1 Tutorial for Proteomics Data Submission Katalin F. Medzihradszky Robert J. Chalkley UCSF
2 Why Have Guidelines? Large-scale proteomics studies create huge amounts of data. It is impossible/impractical to present all the results. The function of the guidelines is to: Provide enough information to be able to explain the experiment. Provide an assessment of the reliability of the results. Provide the data that supports the results, particularly those that have the greatest potential for mis-interpretation, so the readers can manually assess the results that are important to them. The Problem Proteomics experiments are carried out by many different methods, using a variety of instrument types and employing different analysis tools. Hence, many experimental and analysis parameters need to be reported and the parameters required will differ depending on how the experiment was performed and analyzed.
3 Paris Guidelines The present publication guidelines were created at a meeting in Paris in May 2005 by about 30 key people within the proteomics community, including: Academic Researchers Instrument Manufacturers Search Engine Designers Journal Representatives The guidelines are published 1 and are on the Molecular and Cellular Proteomics (MCP) website ( MCP is using these guidelines for reviewing germane submissions. Other proteomics journals use different guidelines for a variety of reasons. A large percentage of manuscripts submitted to MCP are initially not compliant with the guidelines. 1 Bradshaw RA, Burlingame AL, Carr S and Aebersold R. Mol Cell Proteomics (2006) 5(5):787-8.
4 How Does MCP Help Authors Make their Papers Acceptable? MCP does try to help/advise authors during the submission and review process on how to make their manuscript compliant. MCP has created a checklist based on the Paris guidelines that makes it easier for authors to tell whether they are compliant. The journal has created this tutorial to try to explain why certain pieces of information are required and give examples. The journal intends to host workshop sessions at conferences to present the guidelines to wide audiences, to answer questions and to try to encourage comments on the guidelines. Encourage search engine manufacturers to make it easier to get the required information in the results output.
5 In the following slides the points on the guidelines checklist (in red) will be discussed, point by point, starting with the Experimental section
6 Peak Picking Software In the Experimental Section Name of peaklist-generating software and release version (number or date) The raw data acquired by the mass spectrometer is converted into a centroided peaklist file for database searching. The program that performs this can do many things, such as: Remove peaks that are below a certain intensity or signal to noise ratio; Require peaks to have a certain resolution in order to make the list; Set a threshold for the maximum number of peaks within a mass range; Assign a charge state to ions; Merge together MS/MS spectra that have the same precursor mass.
7 Peak Picking (cont d) In the Experimental Section Parameters used default vs altered Using different peak picking software / parameters will change the subsequent database searching results, so it is necessary to report the software used for peaklist generation and parameters used. Examples of software Extract_msn in Bioworks 3.0 (Thermo) Mascot.dll v1.6b19 (Applied Biosystems) DataAnalysis 3.2 (Bruker) Mascot Distiller v2.1 As many people use default parameters for whatever software they use, by specifying the version of the software it can be sufficient to state that the default parameters were used.
8 Search Engine In the Experimental Section Name of the search engine and release version (number or date) Search engines change over time: An improved scoring system may be implemented. New ways to filter the results may be included. Hence, the version number of the search engine is important.
9 Database Search Parameters In the Experimental Section Enzyme specificity considered # of missed cleavages permitted Fixed modification(s) (including residue specificity) Variable modification(s) (including residue specificity) Mass tolerance for precursor ions Mass tolerance for fragment ions Search engines work by: 1. Determining a list of potential peptides from a database that can be formed by the specified parameters and have the correct precursor mass. 2. Determining scores for the matches of the fragment peaks to each of these peptides. Changing search parameters will change the number of the potential hits. For software using probabilities or Expectation values (E-values) for scoring, it will change the scores.
10 Search Parameters - Database In the Experimental Section Name of database searched and release version/date Entries in a database change over time: New entries are submitted. Old entries may be removed. Multiple entries may be merged into one new entry. Database examples, NCBI nr ; sequences MSDB database updated May 15, 2005, sequences IPI human database version 3.16, entries (Inclusion of entry numbers is discussed on the next slide).
11 Search Parameters - Database (cont d) In the Experimental Section Species restriction and justification for searching only a subset of a database Number of protein entries in the database actually searched The number of entries in the database that are searched influences the reliability of the results. Probability / E-value scores are calculated on the basis that all the possible identifications for peptides are in the database, and that matching any peptide in the database is equally likely. If a database is searched that contains sequences from proteins that are not possible to be in the sample, then the confidence of matches are going to be lowered. If the database is too small, and so does not contain some possible answers (e.g. human keratin), the search engine will return an over-confident assessment of a match. Probability/ E-value results for searches against a small database can be very inaccurate and unreliable.
12 In the Experimental Section Search Parameters (cont d) Threshold score/e-value for accepting individual MS/MS spectra Justification of the threshold employed For large datasets estimation of false positive rate and how this was calculated. All spectra will have a top match, but not all are correct. A threshold has to be defined, below which results are discarded. This cut-off score has to be statistically justified. e.g. Mascot uses a threshold of 5% probability that a protein identification is incorrect as a default. This threshold is a (probability) score and should be stated in the manuscript. Performing searches using a combination of normal and reversed (or random) databases is an effective way of estimating the number of incorrect peptide and protein assignments in the dataset as a whole when employing a given score threshold. However, this requires many spectra to be accurate and, therefore, is only meaningful on large datasets. Referring to a paper where the authors used the same cut-off values cannot be considered as justification, as most scoring systems are dataset and search parameter dependent.
13 If PTMs are being reported Software/method used to evaluate site assignment In the Experimental Section Search engines are much more reliable at peptide identification than modification site assignment. Search engines are optimized for peptide identification. Optimum parameters for site assignment are different to those for peptide identification. For example, more peaks are often required for site assignment than peptide identification. When a search engine does not have evidence to assign the site, it guesses ; i.e. it assigns a site that is consistent with the data even if there is no specific evidence for the site assignment. It is difficult to tell which assignments are confirmed by the data and which are one of multiple possibilities based on the data. Personal inspection may often be the most reliable tool. Hence, the journal requires annotated spectra for all peptides where a modification site is being reported.
14 Peptide Mass Fingerprint Data In the Experimental Section Name of software used for peak-picking and its release version The software used for converting the raw data into a peak list must be reported. Example software: flexanalysis (version 2.0) for a Bruker MALDI-TOF. Different versions of the same software may use different default parameters, so the release version is also important.
15 PMF Peak Picking In the Experimental Section Parameters and thresholds used for peak-picking; e.g. intensity or S/N threshold, resolution, means of calibrating each spectrum, list of excluded contaminant ions and justification In many ways, good peak-picking software and parameters are more important for PMF data than MSMS data, as unmatched peaks are more significant in assessing the reliability of the results. It is common to exclude certain peaks from the list to be submitted for database searching; e.g. trypsin autolysis peaks, matrix cluster ions, masses of tryptic peptides from keratin. If this is done, this needs to be stated.
16 PMF Acceptance Criteria In the Experimental Section The authors must state the threshold used for acceptance of PMF-based identifications. Some software uses probability / E-value based scoring. For these, stating a probability / E-value threshold is sufficient. For software that does not use a statistical score, in general the threshold will probably include a minimum number of peaks matching and a minimum percentage of peaks or a minimum protein sequence coverage. For software that does not use a statistical score, some other evidence in addition to a score must be presented; e.g. the score for the highest non-homologous protein match. A Western blot may also provide this confirmation as well as MS/MS (PSD, CID) analysis of one or more peptides.
17 In the Experimental Section Combining Peptides into Proteins Identified If peptides match to multiple members of a protein family, criteria used for selecting which member to report; i.e. how was the redundancy eliminated/handled (this is an issue for all protein databases). How were isoforms/individual members of a protein family unambiguously identified. Protein databases often contain entries with similar sequences: Proteins from the same species sharing sequence stretches; Equivalent proteins from different species; Multiple entries for one gene product. Hence, often the peptides identified match equally well to multiple database entries. In this situation the authors have two options: Report all the proteins the data supports equally well. Choose to only report one accession, in which case they must justify why they chose one particular entry over others; e.g. they have corollary information such as a Western blot that identifies a particular isoform.
18 Quantitative Studies In the Experimental Section How the quantitation was performed (number of peaks, peak intensity, peak area, extracted ion chromatogram). There are many valid mass spectrometry strategies for relative or absolute protein quantitation, both in terms of the method of quantitation, e.g. Isotopic labeling (chemical or metabolic) Number of peptides identified and the results can be measured in different ways, e.g. Peak intensity Peak area Area of peak from extracted ion chromatogram The strategy employed must be clearly stated.
19 Quantitative Studies (cont d) In the Experimental Section Minimum thresholds required for data to be used for quantitation. Outlier datapoints removed. If so, give justification. Measurements for weak signals are going to have a lower accuracy. There are sometimes other reasons why specific datapoints are excluded (e.g. there was a co-eluting peak with similar m/z). If any data is excluded from the results this must be stated with justification.
20 Quantitative Studies (cont d) In the Experimental Section Explanation of statistics used to assess accuracy and significance of measurements. Quantitation is usually done at the peptide level but reported at the protein level. It is nearly always possible to perform statistical analysis to measure the accuracy / reliability of measurements, e.g. There may be certain protein(s) that are known not to change between samples. Variability in measurements can be determined by assuming all measurements for the same protein should return the same quantitation results at the peptide level. Hence, statistics such as means and standard deviations or coefficients of variation can be determined to set a confidence level for whether a measurement represents a statistically significant change.
21 Quantitative Studies (cont.d) In the Experimental Section Indicate how biological and analytical reproducibility were addressed by experimental design. In order to determine whether a change is biologically significant, either the experiment must be repeated with multiple biological preparations, or the change must be independently confirmed by another means; e.g. a quantitative Western blot.
22 Now that the experiment/s have been adequately described, it is time to present the results
23 Protein Identifications In the Results Section For each protein identified the following should be reported in a table: Accession number When a frequently studied, highly conserved protein is identified there may be a list of accession numbers that match the peptides identified. If only one or a few of this list are reported in the table then this should be indicated and justification for why a particular entry was chosen should be given, e.g. The author had independent evidence that a particular isoform was present. The database entry had the most descriptive protein name The database entry was the best annotated. Do not report accession numbers of protein constructs or hypothetical proteins whenever possible.
24 Protein Identification (cont d) In the Results Section Number of unique (in terms of amino acid sequence) peptides identified % sequence coverage identified from MS/MS data or a list of sequences identified Unique means sequences differing in at least 1 amino acid residue: Covalently modified peptides, including N- or C-terminal elongation (i.e. missed cleavages) count as unique; different charge states or multiple fragmentation spectra of the same peptide do NOT count. Sequence coverage is calculated by dividing the number of amino acids observed by the protein amino acid length (66/330 = 22%) Observing the same peptide (even differently modified) does not increase sequence coverage. Do NOT list sequences whose score were below the threshold you stated you were using to guarantee reliable answers in the experimental section.
25 Example Format for Presenting Protein Identifications Based on Multiple Fragmentation Spectra In the Results Section * * * Accession number Unique peptides Sequence coverage Expectation-value detected % ADA22_MOUSE E-05 Q5SV72_MOUSE E-04 Q9Z0P5_MOUSE E-06 Q7TNV2_MOUSE E-05 ABI1_MOUSE E-05 ABI2_MOUSE E-05 Q921H8_MOUSE E-03 * Required information Additional information, such as a protein s name, function, MW, pi, score, peptide sequences, etc. may be included.
26 Single Peptide Protein IDs and PTMs In the Results Section Create a Table and include: Sequence identified The precursor m/z and charge Score / E-value for this peptide If a score / E-value threshold has been employed at the peptide level, the majority of any incorrect assignments will be to peptides that are the only one assigned to a given protein, i.e. one hit wonders. Search engines are significantly more reliable at identifying peptide sequences than identifying sites of modification. Hence, the journal requires extra information about single-peptidebased protein IDs and PTM assignments.
27 Example Format for Presenting Single- Peptide-Based Protein Identifications In the Results Section * * * * either/or Acc # m/z z error [Da] Peptide Score Expect P FLEQQNQVLQTKWELLQQVDTSTR e-6 P HGVQELEIELQSQLSK e-8 P ELTTEIDNNIEQISSYK e-5 P YICENQDSISSK e-6 P AIGGGLSSVGGGSSTIK e-5 Q GQTGGDVNVEMDAAPGVDLSR e-6 P ALEEANADLEVK P TTPPVLDSDGSFFLYSK e-4 * Required information * Additional information, such as the protein s name, MW, pi, mass measurement error, number of database entries that contain this peptide, i.e. uniqueness, etc. may be included.
28 Single-Peptide-Based Protein IDs and PTMs In the Results Section For each protein or site, a MS/MS spectrum appropriately labeled should be included, with masses detected as well as fragment assignments It is recognized that properly labeled spectra are not always readily produced. Other acceptable options (in order of preference): Two copies of the same spectrum/page one labeled with the masses, the other with fragment assignments; Spectrum labeled with masses, accompanied with a Table of the fragment assignments; Fragment assignments provided by the search engine in the spectrum and corresponding masses highlighted in a Table of theoretical fragments (e.g. Mascot results output). These files can be quite large. If there are large numbers of spectra it is often easier to split them between multiple files. We suggest to not include data on more than 50 proteins in a single file.
29 In the Results Section Example Presentation of Spectrum of a Single-Peptide-Based Protein ID
30 Example Presentation of Spectrum of a Modified Peptide In the Results Section Not required, but recommended.
31 PMF-based Identifications In the Results Section For PMF-based IDs Number of masses matched Number of masses not matched % sequence coverage Criteria for acceptance For PMF data, the number of matched and unmatched peaks are both important for assessing the reliability of a match. Search engines that use a probability based scoring will take both of these factors into account for their scoring. For these search engines, stating the number of peaks identified is sufficient to assess the reliability (obviously along with the probability cut-off as acceptance criterion).
32 PMF-based Identifications (cont d) In the Results Section For search engines that do not use a probability based scoring the number of peaks matched and unmatched must be reported. If poor parameters were used for the peaklist generation this will make assignments appear less reliable than they may actually be due to the high number of masses unaccounted for. For search engines that do not use probability/ E-value scoring extra information is required to assess the reliability; e.g. Comparison of the score to that of the highest ranking nonhomologous protein. Sequence confirmation by MS/MS (the properly labeled spectrum should be submitted) or Edman-sequencing Western blot
33 Example Presentation of PMF Results In the Results Section * * * * Acceptance criterion. Accession# Matched /searched Seq.Cov BAD /24 28% CAA /34 41% BAD /28 37% CAA /21 46% BAC /17 13% CAH /14 31% CAB /47 38% AAD /36 37% AAD /35 34% Sequence confirmed by PSD or CID e 103 VLQAPGYNGTGKDWALIK SASGGGFYPPDEVIER WGGTAGQAFDR TGTSFPNNDYGIIR SGIRGDGVGAYSR DSVLGYADVTLPPGR ILTDAGYAPIPAEINAK AAATTQGSGWGVLAYEPVSGK DKEAWGAINGLQK 64 Could also be a score; probability; score comparison... * Required information Additional information, such as protein s name, function, MW, pi, may be included.
34 PMF-based Identifications In the Results Section MS spectra appropriately labeled should be included masses detected as well as peptide assignments. Some software will not produce spectra with mass and assignment labeled peaks. Alternative acceptable format: The spectrum with the masses labeled, accompanied with a table with the masses and peptide assignments.
35 Example Presentation of PMF Spectrum In the Results Section
36 Quantitation Results In the Results Section Number of peptides used for protein quantitation measurement The reliability of a protein level measurement will be improved by more peptide measurement data points. It is especially important to indicate any protein quantitation measurements that are the result of a single peptide measurement.
37 Quantitation Results Protein quantitation measurement and accuracy (e.g. mean and standard deviation) In the Results Section There are many factors that can affect the accuracy of measurements: The quantitation method (label-free; SILAC; ICAT etc.) The degree of label incorporation (for isotopic labeling measurements); The amount of sample; Problems with signal to noise for weak signals; Saturation problems for high level samples. Hence, it is not sufficient to quote measurement accuracies from other studies using a similar method and assume the same accuracy in your study. The threshold for deciding if a result represents a significant change should be justified in relation to the accuracy of your quantitation measurements.
38 Example Presentation of Quantitation Data * * * * * Required
39 The Future These guidelines will be continuously adjusted to the ever changing needs of the proteomics field. As software improves to become easier to output results into compliant formats, it will become easier to properly present proteomics data in any forum. As common formats e.g. mzml, AnalysisXML, become universally available, tools will be produced that will be able to automatically extract the relevant information from raw data and search results. Once these formatting problems are solved, journal submission guidelines for proteomics data are expected to become similar for all journals. Making raw data publicly available is going to become more common.
The Mascot protein identification program (Matrix Science, Ltd.) uses statistical methods to assess the validity of a match. MS/MS data is not ideal. That is, there are unassignable peaks (noise) and usually
ProteinScape Proteomics Data Analysis & Management Innovation with Integrity Mass Spectrometry ProteinScape a Virtual Environment for Successful Proteomics To overcome the growing complexity of proteomics
Aiping Lu Key Laboratory of System Biology Chinese Academic Society APLV@sibs.ac.cn Proteome and Proteomics PROTEin complement expressed by genome Marc Wilkins Electrophoresis. 1995. 16(7):1090-4. proteomics
Error Tolerant Searching of Uninterpreted MS/MS Data 1 In any search of a large LC-MS/MS dataset 2 There are always a number of spectra which get poor scores, or even no match at all. 3 Sometimes, this
Global and Discovery Proteomics Christine A. Jelinek, Ph.D. Johns Hopkins University School of Medicine Department of Pharmacology and Molecular Sciences Middle Atlantic Mass Spectrometry Laboratory Global
quantification using ms based proteomics lennart martens Computational Omics and Systems Biology Group Department of Medical Protein Research, VIB Department of Biochemistry, Ghent University Ghent, Belgium
Exam Master course KEMM03 Principles of Mass Spectrometric Protein Characterization 2010-10-29 kl 08.15-13.00 Use a new paper for answering each question! Write your name on each paper! Aids: Mini calculator,
Mass Spectra Alignments and their Significance Sebastian Böcker 1, Hans-Michael altenbach 2 1 Technische Fakultät, Universität Bielefeld 2 NRW Int l Graduate School in Bioinformatics and Genome Research,
Laboration 1 Identifiering av proteiner med Mass Spektrometri Klinisk Kemisk Diagnostik Sven Kjellström 2014 firstname.lastname@example.org 0702-935060 Laboration 1 Klinisk Kemisk Diagnostik Identifiering av
Proteomics data analysis seminar Quantitative proteomics and transcriptomics of anaerobic and aerobic yeast cultures reveals post transcriptional regulation of key cellular processes de Groot, M., Daran
ProSightPC 3.0 Quick Start Guide The Thermo ProSightPC 3.0 application is the only proteomics software suite that effectively supports high-mass-accuracy MS/MS experiments performed on LTQ FT and LTQ Orbitrap
Mascot Search Results FAQ 1 We had a presentation with this same title at our 2005 user meeting. So much has changed in the last 6 years that it seemed like a good idea to re-visit the topic. Just about
AB SCIEX TOF/TOF 4800 PLUS SYSTEM Cost effective flexibility for your core needs AB SCIEX TOF/TOF 4800 PLUS SYSTEM It s just what you expect from the industry leader. The AB SCIEX 4800 Plus MALDI TOF/TOF
In-Depth Qualitative Analysis of Complex Proteomic Samples Using High Quality MS/MS at Fast Acquisition Rates Using the Explore Workflow on the AB SCIEX TripleTOF 5600 System A major challenge in proteomics
Application Note # LCMS-81 Introducing New Proteomics Acquisiton Strategies with the compact Towards the Universal Proteomics Acquisition Method Introduction During the last decade, the complexity of samples
Program Overview Session 1. Course Presentation: Mass spectrometry-based proteomics for molecular and cellular biologists Session 2. Principles of Mass Spectrometry Session 3. Mass spectrometry based proteomics
Searching Nucleotide Databases 1 When we search a nucleic acid databases, Mascot always performs a 6 frame translation on the fly. That is, 3 reading frames from the forward strand and 3 reading frames
ProteinPilot Report for ProteinPilot Software Detailed Analysis of Protein Identification / Quantitation Results Automatically Sean L Seymour, Christie Hunter SCIEX, USA Pow erful mass spectrometers like
Protein Prospector and Ways of Calculating Expectation Values 1/16 Aenoch J. Lynn; Robert J. Chalkley; Peter R. Baker; Mark R. Segal; and Alma L. Burlingame University of California, San Francisco, San
Thermo Scientific PepFinder Software A New Paradigm for Peptide Mapping For Conclusive Characterization of Biologics Deep Protein Characterization Is Crucial Pharmaceuticals have historically been small
With data depth and quality Analysis of a tryptic digest by peptide mass fingerprinting, MS/MS and MS/MS/MS MS was performed on the tryptic digest of horse myoglobin using DHBA on the target. The resulting
Ma B. Challenges in computational analysis of mass spectrometry data for proteomics. SCIENCE AND TECHNOLOGY 25(1): 1 Jan. 2010 JOURNAL OF COMPUTER Challenges in Computational Analysis of Mass Spectrometry
MRMPilot Software: Accelerating MRM Assay Development for Targeted Quantitative Proteomics With Unique QTRAP and TripleTOF 5600 System Technology Targeted peptide quantification is a rapidly growing application
The Scheduled MRM Algorithm Enables Intelligent Use of Retention Time During Multiple Reaction Monitoring Delivering up to 2500 MRM Transitions per LC Run Christie Hunter 1, Brigitte Simons 2 1 AB SCIEX,
Tutorial for proteome data analysis using the Perseus software platform Laboratory of Mass Spectrometry, LNBio, CNPEM Tutorial version 1.0, January 2014. Note: This tutorial was written based on the information
La Protéomique : Etat de l art et perspectives Odile Schiltz Institut de Pharmacologie et de Biologie Structurale CNRS, Université de Toulouse, Odile.Schiltz@ipbs.fr Protéomique et Spectrométrie de Masse
How Mascot Integra helps run a Core Lab 1 Areas where a database can help a core lab Project, experiment and sample tracking Flexibility in experiment design Role based security Automation Custom results
Data mining with Mascot Integra 1 What is Mascot Integra? Fully functional out-the-box solution for proteomics workflow and data management Support for all the major mass-spectrometry data systems Powered
A look at data for quantitative analysis using MSight and Phenyx Pierre-Alain Binz Institut Suisse de Bioinformatique GeneBio SA Atelier Protéomique Quantitative 25-27 Juin 2007 La Grande Motte Already
Introduction to Proteomics Åsa Wheelock, Ph.D. Division of Respiratory Medicine & Karolinska Biomics Center email@example.com In: Systems Biology and the Omics Cascade, Karolinska Institutet, June 9-13,
Visualize The Multitool for Proteomics! File Open Opens an.ez2 file to be examined. Import from TPP Imports data from files created by Trans Proteomic Pipeline. User chooses mzxml, pepxml and FASTA files
MassMatrix Web Server User Manual Version 2.2.3 or later Hua Xu, Ph. D. Center for Proteomics & Bioinformatics Case Western Reserve University August 2009 Main Navigation Bar of the Site MassMatrix Web
Proteomic Analysis using Accurate Mass Tags Gordon Anderson PNNL January 4-5, 2005 Outline Accurate Mass and Time Tag (AMT) based proteomics Instrumentation Data analysis Data management Challenges 2 Approach
High-resolution MALDI-FT-ICR MS Imaging for the in-situ Analysis of Metabolites from Intact Tissues Axel Walch Research Unit Analytical Pathology Neuherberg, 2016-10-12 Molecular Tissue Analysis by MALDI
Proteomic data analysis for Orbitrap datasets using Resources available at MSI. September 28 th 2011 Pratik Jagtap The Minnesota http://www.mass.msi.umn.edu/ Proteomics workflow Trypsin Protein Peptides
A Strategy for the Efficient Identification of Modified Peptides 1 Aim To get matches to additional modified peptides Unsuspected chemical or post-translational modifications Minor sequence variants, such
Pinpointing phosphorylation sites using Selected Reaction Monitoring and Skyline Christina Ludwig group of Ruedi Aebersold, ETH Zürich The challenge of phosphosite assignment Peptides Phosphopeptides MS/MS
Research-grade Targeted Proteomics Assay Development: PRMs for PTM Studies with Skyline or, How I learned to ditch the triple quad and love the QE Jacob D. Jaffe Skyline Webinar July 2015 Proteomics and
Spectrum Quality Assessment in Mass Spectrometry Proteomics 1. Background Rheanna Mainzer Supervised by Dr. Luke Prendergast La Trobe University An important research problem in mass spectrometry is in
Mass Spectrometry Based Proteomics Proteomics Shared Research Oregon Health & Science University Portland, Oregon This document is designed to give a brief overview of Mass Spectrometry Based Proteomics
MultiQuant Software 2.0 for Targeted Protein / Peptide Quantification Gold Standard for Quantitative Data Processing Because of the sensitivity, selectivity, speed and throughput at which MRM assays can
Written test: Analysis of data from high-throughput molecular biology experiments BB2490 (BIO) or DD2399 (CSC) Name: Pnr: Wednesday the 18th of February, 13.00-15.00, Albanova FB52 Instructions: The test
Quantitation Quantitation was first introduced in Mascot 2.2. Our goal is to support all of the popular methodologies. 1 Quantitation - Overview Protocol Basis Ratios Examples reporter Specific reporter
Accurate Mass Screening Workflows for the Analysis of Novel Psychoactive Substances TripleTOF 5600 + LC/MS/MS System with MasterView Software Adrian M. Taylor AB Sciex Concord, Ontario (Canada) Overview
A Streamlined Workflow for Untargeted Metabolomics Employing XCMS plus, a Simultaneous Data Processing and Metabolite Identification Software Package for Rapid Untargeted Metabolite Screening Baljit K.
Retrospective Analysis of a Host Cell Protein Perfect Storm: Identifying Immunogenic Proteins and Fixing the Problem Kevin Van Cott, Associate Professor Dept. of Chemical and Biomolecular Engineering Nebraska
Cambridge Isotope Laboratories, Inc. www.isotope.com Proteomics Mass Spectrometry Signal Calibration for Protein Quantitation Michael J. MacCoss, PhD Associate Professor of Genome Sciences University of
Proteomics Methodology for LC-MS/MS Data Analysis Methodology for LC-MS/MS Data Analysis Peptide mass spectrum data of individual protein obtained from LC-MS/MS has to be analyzed for identification of
High Throughput Proteomics 1 High throughput means... Continuous real-time searching of data Human scrutiny of results is not practical High throughput doesn t necessarily mean large scale. We would characterise
Agilent G2721AA/G2733AA Spectrum Mill MS Proteomics Workbench Application Guide Agilent Technologies Notices Agilent Technologies, Inc. 2012 No part of this manual may be reproduced in any form or by any
Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data M. Cannataro, P. H. Guzzi, T. Mazza, and P. Veltri Università Magna Græcia di Catanzaro, Italy 1 Introduction Mass Spectrometry
Increasing the Multiplexing of High Resolution Targeted Peptide Quantification Assays Scheduled MRM HR Workflow on the TripleTOF Systems Jenny Albanese, Christie Hunter AB SCIEX, USA Targeted quantitative
PREDA S4-classes Francesco Ferrari October 13, 2015 Abstract This document provides a description of custom S4 classes used to manage data structures for PREDA: an R package for Position RElated Data Analysis.
MaxQuant User s Guide Version 184.108.40.206 Jűrgen Cox and Matthias Mann Nature Biotechnology 26, 1367-1372 (2008) Sara ten Have 2012 http://www.lamondlab.com/ http://greproteomics.lifesci.dundee.ac.uk/ References
Workshop IIc Manual interpretation of MS/MS spectra Ebbing de Jong Why MS/MS spectra? The information contained in an MS spectrum (m/z, isotope spacing and therefore z ) is not enough to tell us the amino
Pesticide Analysis by Mass Spectrometry Purpose: The purpose of this assignment is to introduce concepts of mass spectrometry (MS) as they pertain to the qualitative and quantitative analysis of organochlorine
Mass Frontier 7.0 Quick Start Guide The topics in this guide briefly step you through key features of the Mass Frontier application. Editing a Structure Working with Spectral Trees Building a Library Predicting
TÁMOP-4.1.1.C-13/1/KONV-2014-0001 projekt Az élettudományi-klinikai felsőoktatás gyakorlatorientált és hallgatóbarát korszerűsítése a vidéki képzőhelyek nemzetközi versenyképességének erősítésére program
Anal Bioanal Chem (2007) 389:1017 1031 DOI 10.1007/s00216-007-1486-6 REVIEW Quantitative mass spectrometry in proteomics: a critical review Marcus Bantscheff & Markus Schirle & Gavain Sweetman & Jens Rick
Interpretation of MS-Based Proteomics Data Yet-Ran Chen, 陳 逸 然 Agricultural Biotechnology Research Center Academia Sinica Brief Overview of Protein Identification Workflow Protein Sample Specific Protein
SimGlycan Software*: A New Predictive Carbohydrate Analysis Tool for MS/MS Data Automated Data Interpretation for Glycan Characterization Jenny Albanese 1, Matthias Glueckmann 2 and Christof Lenz 2 1 AB
Shotgun Proteomic Analysis Department of Cell Biology The Scripps Research Institute Biological/Functional Resolution of Experiments Organelle Multiprotein Complex Cells/Tissues Function Expression Analysis
Application Notes # LCMS-35 esquire series Application of LC/APCI Ion Trap Tandem Mass Spectrometry for the Multiresidue Analysis of Pesticides in Water An LC-APCI-MS/MS method using an ion trap system
Reiner Westermeier, Torn Naven Hans-Rudolf Höpker Proteomics in Practice A Guide to Successful Experimental Design 2008 Wiley-VCH Verlag- Weinheim 978-3-527-31941-1 Preface Foreword XI XIII Abbreviations,
Erasmus Intensive Program SYNAPS Univ. of Crete - Summer 2007 Mass Spectrometry for Chemists and Biochemists Spiros A. Pergantis Assistant Professor of Analytical Chemistry Department of Chemistry University
Bruker Daltonics Application Note # MT-90 MALDI-TDS: A Coherent MALDI Top-Down-Sequencing Approach Applied to the ABRF-Protein Research Group Study 2008 In the ABRF-PRG study 2008 [*] the ability to characterize
Introduction to mass spectrometry (MS) based proteomics and metabolomics Tianwei Yu Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University September 10, 2015 Background
PeptidomicsDB: a new platform for sharing MS/MS data. Federica Viti, Ivan Merelli, Dario Di Silvestre, Pietro Brunetti, Luciano Milanesi, Pierluigi Mauri NETTAB2010 Napoli, 01/12/2010 Mass Spectrometry
Research In-depth Analysis of Tandem Mass Spectrometry Data from Disparate Instrument Types* S Robert J. Chalkley, Peter R. Baker, Katalin F. Medzihradszky, Aenoch J. Lynn, and A. L. Burlingame Mass spectrometric
Building innovative drug discovery alliances Evotec Munich Quantitative Proteomics to Support the Discovery & Development of Targeted Drugs Evotec AG, Evotec Munich, June 2013 About Evotec Munich A leader
For the next half hour I m going to be describing some of the different options for peak peaking. The profit is with getting better protein ID or quantitation, but to be totally honest, the pleasure really
Introduction to Proteomics Why Proteomics? Same Genome Different Proteome Black Swallowtail - larvae and butterfly Biological Complexity Yeast - a simple proteome 6,113 proteins = 344,855 tryptic peptides
Advantages of Using Triple Quadrupole over Single Quadrupole Mass Spectrometry to Quantify and Identify the Presence of Pesticides in Water and Soil Samples André Schreiber AB SCIEX Concord, Ontario (Canada)
Unique Software Tools to Enable Quick Screening and Identification of Residues and Contaminants in Food Samples using Accurate Mass LC-MS/MS Using PeakView Software with the XIC Manager to Get the Answers
Research Improved Validation of Peptide MS/MS Assignments Using Spectral Intensity Prediction* S Shaojun Sun, Karen Meyer-Arendt, Brian Eichelberger, Robert Brown, Chia-Yu Yen, William M. Old, Kevin Pierce,
MS Amanda Standalone User Manual February 2014 Viktoria Dorfer, Peter Pichler, Stephan Winkler, and Karl Mechtler 1. Installation of MS Amanda Standalone In order to install MS Amanda Standalone please
SimGlycan Software*: A New Predictive Carbohydrate Analysis Tool for MS/MS Data Automated Data Interpretation for Glycan Characterization Jenny Albanese1, Matthias Glueckmann2 and Christof Lenz2 1Applied
Supplementary data Protein Hit1, a novel box C/D snornp assembly factor, controls cellular concentration of protein Rsa1p by direct interaction Benjamin Rothé, Jean-Michel Saliou, Marc Quinternet, Régis
Interpretation Mass spectral interpretation is not a trivial process. Presented below are some basic terms and examples designed to provide a background on which to build further knowledge through additional
ProteinPilot Software Overview High Quality, In-Depth Protein Identification and Protein Expression Analysis Sean L. Seymour and Christie Hunter SCIEX, USA As mass spectrometers for quantitative proteomics
LC-MS/MS for Chromatographers An introduction to the use of LC-MS/MS, with an emphasis on the analysis of drugs in biological matrices LC-MS/MS for Chromatographers An introduction to the use of LC-MS/MS,
itraq Tips and Tricks Darryl Pappin Cold Spring Harbor Laboratory Patrick Emery Matrix Science Ltd. 1 Good morning. Unfortunately Darryl Pappin is unable to attend the Matrix Science workshop and the ASMS
Overview Fast and Quantitative Analysis of Data for Investigating the Heterogeneity of Intact Glycoproteins by ESI-MS Kate Zhang 1, Robert Alecio 2, Stuart Ray 2, John Thomas 1 and Tony Ferrige 2. 1 Genzyme