profileanalysis Innovation with Integrity Quickly pinpointing and identifying potential biomarkers in Proteomics and Metabolomics research

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1 profileanalysis Quickly pinpointing and identifying potential biomarkers in Proteomics and Metabolomics research Innovation with Integrity Omics Research

2 Biomarker Discovery Made Easy by ProfileAnalysis Today, profiling of complex samples is a routine task in many research laboratories. An integral part of this process is applying statistical methods to quickly pinpoint relevant information and generate knowledge. ProfileAnalysis is designed not only to give beginners a head start in metabolomics and proteomics research, but also to meet the needs of expert users. It provides a complete set of intuitive tools; for data pre-processing, in-depth nonsupervised or supervised statistical analysis, identification and feedback experiments. Linked displays enable interactive data evaluation for quickly pinpointing and validating relevant information. It s All in Your Data... Direct Identification Using SmartFormula It s not only statistics that play an important role in metabolomics applications. High-resolution, accurate mass and isotopic pattern data enable direct identification of potential biomarkers via their molecular formula using Bruker s unique SmartFormula algorithm. Fast Database Searches Using CompoundCrawler facilitate structural assignments. The hypothetical structure can be quickly verified by means of targeted MS/MS accurate mass measurements. Direct Link to Protein Quantification ProfileAnalysis is part of the ProteinScape environment. Statistically relevant regulated peptides are reliably associated to the identified proteins in an easy to use and powerfull biomarker discovery workflow. Quick and easy searches of web-based databases with the CompoundCrawler

3 Pinpoint and Identify Relevant Compounds Using Non-Supervised... Principal Component Analysis (PCA) or Hierarchical Clustering Analysis (HCA) quickly reveal sample clustering helping to focus on the relevant information in a data set. 1. Quickly find interesting information in complex data sets Plotted EIC traces directly reveal whether statistically observed differences can be validated by visual inspection of the raw data. If compounds of interest are discovered, fast identification of elemental compositions using SmartFormula is the next logical step. PCA HCA Structural assignment of potential metabolic biomarkers is enabled through public database queries (for example, within ChemSpider, KEGG or ChEBI) via the direct link to the CompoundCrawler. 2. Validate observations using the raw data: EIC plots All compound annotations can be stored as bucket table feature annotations. 3. Identify targets using SmartFormula and CompoundCrawler 4. Update annotation in bucket table for later review

4 ... or Supervised Statistics Pinpoint and identify relevant compounds EIC traces Survey view SPL distribution Bucket table t-test result table Volcano plot ROC curves Quickly focus on relevant compounds using the t-test based volcano plot. Automatically calculated false discovery rate and family-wise error rates increase confidence in discovered differences. Confirm tentative compound annotations using MS/MS data: Easily generate MS/MS spectra for all compounds of interest by automatically creating scheduled precursor lists (SPL) for follow-up MS/MS measurements. Can the identified biomarker be used as a criterion to separate sample classes, for example, healthy from diseased? Receiver Operator Characteristics (ROC) curves provide a quick answer. Comparing apples with oranges Want to find out whether a sample, for instance, orange juice, was adulterated? Classification of new samples to a validated Partial last squares- (PLS) or PCAbased model can quickly give you the answer. In addition, PLS regression can be used for predicting to which degree a rogue sample was adulterated. Classification of new samples Classified samples Predicted values

5 Efficient Preprocessing of Complex Omics Data Extract all relevant information The Find Molecular Features (FMF) peakfinding algorithm automatically extracts and combines all relevant information, even from very complex LC-MS/MS data sets. It combines the ions belonging to the same compound into one feature, i.e. isotopes, charge states, adducts or neutral losses. Data reduction is achieved by efficiently differentiating real signals from noise. Sophisticated data alignment Retention-time alignment can be performed by a shifting vector algorithm that also takes non-linear retentiontime shifts into account a prerequisite for large metabolomics or label free proteomics studies where retention times may shift. Filter, scale and normalize data to match experimental designs ProfileAnalysis easily creates bucket tables of LC-MS data that are based on the extracted FMF compounds, LC-MALDI compounds and data derived from direct infusion FT-ICR studies. Different filtering, normalization and scaling options such as Pareto scaling complete the set of data preprocessing tools. All data pre-processing steps applied in calculating the bucket table form the basis for successful subsequent statistical analysis. Series of LC-MS/MS experiments Find Molecular Features Retention Time Alignment Bucketing Filtering, Scaling & Normalization Statistical Analysis Identification via Protein ID or Elemental Formula

6 Bruker Daltonics is continually improving its products and reserves the right to change specifications without notice. BDAL , Technical Specifications Data Processing of Bruker FTMS, Ion Trap MS, ESI-(Q)-TOF, MALDI-TOF data and netcdf-files Import of external bucket Tables as.csv file Direct link to original data file Export of Bucket Table to formats compatible to MatLab, R, SIMCA-P Export of quantitative results to ProteinScape for label-free proteomic analysis Data processing Find Molecular Features (FMF) algorithm (combining isotopes, charges states, adducts and neutral losses belonging to the same compound into one feature) Import of Find Molecular Features results calculated in DataAnalysis Recalibration of mass axis Retention Time Alignment Spectral Background Subtraction Rectangular and advanced bucketing Various filtering options (e.g. by filtering for frequency of occurrence) Various normalization and scaling options available Statistics Principle Component Analysis (PCA), unsupervised clustering (HCA) Partial least squares projections to latent structures (PLS) and Partial least squares Discriminant Analysis (PLS-DA ) Interlinked statistical plots for review (e.g. Scores, Loadings, Influence, Hotelling s T²) Cross Validation, Test Set Validation Classification based on PCA and PLS models Student s t-test and ANOVA (including Volcano plot visualization) Receiver Operator characteristics curves Other features SmartFormula: Molecular Formula Generation Survey View: Graphical overview of data distribution Plotting Extracted Ion Chromatograms (EIC traces) within raw data Automatic Scheduled Precursor List (SPL) generation from t-test results Bucket Scatter plots Installation Qualification Detailed Manual Tutorial Data For research use only. Not for use in diagnostic procedures. Bruker Daltonik GmbH Bremen Germany Phone +49 (0) Fax +49 (0) sales@bdal.de Bruker Daltonics Inc. Billerica, MA USA Fremont, CA USA Phone +1 (978) Phone +1 (510) Fax +1 (978) Fax +1 (510) ms-sales@bdal.com cam-sales@bruker.com