
80016198144120.3056.902.9244.3937.083.62
80017202972130.8559.513.1247.2940.243.47
80018201819134.9052.712.8043.9137.453.57
80019209515140.4747.412.8446.1639.353.52
80020176152111.7260.062.6040.8733.933.67
80021176568110.1568.322.5944.6239.063.50
80022169602108.7575.792.8748.0843.732.98
80023189324102.4883.462.8847.0641.363.52
80024171625116.5860.632.9847.6142.473.45
8002516209297.65114.413.2348.3239.953.10
80026166734106.85113.293.3550.8146.103.18
80027188771135.7089.903.3651.3645.203.37
80028208995143.7874.373.4550.6644.313.37
80029189249102.97101.463.5050.7046.133.16
80030168319113.25101.503.3952.5247.693.17
80031207260148.2865.943.5553.1647.513.22
80032214286117.7799.773.0350.9346.693.12
80033204280138.0564.513.3351.1345.233.30
80034218862143.2749.693.5551.4645.873.09
80035212945126.7869.434.0453.3547.423.14
80036209646122.4079.744.1855.9450.402.95
80037200910112.3088.693.8454.0749.682.95
80038208228123.5381.383.9255.2550.762.99
80039218373131.2081.633.9756.8052.262.90
80040210877115.6085.623.9654.5449.832.95
80041205484116.0087.263.7852.5844.034.66
80042201755105.4094.123.8055.6650.723.11
80043199044120.7580.353.6852.9947.783.14
80044194716120.9374.053.6853.3548.073.23
80045213702129.8066.413.8553.6047.913.10
80046212117108.7594.673.8558.0253.313.05
80047222320108.6296.183.6458.3154.052.86
80048215015129.8570.454.0255.0548.913.28
80049212941128.0567.363.9553.5847.393.30
80050207426119.8870.704.2754.2249.053.14
80051221088120.4880.274.9857.3452.493.09
80052206850113.8577.054.2854.5050.163.13
80053218985128.3571.394.4155.4650.363.15
80054215826125.5080.324.5155.5751.033.05
80055217025123.7581.944.3855.0650.343.06
80056221504133.7265.404.1753.4948.733.11
80057217253125.7859.094.0153.6048.373.17
80058217739133.2059.313.9053.2346.933.35
80059206901119.10103.573.5951.5945.563.32
80060209276133.2575.553.9153.2348.593.14
80061206912116.4280.823.8753.3748.653.08
80062208293130.9376.973.9953.8349.453.02
80063216117140.8568.024.0453.9648.523.11
80064221626138.3868.514.0753.9948.413.20
80065226495119.1790.854.2656.1352.262.98
80066211217122.7577.134.0753.1348.263.22
80067207572117.9785.403.9053.4848.973.05
80068212274130.4777.973.7850.9245.663.15
80069225642151.3267.643.4347.9941.143.46
80070206267130.8544.733.0445.2039.723.49
80071197697116.0055.882.8744.8640.683.36
80072201670131.1554.333.1049.6345.223.23
80073199697141.8552.343.2150.8147.202.95
80074220224146.2266.883.1050.2445.853.01
80075198282128.4072.593.1750.4146.143.12
80076187773131.5566.253.3050.9645.633.20
80077186366113.85100.553.1350.3246.193.15
80078189058133.5791.372.9047.0142.993.19
80079213947164.3057.353.1548.4944.573.09
80080221739121.9574.932.7144.1535.063.23
80081205044132.7549.512.7942.3936.713.48
80082210695129.4380.633.5649.5044.263.29
80083217007139.4572.433.4748.6340.363.69
80084210797144.3570.663.4248.6443.353.29
80085211477142.2273.723.3847.6436.573.26
80086200320117.9563.593.0345.1540.323.25
80087204295136.7557.183.3548.2444.393.04
80088203778141.1865.793.2146.7942.243.13
80089209681124.6582.433.2247.8944.072.95
80090199690129.0569.822.9646.2041.843.11
80091202793119.6079.183.1347.4142.533.12
80092188850109.6074.643.1744.8140.443.26
80093188951119.9577.432.8944.1439.453.29
80094177924125.0571.752.1937.0529.903.82
80095175164103.0088.762.7940.6729.893.75
80096170691109.4074.771.2737.5431.244.00
800971777150.00N/A0.000.000.000.00
80098168260121.3864.302.1436.2020.993.23
80099202629149.7272.232.5141.1334.743.56
80100162617125.7266.752.5341.0731.793.48
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