Statistical mass spectrometry-based proteomics
|
|
- Homer Park
- 6 years ago
- Views:
Transcription
1 1 Statistical mass spectrometry-based proteomics Olga Vitek
2 Outline What is proteomics? Biological questions and technologies Protein quantification in label-free workflows Joint analysis of multiple features and conditions Protein quantification in label-based workflows Appropriately account for the labeling structure Mass spectrometry-based imaging Account for the spacial heterogeneity of spectral data
3 Proteomics: system-wide characterization of all proteins Sequence, structure, localization, abundance, PTMs, interactions More challenging than gene expression Complexity Human genome: ~0,000 protein coding genes Their translation+splicing+proteolysis: ~50, ,000 proteins Somatic DNA rearrangements and PTM: ~10 million Dynamic range > 10 orders of magnitude in plasma Unlike nucleotides, proteins cannot be amplified How to make progress? Goals.of.proteomics Sample preparation, separation, sensitive instruments Statistical experimental design and accurate analysis 3
4 Acquisi7on.of.mass.spectra Single Stage MS protein peptides peptides Digestion Ionization Mass Analysis MS m/z 4
5 Liquid.chromatography.coupled.with.mass. spectrometry.(lcams) '()%*+"%!"#$"%!#&,01,01,01,01 #!,$)-%*+")./ Hydrophobicity 5
6 Liquid.chromatography.coupled.with.mass. spectrometry.(lcams) $,#&,)$#/ %01 %01 %01 %01!" Hydrophobicity!"0!" #$%&'()*+,'-. 6
7 Ahrens et al., Nature Reviews, 010 Proteomics.is. increasingly. comprehensive a Number of proteins identified Number of publications Proteome coverage (%) b 3,000,500,000 1,500 1, Year Year 10,000 5,000,000 1, , ,738,013 Saccharomyces cerevisiae Aribadopsis thaliana Drosophila melanogaster Caenorhabditis elegans ,345, ,740 3, Assay technology Instrumentation Computational solutions 50 Year ICAT Mascot Shotgun proteomics SILAC LTQ LTQ-FT OrbiTrap MRM Peptide- Protein- Target Max- 7
8 Sample preparation Global LC-MS/MS Global.proteomic.workflows Käll and Vitek, PLoS Computational Biology, 7, 011 8
9 Sample preparation Targeted SRM Targeted.proteomic.workflows Käll and Vitek, PLoS Computational Biology, 7, 011 9
10 Varia7on.is.experimentAspecific LC-MS: Human diabetes SRM: yeast proteome (Picotti et al, 009) Difference of intensities Affy: ALL (Acute Leukemia) SRM: Human ovarian cancer (Hüttenhain et al, 01)
11 Outline What is proteomics? Biological questions and technologies Protein quantification in label-free workflows Joint analysis of multiple features and conditions Protein quantification in label-based workflows Appropriately account for the labeling structure Mass spectrometry-based imaging Account for the spacial heterogeneity of spectral data 11
12 Example:.labelAfree.LCAMS High/low.invasive.breast.cancer.cell.lines Goal:.proteinAlevel.conclusions Safia Thaminy ETHZ Average log(intensity) Average log(intensity) 6 4 hypoxia low invasive normoxia low invasive hypoxia high invasive normoxia high invasive low nm 6 hrs low nm 4 hrs low hyp 6 hrs low hyp 4 hrs high nm 6 hrs Condition high nm 4 hrs high hyp 6 hrs high hyp 4 hrs 0 6 hours 4 hours Time 6 hours 4 hours 1
13 Important.differences.with.microarrays Oligonucleotide microarrays RMA: Tukey Median Polish Robust averaging of all probes Fluorescent image into array-specific summary Proteomics Number and quality of features per protein vary widely Missing features introduce imbalance Label-based workflows combine multiple samples blocking structure Targeted workflows create a nested structure protein/peptide/transition Explicit probabilistic models best represent the data PM MM AGATCGATCAGTCAGTACTAGTACT AGATCGATCAGTGAGTACTAGTACT Perfect Match (PM) probe A probe pair consists of a PM and MM probe MisMatch (MM) probe Probe pair set Sebastiani et al, Statistical Science,
14 Linear.mixed.models.for.feature.intensi7es Deviation from the reference due to log( Expected Random peak = reference + LC-MS + condition + feature condition + biol. + meas. intensity) abundance feature interaction replicate error y ijkl = µ F i + C j + (F C) ij + S(C) k(j) + " ijkl where F 1 = C 1 =(F C) i1 =(F C) 1j =0 " ijkl iid N 0, Error, ijk and (a) reduced scope of biological replication: S(C) 1(1) =0 (b) expanded scope of biological replication: S(C) k(j) iid N 0, S T. Clough et al. JPR, 009 T. Clough et al. Meth. Mol. Biol., 011 T. Clough et al. BMC Bioinformatics., 01 C.-Y. Chang et al. Mol. Cel. Proteomics, 01 Average log(intensity) low nm 6 hrs low nm 4 hrs low hyp 6 hrs low hyp 4 hrs high nm 6 hrs high nm 4 hrs Condition high hyp 6 hrs high hyp 4 hrs hypoxia low invasive normoxia low invasive 6 hours 4 hours normoxia high invasive Time hypoxia high invasive 6 hours 4 hours
15 All.inference.is.based.on.problemAspecific. linear.combina7ons.of.model.terms Quantity of interest: H 0 : L = µ [high, nm, 6] µ [low, nm, 6] =0 Model-based estimate and test statistic: IP ˆL = Ĉ[high, nm, 6] + 1 I ( F d C) i, [high, nm, 6] + 1 K i=1 IP - Ĉ [low, nm, 6] + 1 I ( F d C) i, [low, nm, 6] + 1 K t = ˆL SE{ ˆL} i=1 Student distribution KP k=1 KP k=1 S(C) d k([high, nm, 6]) S(C) d k([low, nm, 6]) In balanced datasets: ˆL = Ȳ [high, nm, 6] Ȳ [low, nm, 6] t = ˆL p IKL ˆ Error Student IJK(L 1)+(I 1)J(K 1) distribution Also.methods.for.model.diagnos7cs,.data.visualiza7on.etc 15
16 Tools.for.quan7ta7ve.proteomics Veavi&Chang Meena&Choi Tim&Clough A#variety#of#quan0ta0ve#workflow 4 Global,'targeted,'data-independent - Label-free'and'label-based Accounts#for#experimental#designs - Group'comparison,'7me'course Data#visualiza0on#&#QC Model4based#analysis - Model'fi:ng'and'inference Planning#future#experiments - Sample'size,'resource'alloca7ons Collaboration: Since 010: extensive documentation published case studies protocols for typical analyses 13 tutorials and workshops Since 011: 370 unique visitors over 50 unique downloads over 50 mailing list members Ruedi.Aebersold Michael. MacCoss 16
17 Outline What is proteomics? Biological questions and technologies Protein quantification in label-free workflows Joint analysis of multiple features and conditions Protein quantification in label-based workflows Appropriately account for the labeling structure Mass spectrometry-based imaging Account for the spacial heterogeneity of spectral data 17
18 LabelAbased.workflows.help.separate.the. biological.and.the.technological.varia7on Healthy Disease run 1 run T transition log (intensity) T1 T T3 ence enous T1 T T3 H L H L rence enous average of log-intensities of paired log-intensities of L and H difference of log-intensities of L and H = log-ratios of L over H run Group 1 Group I Run 1 Run M Subject 1 Subject J Subject 1 Subject J Peptide 1 Transition Endogenous: Transition L light labeled.. peptide Peptide K Transition Transition L Transitions Peptide 1 Transition Reference: Transition L heavy labeled.. peptide Peptide K Transition Transition L Legend : Label Feature: Transition/Peptide Group Run Subject 18
19 G STATISTICAL PROTEOMICS i =0 S(G) i=0 j=0 AND j(i) =0 F k,l=1 BIOINFORMATICS kl =0 Rm =0 m=1 ect: F; Run: F ect: R;Run: F ect: F; Run: R log (intensity) More.complex.but.similar.linear. T i (T F ) ikl mixed.effects.models Systematic deviations from the mean Statistical interactions iid ect: R;Run: R Observed Systematic deviations from the mean Statistical interactions log(int of peak) = Overall mean + Group N(0, iid S ) N(0, iid R ) N(0, Time by Group by Run by + Subject +Feature+Run+ + + Random RF ) Observed or time Systematic log(int deviations of peak) from = subject the Overall mean meant i + (T Group Ti F ) ikl feature Statistical + Subject or time feature interactions +Feature+Run+ error su me Observed log(int of peak) = Overall mean + Group Time by Group Run by (a) course: case-control: Observed + Subject +Feature+Run+ + Random log(int of peak) = Overall or time mean + Group (a) case-control: Time by Group by Run by + Subject +Feature+Run+ subject feature + + or time subject feature feature error y (a) case-control: ijklm = µ + TG i (T+S(G) F j(i) ) ikl + F kl y ijklm + R m + = µ T i + (G (T G i F ) ikl +S(G) +(R j(i) F + ) klm F+ kl + ijklm R m + (a) case-control: Fixed/Random F F F/R Fixed/Random F F/R F/R IX F F IX F/R F/R MX F R: N(0, F/R y ijklm = µ + G i +S(G) j(i) + kl + R m + (G F ) ikl +(R klm + ) (T S) IX JX KL ij =0 (T F ) ikl =0 (R F ) ijklm =0 y ijklm = µ + G Xi +S(G) j(i) MX + F kl + R ect: F; Run: F T i =0 S j =0 F kl =0 Rm =0 i=0 m + (G F ) i=0 m=1 ikl +(R F ) klm + iid JX KL (b) time course: X KL i=0 j=0 k,l=1 (b) time course: X m=1 (T S) Fixed/Random F F F/R F F/R ij =0 (T F ) ikl =0 (R F ) F/R R: N(0, klm =0 Fixed/Random F F F/R F ) j=0 F/RT i (T F ) k,l=1 ikl F k,l=1 F/R Reference yeast: measurement y Healthy Disease (b) time ijklm = µ + T course: i (T+ SF j ) ikl + F kl y ijklm Endogenous + R m + (T = S) µ ij + (T T i F ) ikl + +(RS j F + ) klm F+ kl + ijklm R m + (T iid ect: R;Run: F (b) time course: 3,800 N(0, iid Idp Timepoint S ) Peptide 1 1 N(0, Light TS ) run 1 run T1 T T3 T4 T5 T6 T7 T8 T9 T10 Fixed/Random F F F/R Fixed/Random F F/R F/R T7F T8 T9 T10 F F/R F/R F R: N(0, F/R y ijklm = µ + T i + S j + kl + R 6.56 m + (T S) ij (T F ) ikl +(R klm + ) iid ect: F; Run: R y ijklm = µ + T i + S j ijklm N(0, + F iid R ) kl + R m + (T S) ij + (T F ) ikl N(0, +(R F ) RF ) klm e5 Intensity, cps Idp Peptide 1 Heavy 6.56 Timepoint Time, min IX run 1.6e5 Intensity, cps Idp Peptide 1 Heavy JX T i (T F ) ikl 6.55 iid N(0, S ) T i (T F ) ikl log (intensity) T Time, min i (T F ) ikl KL X Intensity, cps Time, min Fixed/Random Fixed/Random F F F F/R F F F/R F/R F F/R F/R F/R F F/R F R: N(0, F/R iid T1 T T3 T4 T5 ect: R;Run: R N(0, S ) iid N(0, iid R ) N(0, iid TS ) N(0, RF ) MX iid N(0, R ) T6 run 1.3e5 Intensity, cps Idp Peptide (G F ) ikl =0 i=0 KL X (G F ) ikl =0 k,l=1 log (intensity) Time, min T i (T F ) ikl T1 T T3 H L H L (H) reference (L) endogenous (R F ) klm =0 m=1 KL X (R F ) klm =0 k,l=1 iid N(0, RF ) y ijklm = µ + T i + S j + F kl + R m + (T S) ij + (T F ) ikl +(R F ) klm + ijklm C.-Y. Chang et al. MCP, 01 T i (T F ) ikl T i (T F ) ikl Systematic deviations from the mean T i (T F ) ikl T i (T F ) ikl T (T F ) T1 T T3 19 (H) reference (L) endogenous ) iid N + N
20 STATISTICAL PROTEOMICS AND BIOINFORMATICS Purdue University Kyle Bemis Meena Choi Tim Clough Veavi Chang Robert Ness Danni Yu Cheng Zheng s ETH Zürich Ruedi Aebersold Ruth Hüttenhain and lab Thaminy SilviaSurinova Surinova ga VitekSafia Robert Ness Silvia Purdue Graham Cooks and lab Stanford Mark Stolowtiz CGR, Spain Parag Mallick NSF-SI-SSE Eduard Sabidó NSF-BIO-DBI 36
Proteome-wide label-free quantification with MaxQuant. Jürgen Cox Max Planck Institute of Biochemistry July 2011
Proteome-wide label-free quantification with MaxQuant Jürgen Cox Max Planck Institute of Biochemistry July 2011 MaxQuant MaxQuant Feature detection Data acquisition Initial Andromeda search Statistics
More informationProtein Quantitation II: Multiple Reaction Monitoring. Kelly Ruggles New York University
Protein Quantitation II: Multiple Reaction Monitoring Kelly Ruggles kelly@fenyolab.org New York University Traditional Affinity-based proteomics Use antibodies to quantify proteins Western Blot RPPA Immunohistochemistry
More informationQuantitative Proteomics
Quantitative Proteomics Quantitation AND Mass Spectrometry Condition A Condition B Identify and quantify differently expressed proteins resulting from a change in the environment (stimulus, disease) Lyse
More informationChemical Labeling Strategy for Generation of Internal Standards for Targeted Quantitative Proteomics
Chemical Labeling Strategy for Generation of Internal Standards for Targeted Quantitative Proteomics mtraq Reagents Triplex Christie Hunter, Brian Williamson, Marjorie Minkoff AB SCIEX, USA The utility
More informationProtein Quantitation II: Multiple Reaction Monitoring. Kelly Ruggles New York University
Protein Quantitation II: Multiple Reaction Monitoring Kelly Ruggles kelly@fenyolab.org New York University Traditional Affinity-based proteomics Use antibodies to quantify proteins Western Blot Immunohistochemistry
More informationDeveloping Algorithms for the Determination of Relative Abundances of Peptides from LC/MS Data
Developing Algorithms for the Determination of Relative Abundances of Peptides from LC/MS Data RIPS Team Jake Marcus (Project Manager) Anne Eaton Melanie Kanter Aru Ray Faculty Mentors Shawn Cokus Matteo
More informationWorkflow concept. Data goes through the workflow. A Node contains an operation An edge represents data flow The results are brought together in tables
PROTEOME DISCOVERER Workflow concept Data goes through the workflow Spectra Peptides Quantitation A Node contains an operation An edge represents data flow The results are brought together in tables Protein
More informationOverview - MS Proteomics in One Slide. MS masses of peptides. MS/MS fragments of a peptide. Results! Match to sequence database
Overview - MS Proteomics in One Slide Obtain protein Digest into peptides Acquire spectra in mass spectrometer MS masses of peptides MS/MS fragments of a peptide Results! Match to sequence database 2 But
More informationStatistical analysis of isobaric-labeled mass spectrometry data
Statistical analysis of isobaric-labeled mass spectrometry data Farhad Shakeri July 3, 2018 Core Unit for Bioinformatics Analyses Institute for Genomic Statistics and Bioinformatics University Hospital
More informationAplicació de la proteòmica a la cerca de Biomarcadors proteics Barcelona, 08 de Juny 2010
Aplicació de la proteòmica a la cerca de Biomarcadors proteics Barcelona, 8 de Juny 21 Eliandre de Oliveira Plataforma de Proteòmica Parc Científic de Barcelona Protein Chemistry Proteomics Hypothesis-free
More informationBIOINF 4120 Bioinformatics 2 - Structures and Systems - Oliver Kohlbacher Summer Systems Biology Exp. Methods
BIOINF 4120 Bioinformatics 2 - Structures and Systems - Oliver Kohlbacher Summer 2013 14. Systems Biology Exp. Methods Overview Transcriptomics Basics of microarrays Comparative analysis Interactomics:
More informationSeqAn and OpenMS Integration Workshop. Temesgen Dadi, Julianus Pfeuffer, Alexander Fillbrunn The Center for Integrative Bioinformatics (CIBI)
SeqAn and OpenMS Integration Workshop Temesgen Dadi, Julianus Pfeuffer, Alexander Fillbrunn The Center for Integrative Bioinformatics (CIBI) Mass-spectrometry data analysis in KNIME Julianus Pfeuffer,
More informationYifei Bao. Beatrix. Manor Askenazi
Detection and Correction of Interference in MS1 Quantitation of Peptides Using their Isotope Distributions Yifei Bao Department of Computer Science Stevens Institute of Technology Beatrix Ueberheide Department
More informationKey questions of proteomics. Bioinformatics 2. Proteomics. Foundation of proteomics. What proteins are there? Protein digestion
s s Key questions of proteomics What proteins are there? Bioinformatics 2 Lecture 2 roteomics How much is there of each of the proteins? - Absolute quantitation - Stoichiometry What (modification/splice)
More informationAnalysis of Labeled and Non-Labeled Proteomic Data Using Progenesis QI for Proteomics
Analysis of Labeled and Non-Labeled Proteomic Data Using Progenesis QI for Proteomics Lee Gethings, Gushinder Atwal, Martin Palmer, Chris Hughes, Hans Vissers, and James Langridge Waters Corporation, Wilmslow,
More informationWorkshop: SILAC and Alternative Labeling Strategies in Quantitative Proteomics
Workshop: SILAC and Alternative Labeling Strategies in Quantitative Proteomics SILAC and Stable Isotope Dimethyl-Labeling Approaches in Quantitative Proteomics Ho-Tak Lau, Hyong-Won Suh, Shao-En Ong UW
More informationDesigned for Accuracy. Innovation with Integrity. High resolution quantitative proteomics LC-MS
Designed for Accuracy High resolution quantitative proteomics Innovation with Integrity LC-MS Setting New Standards in Accuracy The development of mass spectrometry based proteomics approaches has dramatically
More informationMS Based Proteomics: Recent Case Studies Using Advanced Instrumentation
MS Based Proteomics: Recent Case Studies Using Advanced Instrumentation Chris Adams, PH.D. Stanford University Mass Spectrometry http://mass-spec.stanford.edu/ For personal use only. Please do not reuse
More informationUCD Conway Institute of Biomolecular & Biomedical Research Graduate Education 2009/2010
EMERGING PROTEOMIC TECHNOLOGIES - MODULE SCHEDULE & OUTLINE 2010 Course Organiser: Dr. Giuliano Elia Module Co-ordinator: Dr Giuliano Elia Credits: 5 Date & Time Session & Topic Coordinator 14th April
More informationProteomics. Yeast two hybrid. Proteomics - PAGE techniques. Data obtained. What is it?
Proteomics What is it? Reveal protein interactions Protein profiling in a sample Yeast two hybrid screening High throughput 2D PAGE Automatic analysis of 2D Page Yeast two hybrid Use two mating strains
More informationBioinformatics 2. Yeast two hybrid. Proteomics. Proteomics
GENOME Bioinformatics 2 Proteomics protein-gene PROTEOME protein-protein METABOLISM Slide from http://www.nd.edu/~networks/ Citrate Cycle Bio-chemical reactions What is it? Proteomics Reveal protein Protein
More informationData pre-processing in liquid chromatography mass spectrometry-based proteomics
BIOINFORMATICS ORIGINAL PAPER Vol. 21 no. 21 25, pages 454 459 doi:1.193/bioinformatics/bti66 Data and text mining Data pre-processing in liquid chromatography mass spectrometry-based proteomics Xiang
More informationIntroduction into Selected Reaction Monitoring (SRM) Christina Ludwig
Introduction into Selected Reaction Monitoring (SRM) Christina Ludwig EuPA Bioinformatics course 28.11.2013 Overview A) What is selected reac/on monitoring, how does it work and why is it useful? B) How
More informationStatistical Issues in Preprocessing Quantitative Bottom-Up LCMS Proteomics Data
Statistical Issues in Preprocessing Quantitative Bottom-Up LCMS Proteomics Data Jean-Eudes DAZARD xd101@case.edu Case Western Reserve University Center for Proteomics and Bioinformatics November 05, 2009
More informationIntroduction to Bioinformatics. Shifra Ben-Dor Irit Orr
Introduction to Bioinformatics Shifra Ben-Dor Irit Orr Lecture Outline: Technical Course Items Introduction to Bioinformatics Introduction to Databases This week and next week What is bioinformatics? A
More informationModeling Mass Spectrometry-Based Protein Analysis
Chapter 8 Jan Eriksson and David Fenyö Abstract The success of mass spectrometry based proteomics depends on efficient methods for data analysis. These methods require a detailed understanding of the information
More informationGiri Narasimhan. CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools. Evaluation. Course Homepage.
CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools Giri Narasimhan ECS 389; Phone: x3748 giri@cis.fiu.edu www.cis.fiu.edu/~giri/teach/bioinfs06.html 1/12/06 CAP5510/CGS5166 1 Evaluation
More informationLesson 11. Functional Genomics I: Microarray Analysis
Lesson 11 Functional Genomics I: Microarray Analysis Transcription of DNA and translation of RNA vary with biological conditions 3 kinds of microarray platforms Spotted Array - 2 color - Pat Brown (Stanford)
More informationWelcome to BIOL 572: Recombinant DNA techniques
Lecture 1: 1 Welcome to BIOL 572: Recombinant DNA techniques Agenda 1: Introduce yourselves Agenda 2: Course introduction Agenda 3: Some logistics for BIOL 572 Agenda 4: Q&A section Agenda 1: Introduce
More informationiprophet: Multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates
MCP Papers in Press. Published on August 29, 2011 as Manuscript M111.007690 This is the Pre-Published Version iprophet: Multi-level integrative analysis of shotgun proteomic data improves peptide and protein
More informationStatistical Designs for 3D-tissue Microarrays
Statistical Designs for 3D-tissue Microarrays Michael J Phelan Biostatistics Shared Resource Chao Family Comprehensive Cancer Center UC, Irvine School of Medicine October 9, 2012 MJ Phelan (UCIrvine) Designs
More informationProteomics. Areas of Interest
Introduction to BioMEMS & Medical Microdevices Proteomics and Protein Microarrays Companion lecture to the textbook: Fundamentals of BioMEMS and Medical Microdevices, by Prof., http://saliterman.umn.edu/
More informationIsotopic-Labeling and Mass Spectrometry-Based Quantitative Proteomics
Isotopic-Labeling and Mass Spectrometry-Based Quantitative Proteomics Xiao-jun Li, Ph.D. Current address: Homestead Clinical Day 4 October 19, 2006 Protein Quantification LC-MS/MS Data XLink mzxml file
More informationMapping connections between the genome, ionome and the physical landscape. Photo by Bruce Bohm. David E Salt Purdue University, USA
Mapping connections between the genome, ionome and the physical landscape Photo by Bruce Bohm David E Salt Purdue University, USA What is the Ionome Environment Transcriptome Proteome Ionome The elemental
More informationProteomics: the first decade and beyond. (2003) Patterson and Aebersold Nat Genet 33 Suppl: from
Advances in mass spectrometry and the generation of large quantities of nucleotide sequence information, combined with computational algorithms that could correlate the two, led to the emergence of proteomics
More informationRelated Courses He who asks is a fool for five minutes, but he who does not ask remains a fool forever.
CSE 527 Computational Biology http://www.cs.washington.edu/527 Lecture 1: Overview & Bio Review Autumn 2004 Larry Ruzzo Related Courses He who asks is a fool for five minutes, but he who does not ask remains
More informationnetworks in molecular biology Wolfgang Huber
networks in molecular biology Wolfgang Huber networks in molecular biology Regulatory networks: components = gene products interactions = regulation of transcription, translation, phosphorylation... Metabolic
More informationCalibration in Proteomics. Proteomics 202 :: Practical Proteomics Using the Skyline Software Ecosystem Lindsay K. Pino Monday, Jan 22
Calibration in Proteomics Proteomics 202 :: Practical Proteomics Using the Skyline Software Ecosystem Lindsay K. Pino (lpino@uw.edu) Monday, Jan 22 objectives Define calibration for proteomics List common
More informationProcedure to Create NCBI KOGS
Procedure to Create NCBI KOGS full details in: Tatusov et al (2003) BMC Bioinformatics 4:41. 1. Detect and mask typical repetitive domains Reason: masking prevents spurious lumping of non-orthologs based
More informationMass Spectrometry and Proteomics - Lecture 5 - Matthias Trost Newcastle University
Mass Spectrometry and Proteomics - Lecture 5 - Matthias Trost Newcastle University matthias.trost@ncl.ac.uk Previously Proteomics Sample prep 144 Lecture 5 Quantitation techniques Search Algorithms Proteomics
More informationMS-based proteomics to investigate proteins and their modifications
MS-based proteomics to investigate proteins and their modifications Francis Impens VIB Proteomics Core October th 217 Overview Mass spectrometry-based proteomics: general workflow Identification of protein
More informationDiscovering molecular pathways from protein interaction and ge
Discovering molecular pathways from protein interaction and gene expression data 9-4-2008 Aim To have a mechanism for inferring pathways from gene expression and protein interaction data. Motivation Why
More informationStatistical approach to protein quantification
MCP Papers in Press. Published on November 19, 2013 as Manuscript M112.025445 Statistical approach to protein quantification Authors and affilitations Sarah Gerster a,g, Taejoon Kwon b, Christina Ludwig
More informationMass spectrometry in proteomics
I519 Introduction to Bioinformatics, Fall, 2013 Mass spectrometry in proteomics Haixu Tang School of Informatics and Computing Indiana University, Bloomington Modified from: www.bioalgorithms.info Outline
More informationProtein Identification Using Tandem Mass Spectrometry. Nathan Edwards Informatics Research Applied Biosystems
Protein Identification Using Tandem Mass Spectrometry Nathan Edwards Informatics Research Applied Biosystems Outline Proteomics context Tandem mass spectrometry Peptide fragmentation Peptide identification
More informationNPTEL VIDEO COURSE PROTEOMICS PROF. SANJEEVA SRIVASTAVA
LECTURE-25 Quantitative proteomics: itraq and TMT TRANSCRIPT Welcome to the proteomics course. Today we will talk about quantitative proteomics and discuss about itraq and TMT techniques. The quantitative
More informationQuantitative Proteomics
BSPR workshop 16 th July 2010 Quantitative Proteomics Kathryn Lilley Cambridge Centre for Proteomics Department of Biochemistry University of Cambridge k.s.lilley@bioc.cam.ac.uk www.bio.cam.ac.uk/proteomics/
More informationTargeted Proteomics Environment
Targeted Proteomics Environment Quantitative Proteomics with Bruker Q-TOF Instruments and Skyline Brendan MacLean Quantitative Proteomics Spectrum-based Spectral counting Isobaric tags Chromatography-based
More informationBioconductor Project Working Papers
Bioconductor Project Working Papers Bioconductor Project Year 2004 Paper 6 Error models for microarray intensities Wolfgang Huber Anja von Heydebreck Martin Vingron Department of Molecular Genome Analysis,
More informationComputational Genomics. Reconstructing dynamic regulatory networks in multiple species
02-710 Computational Genomics Reconstructing dynamic regulatory networks in multiple species Methods for reconstructing networks in cells CRH1 SLT2 SLR3 YPS3 YPS1 Amit et al Science 2009 Pe er et al Recomb
More informationDe novo Protein Sequencing by Combining Top-Down and Bottom-Up Tandem Mass Spectra. Xiaowen Liu
De novo Protein Sequencing by Combining Top-Down and Bottom-Up Tandem Mass Spectra Xiaowen Liu Department of BioHealth Informatics, Department of Computer and Information Sciences, Indiana University-Purdue
More informationQuantitation of a target protein in crude samples using targeted peptide quantification by Mass Spectrometry
Quantitation of a target protein in crude samples using targeted peptide quantification by Mass Spectrometry Jon Hao, Rong Ye, and Mason Tao Poochon Scientific, Frederick, Maryland 21701 Abstract Background:
More informationModule Based Neural Networks for Modeling Gene Regulatory Networks
Module Based Neural Networks for Modeling Gene Regulatory Networks Paresh Chandra Barman, Std 1 ID: 20044523 Term Project: BiS732 Bio-Network Department of BioSystems, Korea Advanced Institute of Science
More informationTargeted protein quantification
Targeted Quantitative Proteomics Targeted protein quantification with high-resolution, accurate-mass MS Highly selective Very sensitive Complex samples HR/AM A more complete quantitative proteomics picture
More informationSILAC and TMT. IDeA National Resource for Proteomics Workshop for Graduate Students and Post-docs Renny Lan 5/18/2017
SILAC and TMT IDeA National Resource for Proteomics Workshop for Graduate Students and Post-docs Renny Lan 5/18/2017 UHPLC peak chosen at 26.47 min LC Mass at 571.36 chosen for MS/MS MS/MS MS This is a
More informationMixed-effect model analysis of ISTA GMO Proficiency Tests
Mixed-effect model analysis of ISTA GMO Proficiency Tests ISTA GMO TF ISTA Statistics Committee Jean-Louis Laffont Outline PT-Round Species Event spiking levels #samples PT01 PT0 PT03 PT04 PT05 PT06 PT07
More informationGenome wide analysis of protein and mrna half lives reveals dynamic properties of mammalian gene expression
Genome wide analysis of protein and mrna half lives reveals dynamic properties of mammalian gene expression Matthias Selbach Cell Signaling and Mass Spectrometry Max Delbrück Center for Molecular Medicine
More informationA Software Suite for the Generation and Comparison of Peptide Arrays from Sets. of Data Collected by Liquid Chromatography-Mass Spectrometry
MCP Papers in Press. Published on July 26, 2005 as Manuscript M500141-MCP200 A Software Suite for the Generation and Comparison of Peptide Arrays from Sets of Data Collected by Liquid Chromatography-Mass
More informationPRIDE Cluster: building the consensus of proteomics data
Supplementary Materials PRIDE Cluster: building the consensus of proteomics data Johannes Griss, Joseph Michael Foster, Henning Hermjakob and Juan Antonio Vizcaíno EMBL-European Bioinformatics Institute,
More informationGS Analysis of Microarray Data
GS01 0163 Analysis of Microarray Data Keith Baggerly and Bradley Broom Department of Bioinformatics and Computational Biology UT M. D. Anderson Cancer Center kabagg@mdanderson.org bmbroom@mdanderson.org
More informationTowards the Prediction of Protein Abundance from Tandem Mass Spectrometry Data
Towards the Prediction of Protein Abundance from Tandem Mass Spectrometry Data Anthony J Bonner Han Liu Abstract This paper addresses a central problem of Proteomics: estimating the amounts of each of
More informationComprehensive support for quantitation
Comprehensive support for quantitation One of the major new features in the current release of Mascot is support for quantitation. This is still work in progress. Our goal is to support all of the popular
More informationA Quadrupole-Orbitrap Hybrid Mass Spectrometer Offers Highest Benchtop Performance for In-Depth Analysis of Complex Proteomes
A Quadrupole-Orbitrap Hybrid Mass Spectrometer Offers Highest Benchtop Performance for In-Depth Analysis of Complex Proteomes Zhiqi Hao 1, Yi Zhang 1, Shannon Eliuk 1, Justin Blethrow 1, Dave Horn 1, Vlad
More informationGS Analysis of Microarray Data
GS01 0163 Analysis of Microarray Data Keith Baggerly and Kevin Coombes Section of Bioinformatics Department of Biostatistics and Applied Mathematics UT M. D. Anderson Cancer Center kabagg@mdanderson.org
More informationGS Analysis of Microarray Data
GS01 0163 Analysis of Microarray Data Keith Baggerly and Kevin Coombes Section of Bioinformatics Department of Biostatistics and Applied Mathematics UT M. D. Anderson Cancer Center kabagg@mdanderson.org
More informationAdvances in quantitative proteomics using stable isotope tags
Advances in quantitative proteomics using stable isotope tags Mark R. Flory, Timothy J. Griffin, Daniel Martin and Ruedi Aebersold A great deal of current biological and clinical research is directed at
More informationSmall RNA in rice genome
Vol. 45 No. 5 SCIENCE IN CHINA (Series C) October 2002 Small RNA in rice genome WANG Kai ( 1, ZHU Xiaopeng ( 2, ZHONG Lan ( 1,3 & CHEN Runsheng ( 1,2 1. Beijing Genomics Institute/Center of Genomics and
More informationQuantitative analysis of the proteome
BMG 744 Proteomics-Mass Spectrometry Quantitative analysis of the proteome Stephen Barnes, PhD sbarnes@uab.edu 1 The ASMS 1996-2007 www.tagate.com/western/wild_west.jpg 2 1 Proteomics Data Standards 2005
More informationTUTORIAL EXERCISES WITH ANSWERS
TUTORIAL EXERCISES WITH ANSWERS Tutorial 1 Settings 1. What is the exact monoisotopic mass difference for peptides carrying a 13 C (and NO additional 15 N) labelled C-terminal lysine residue? a. 6.020129
More informationCGS 5991 (2 Credits) Bioinformatics Tools
CAP 5991 (3 Credits) Introduction to Bioinformatics CGS 5991 (2 Credits) Bioinformatics Tools Giri Narasimhan 8/26/03 CAP/CGS 5991: Lecture 1 1 Course Schedules CAP 5991 (3 credit) will meet every Tue
More informationHigh-Field Orbitrap Creating new possibilities
Thermo Scientific Orbitrap Elite Hybrid Mass Spectrometer High-Field Orbitrap Creating new possibilities Ultrahigh resolution Faster scanning Higher sensitivity Complementary fragmentation The highest
More information6 x 5 Ways to Ensure Your LC-MS/MS is Healthy
6 x 5 Ways to Ensure Your LC-MS/MS is Healthy (Also known as - Tracking Performance with the 6 x 5 LC-MS/MS Peptide Reference Mixture) Mike Rosenblatt, Ph.D. Group Leader Mass Spec Reagents 215. We monitor
More informationPredicting Protein Functions and Domain Interactions from Protein Interactions
Predicting Protein Functions and Domain Interactions from Protein Interactions Fengzhu Sun, PhD Center for Computational and Experimental Genomics University of Southern California Outline High-throughput
More informationProtocol. Product Use & Liability. Contact us: InfoLine: Order per fax: www:
Protocol SpikeTides Set TAA - light SpikeTides Set TAA_L - heavy Peptide Sets for relative quantification of Tumor Associated Antigens (TAAs) in SRM and MRM Assays Contact us: InfoLine: +49-30-6392-7878
More informationTranslational Biomarker Core
Translational Biomarker Core Instrumentation Thermo Scientific TSQ Quantum Triple Quadrupole Mass Spectrometers. There are two TSQ Quantum Ultra AM instruments available in the TBC. The TSQ Quantum Ultra
More informationLow-Level Analysis of High- Density Oligonucleotide Microarray Data
Low-Level Analysis of High- Density Oligonucleotide Microarray Data Ben Bolstad http://www.stat.berkeley.edu/~bolstad Biostatistics, University of California, Berkeley UC Berkeley Feb 23, 2004 Outline
More informationThe Role of Network Science in Biology and Medicine. Tiffany J. Callahan Computational Bioscience Program Hunter/Kahn Labs
The Role of Network Science in Biology and Medicine Tiffany J. Callahan Computational Bioscience Program Hunter/Kahn Labs Network Analysis Working Group 09.28.2017 Network-Enabled Wisdom (NEW) empirically
More informationPeptide Targeted Quantification By High Resolution Mass Spectrometry A Paradigm Shift? Zhiqi Hao Thermo Fisher Scientific San Jose, CA
Peptide Targeted Quantification By High Resolution Mass Spectrometry A Paradigm Shift? Zhiqi Hao Thermo Fisher Scientific San Jose, CA Proteomics is Turning Quantitative Hmmm.. Which ones are my targets?
More informationNested Effects Models at Work
21/06/2010 Nested ffects Models at Work Tutorial Session: Network Modelling in Systems Biology with R Prof. Dr. Holger Fröhlich Algorithmic Bioinformatics Bonn-Aachen International Center for Information
More informationProtein function studies: history, current status and future trends
19 3 2007 6 Chinese Bulletin of Life Sciences Vol. 19, No. 3 Jun., 2007 1004-0374(2007)03-0294-07 ( 100871) Q51A Protein function studies: history, current status and future trends MA Jing, GE Xi, CHANG
More informationThe Agilent 6495 Triple Quadrupole LC/MS: Peptide Quantitation Performance
The Agilent 495 Triple Quadrupole LC/MS: Peptide Quantitation Performance Technical Overview Introduction Sample complexity and the low concentration of certain biomarkers are the major challenges encountered
More informationReagents. Affinity Tag (Biotin) Acid Cleavage Site. Figure 1. Cleavable ICAT Reagent Structure.
DATA SHEET Protein Expression Analysis Reagents Background The ultimate goal of proteomics is to identify and quantify proteins that are relevant to a given biological state; and to unearth networks of
More informationMixture models for analysing transcriptome and ChIP-chip data
Mixture models for analysing transcriptome and ChIP-chip data Marie-Laure Martin-Magniette French National Institute for agricultural research (INRA) Unit of Applied Mathematics and Informatics at AgroParisTech,
More informationBayesian Clustering of Multi-Omics
Bayesian Clustering of Multi-Omics for Cardiovascular Diseases Nils Strelow 22./23.01.2019 Final Presentation Trends in Bioinformatics WS18/19 Recap Intermediate presentation Precision Medicine Multi-Omics
More informationApplications of Mass Spectrometry for Biotherapeutic Characterization
Applications of Mass Spectrometry for Biotherapeutic Characterization Case Studies of Disulfide Characterization and Separation free Modes of Analysis Steven L. Cockrill Amgen Colorado Analytical Sciences
More informationComputational Methods for Mass Spectrometry Proteomics
Computational Methods for Mass Spectrometry Proteomics Eidhammer, Ingvar ISBN-13: 9780470512975 Table of Contents Preface. Acknowledgements. 1 Protein, Proteome, and Proteomics. 1.1 Primary goals for studying
More informationComputational methods for predicting protein-protein interactions
Computational methods for predicting protein-protein interactions Tomi Peltola T-61.6070 Special course in bioinformatics I 3.4.2008 Outline Biological background Protein-protein interactions Computational
More informationMass spectrometry-based proteomics has become
FOCUS: THE ORBITRAP Computational Principles of Determining and Improving Mass Precision and Accuracy for Proteome Measurements in an Orbitrap Jürgen Cox and Matthias Mann Proteomics and Signal Transduction,
More informationStatistics for Differential Expression in Sequencing Studies. Naomi Altman
Statistics for Differential Expression in Sequencing Studies Naomi Altman naomi@stat.psu.edu Outline Preliminaries what you need to do before the DE analysis Stat Background what you need to know to understand
More informationTANDEM MASS SPECTRAL LIBRARIES OF PEPTIDES AND THEIR ROLES IN PROTEOMICS RESEARCH
TANDEM MASS SPECTRAL LIBRARIES OF PEPTIDES AND THEIR ROLES IN PROTEOMICS RESEARCH Wenguang Shao 1,2 and Henry Lam 2,3 * 1 Department of Biology, Institute of Molecular Systems Biology, Eidgen ossische
More informationGraph Alignment and Biological Networks
Graph Alignment and Biological Networks Johannes Berg http://www.uni-koeln.de/ berg Institute for Theoretical Physics University of Cologne Germany p.1/12 Networks in molecular biology New large-scale
More informationIncreased Selectivity, Analytical Precision, and Throughput in. Targeted Proteomics
MCP Papers in Press. Published on July 27, 2010 as Manuscript M110.002931 Manuscript MCP isrm Revised Pg. 1/21 Increased Selectivity, Analytical Precision, and Throughput in Targeted Proteomics Reiko Kiyonami
More informationSBI lab Jou-hyun Jeon Jae-seong Yang Solip Park Yonghwan Choi Yoonsup Choi Jinho Kim HyunJun Nam JiHye Hwang Inhae Kim Youngeun Shin Sung gyu Han
POSTECH 생명과학과김상욱 Structural Bioinformatics Laboratory Pohang University of Science and Technology Acknowledgement SBI lab Jou-hyun Jeon Jae-seong Yang Solip Park Yonghwan Choi Yoonsup Choi Jinho Kim HyunJun
More informationSkyline Small Molecule Targets
Skyline Small Molecule Targets The Skyline Targeted Proteomics Environment provides informative visual displays of the raw mass spectrometer data you import into your Skyline documents. Originally developed
More informationProtein inference based on peptides identified from. tandem mass spectra
Protein inference based on peptides identified from tandem mass spectra A Thesis Submitted to the College of Graduate Studies and Research in Partial Fulfillment of the Requirements for the degree of Doctor
More informationIntroduction to Mass Spectrometry and Proteomics
Introduction to Mass Spectrometry and Proteomics Annie Moradian Proteome Exploration Laboratory Contents 1. Basic Principles of Mass Spectrometry Ionization Sources Mass Analyzers Detectors 2. Tandem Mass
More informationIn shotgun proteomics, a complex protein mixture derived from a biological sample is directly analyzed. Research Article
JOURNAL OF COMPUTATIONAL BIOLOGY Volume 16, Number 8, 2009 # Mary Ann Liebert, Inc. Pp. 1 11 DOI: 10.1089/cmb.2009.0018 Research Article A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics
More informationPyrobayes: an improved base caller for SNP discovery in pyrosequences
Pyrobayes: an improved base caller for SNP discovery in pyrosequences Aaron R Quinlan, Donald A Stewart, Michael P Strömberg & Gábor T Marth Supplementary figures and text: Supplementary Figure 1. The
More informationGenome Evolution Greg Lang, Department of Biological Sciences
Genome Evolution Greg Lang, Department of Biological Sciences BioS 010: Bioscience in the 21st Century Mechanisms of genome evolution Gene Duplication Genome Rearrangement Whole Genome Duplication Gene
More informationStatistics Applied to Bioinformatics. Tests of homogeneity
Statistics Applied to Bioinformatics Tests of homogeneity Two-tailed test of homogeneity Two-tailed test H 0 :m = m Principle of the test Estimate the difference between m and m Compare this estimation
More information