Analysis of mass spectrometric data: peak picking and map alignment

212 Pages·2008·2.72 MB·English

Analysis of mass spectrometric data:
peak picking and map alignment
Dissertation
zurErlangungdesakademischenGradeseines
DoktorsderNaturwissenschaften
imFachbereichMathematikundInformatik
derFreienUniversita¨tBerlin
vorgelegtvon
EvaLange
Berlin2008
DatumdesKolloquiums: 13. Juni2008
Dekan:
ProfessorDr. Ralph-HardoSchulz
Betreuer:
ProfessorDr. KnutReinert
FreieUniversita¨tBerlin
Institutfu¨rInformatik
AlgorithmischeBioinformatik
Takustraße9
D-14195Berlin
Gutachter:
ProfessorDr. KnutReinert,FreieUniversita¨tBerlin,Berlin
ProfessorDr. OliverKohlbacher,EberhardKarlsUniversita¨tTu¨bingen,Tu¨bingen
Abstract
We study two fundamental processing steps in mass spectrometric data analysis from a
theoreticalandpracticalpointofview.
For the detection and extraction of mass spectral peaks we developed an efficient peak
picking algorithm that is independent of the underlying machine or ionization method, and is
able to resolve highly convoluted and asymmetric signals. The method uses the multiscale
natureofspectrometricdatabyfirstdetectingthemasspeaksinthewavelet-transformedsignal
beforeagivenasymmetricpeakfunctionisfittedtotherawdata. Intwooptionalstages,highly
overlapping peaks can be separated or all peak parameters can be further improved using
techniquesfromnonlinearoptimization. Incontrasttocurrentlyestablishedtechniques,oural-
gorithmisabletoseparateoverlappingpeaksofmultiplychargedpeptidesinLC-ESI-MSdata
of low resolution. Furthermore, applied to high-quality MALDI-TOF spectra it yields a high
degree of accuracy and precision and compares very favorably with the algorithms supplied
bythevendorofthemassspectrometers. Onthehigh-resolutionMALDIspectraaswellason
thelow-resolutionLC-MSdataset,ouralgorithmachievesafastruntimeofonlyafewseconds.
Another important processing step that can be found in every typical protocol for label-
freequantificationisthecombinationofresultsfrommultipleLC-MSexperimentstoimprove
confidence in the obtained measurements or to compare results from different samples. To
do so, a multiple alignment of the LC-MS maps needs to be estimated. The alignment has
to correct for variations in mass and elution time which are present in all mass spectrometry
experiments. For the first time we formally define the multiple LC-MS raw and feature
map alignment problem using our own distance function for LC-MS maps. Furthermore,
we present a solution to this problem. Our novel algorithm aligns LC-MS samples and
matches corresponding ion species across samples. In a first step, it uses an adapted pose
clusteringapproachtoefficientlysuperimposerawmapsaswellasfeaturemaps. Thisisdone
in a star-wise manner, where the elements of all maps are transformed onto the coordinate
system of a reference map. To detect and combine corresponding features in multiple feature
maps into a so-called consensus map, we developed an additional step based on techniques
from computational geometry. We show that our alignment approach is fast and reliable
as compared to five other alignment approaches. Furthermore, we prove its robustness in
the presence of noise and its ability to accurately align samples with only few common ion
species.

Acknowledgments
The research presented in this thesis was carried out in the Algorithmic Bioinformatics group
oftheFreieUniversita¨tBerlinfrom2003to2007. Firstandforemost,Iwouldliketothankmy
supervisorKnutReinert,notonlyfortheopportunitytopursuethisresearchandtogaininsight
intotheexcitingfieldofbioinformatics,butalsoforcreatingahospitableatmosphereinwhich
it was a pleasure to work. Finally, I am deeply grateful for Knut’s valuable scientific advice
I could always rely on. I would also like to thank Oliver Kohlbacher, who co-supervised my
thesisanddirectedmethroughmanyfruitfuldiscussions.
Thisthesiscanlooselybesplitintwoparts,peakpickingandmapalignment. AndreasHilde-
brandthelpedmeagreatdealwithmyworkonpeakpickingandsupportedmewhenIwrotemy
firstpublication. WhatAndreaswasforpeakpicking,ClemensGro¨plwasformapalignment—
Thank you guys! I also want to thank my direct collaborators in the OpenMS project: Marc
Sturm, Andreas Bertsch, Clemens Gro¨pl, Andreas Hildebrandt, Rene Hussong, Nico Pfeifer,
Ole Schulz-Trieglaff, Alexandra Zerck, Knut Reinert, and Oliver Kohlbacher. Alexandra sig-
nificantlycontributedtothepresentthesiswhilewritingherownMaster’sthesis. RalfTauten-
hahnoftheLeibnizInstituteofPlantBiochemistryinHallecarriedoutapartoftheevaluation
of map alignment algorithms. Especially in the early years, Andreas Do¨ring spent endless
hoursteachingmehowtoprogramC++. ForthatIaminhisdebt. MichaelDorrandCaroline
Ku¨hnelreadandcommentedonearlyversionsofthisthesis.
LastbutnotleastIwouldliketothankmyparentsfortheircontinuoussupportthroughallthe
years.

Contents
I Introductionandbackground 1
1 Guidetothethesis 3
1.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Motivationandowncontribution 5
3 Massspectrometryinproteomics 13
3.1 Principlesofmassspectrometry . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1.1 Tandemmassspectrometer(MS/MS) . . . . . . . . . . . . . . . . . . 16
3.1.2 Liquidchromatography-massspectrometry(LC-MS) . . . . . . . . . . 16
3.2 Proteinidentification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3 Proteinquantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3.1 Label-freequantification . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3.2 Labeledquantification . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4 OpenMS—Anopen-sourceframeworkformassspectrometry 21
4.1 Themapconcept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.2 Designandimplementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.2.1 Designgoals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.2.2 Overallarchitectureandfeatures . . . . . . . . . . . . . . . . . . . . . 26
4.3 Examplealgorithmsandfeatures . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.3.1 Standardizedfileformats . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.3.2 Databasesupport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.3.3 Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Contents
4.3.4 Signalprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.3.5 Peakpicking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.3.6 Featuredetectionandquantification . . . . . . . . . . . . . . . . . . . 28
4.3.7 LC-MSmapalignment . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.3.8 Retentiontimeprediction . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.4 TOPP—TheOpenMSProteomicsPipeline . . . . . . . . . . . . . . . . . . . . 29
II Peakpicking 31
5 Mathematicalpreliminaries 33
5.1 Uncertaintiesinmeasurements . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2 Introductiontowavelettheory . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5.2.1 ClassicalFouriertransform . . . . . . . . . . . . . . . . . . . . . . . . 35
5.2.2 WindowedFouriertransform . . . . . . . . . . . . . . . . . . . . . . . 37
5.2.3 Continuouswavelettransform(CWT) . . . . . . . . . . . . . . . . . . 38
5.3 TheLevenberg-Marquardtmethodfornon-linearleastsquaresfitting . . . . . . 40
5.3.1 TheSteepestDescentmethod . . . . . . . . . . . . . . . . . . . . . . 43
5.3.2 Gauss-Newtonalgorithm . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.3.3 Levenberg-Marquardtalgorithm . . . . . . . . . . . . . . . . . . . . . 44
6 Introductiontopeakpicking 47
6.1 Natureofmassspectrometricmeasurements . . . . . . . . . . . . . . . . . . . 48
6.2 Peakpickingproblem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
7 Relatedwork 57
8 Owncontribution 63
8.1 Generalschemaofourpeakpickingalgorithm . . . . . . . . . . . . . . . . . . 65
8.2 Peakdetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
8.2.1 Detectingapeakinthecontinuouswavelettransform . . . . . . . . . . 69
8.2.2 Searchingforapeak’smaximumanditsendpoints . . . . . . . . . . . 71
8.2.3 Estimatingapeak’scentroid . . . . . . . . . . . . . . . . . . . . . . . 71
8.3 Peakfitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
ii
Contents
8.3.1 FitofanasymmetricLorentzianandsech2 peakfunction . . . . . . . . 72
8.3.2 Examinationofthebestfittingfunction . . . . . . . . . . . . . . . . . 75
8.4 Separationofoverlappingpeaks . . . . . . . . . . . . . . . . . . . . . . . . . 76
8.4.1 Determiningthenumberofoverlappingpeaks . . . . . . . . . . . . . . 78
8.4.2 Discriminatingoverlappingpeaks . . . . . . . . . . . . . . . . . . . . 78
8.5 Optimizationofallpeakparameters . . . . . . . . . . . . . . . . . . . . . . . 81
8.5.1 ThePeakPickerTOPPtool . . . . . . . . . . . . . . . . . . . . . . . . 82
9 Experiments 85
9.1 SamplepreparationandMSanalysis . . . . . . . . . . . . . . . . . . . . . . . 86
9.2 MassaccuracyandseparationcapabilityinlowresolvedLC-MSmeasurements 86
9.3 MassaccuracyinhighresolutionMALDI-TOFmeasurements . . . . . . . . . 89
9.3.1 Spectracalibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
10 Discussionandconclusion 93
III Mapalignment 95
11 Computationalgeometrypreliminaries 97
11.1 Voronoidiagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
11.2 Delaunaytriangulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
11.3 k-nearestneighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
12 IntroductiontoLC-MSmapalignment 101
12.1 LC-MSmapalignmentproblems . . . . . . . . . . . . . . . . . . . . . . . . . 102
12.2 AdistancefunctiondsimforLC-MSmaps . . . . . . . . . . . . . . . . . . . . 103
12.3 Multiplerawandfeaturemapalignmentproblem . . . . . . . . . . . . . . . . 107
13 Relatedwork 111
13.1 Generalapproachesforpointpatternmatchingproblems . . . . . . . . . . . . 111
13.2 MultipleLC-MSmapalignmentalgorithms . . . . . . . . . . . . . . . . . . . 114
14 Owncontribution 121
14.1 Implementationandapplicationsofdsim . . . . . . . . . . . . . . . . . . . . . 121
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Contents
14.2 MultipleLC-MSmapalignment . . . . . . . . . . . . . . . . . . . . . . . . . 122
14.2.1 Thesuperpositionphase . . . . . . . . . . . . . . . . . . . . . . . . . 123
14.2.2 ApplicationtoLC-MSrawmaps . . . . . . . . . . . . . . . . . . . . . 136
14.2.3 Theconsensusphase . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
14.2.4 ApplicationtoLC-MSfeaturemaps . . . . . . . . . . . . . . . . . . . 138
14.2.5 TheMapAlignmentTOPPtool . . . . . . . . . . . . . . . . . . . . . . 143
15 Experiments 145
15.1 Usageofthedifferentfeaturemapalignmenttools . . . . . . . . . . . . . . . . 146
15.1.1 OpenMSalignmentalgorithmOpenMS . . . . . . . . . . . . . . . . 146
MA
15.1.2 msInspectalignmentalgorithmmsInspect . . . . . . . . . . . . . . 146
MA
15.1.3 SpecArrayalignmentalgorithmSpecArray . . . . . . . . . . . . . . 146
MA
15.1.4 XAlign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
15.1.5 XCMSalignmentalgorithmXCMS . . . . . . . . . . . . . . . . . . 147
MA
15.1.6 MZminealignmentalgorithmMZMine . . . . . . . . . . . . . . . . 147
MA
15.2 Evaluationoftheconsensusmaps . . . . . . . . . . . . . . . . . . . . . . . . 148
15.2.1 Recallandprecisionofmultiplefeaturemapalignmentalgorithms . . . 148
15.3 Experimentaldata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
15.3.1 SamplepreparationandLC-LC-MS/MSanalysis . . . . . . . . . . . . 150
15.3.2 Preprocessingandextractionofpeptidefeatures. . . . . . . . . . . . . 151
15.3.3 Generationofagroundtruth . . . . . . . . . . . . . . . . . . . . . . . 152
15.3.4 Samplewithdifferentinjectionvolume . . . . . . . . . . . . . . . . . 154
15.3.5 Differentbiologicalstate . . . . . . . . . . . . . . . . . . . . . . . . . 157
15.4 Robustnessanalysiswithsimulateddata . . . . . . . . . . . . . . . . . . . . . 160
15.4.1 SamplepreparationandLC-MSanalysis . . . . . . . . . . . . . . . . 160
15.4.2 Preprocessingandextractionofpeptidefeatures. . . . . . . . . . . . . 160
15.4.3 AlignmentofnoisyLC-MSmaps . . . . . . . . . . . . . . . . . . . . 161
15.4.4 Aligningmapswithlittleoverlap. . . . . . . . . . . . . . . . . . . . . 170
16 Discussionandconclusion 177
17 AvailabilityandrequirementsoftheOpenMS/TOPPproject 179
iv