A Markov-Chain Model for the Analysis of High-Resolution Enzymatically 18O-Labeled Mass Spectra
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Dirk Valkenborg
The enzymatic 18O-labeling is a useful quantification technique to account for between-spectrum variability of the results of mass spectrometry experiments. One of the important issues related to the use of the technique is the problem of incomplete labeling of peptide molecules, which may result in biased estimates of the relative peptide abundance. In this manuscript, we propose a Markov-chain model, which takes into account the possibility of incomplete labeling in the estimation of the relative abundance from the observed data. This allows for the use of less precise but faster labeling strategies, which should better fit in the high-throughput proteomic framework. Our method does not require extra experimental steps, as proposed in the approaches developed by Mirgorodskaya et al. (2000), López-Ferrer et al. (2006) and Rao et al. (2005), while it includes the model proposed by Eckel-Passow et al. (2006) as a special case. The method estimates information about the isotopic distribution directly from the observed data and is able to account for biases induced by the different sulphur content in peptides as reported by Johnson and Muddiman (2004). The method is integrated in a statistically sound framework and allows for the calculation of the errors on the parameter estimates based on model theory. In this manuscript, we describe the methodology in a technical matter and assess the properties of the algorithm via a thorough simulation study. The method is also tested on a limited dataset; more intense validation and investigation of the operational characteristics is being scheduled.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Invited Editorial
- Measurement of Evidence and Evidence of Measurement
- Article
- Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays
- Determining Coding CpG Islands by Identifying Regions Significant for Pattern Statistics on Markov Chains
- Assessing Modularity Using a Random Matrix Theory Approach
- Choice of Summary Statistic Weights in Approximate Bayesian Computation
- Genetic Linkage Analysis in the Presence of Germline Mosaicism
- Fitting Boolean Networks from Steady State Perturbation Data
- Adaptive Elastic-Net Sparse Principal Component Analysis for Pathway Association Testing
- Bayesian Learning from Marginal Data in Bionetwork Models
- Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome
- Multiple Testing in Candidate Gene Situations: A Comparison of Classical, Discrete, and Resampling-Based Procedures
- Modeling Read Counts for CNV Detection in Exome Sequencing Data
- Multiscale Characterization of Signaling Network Dynamics through Features
- A Calibrated Multiclass Extension of AdaBoost
- False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies
- A Markov-Chain Model for the Analysis of High-Resolution Enzymatically 18O-Labeled Mass Spectra
- Repeated Measures Semiparametric Regression Using Targeted Maximum Likelihood Methodology with Application to Transcription Factor Activity Discovery
- Learning Monotonic Genotype-Phenotype Maps
- A Comparison of Multifactor Dimensionality Reduction and L1-Penalized Regression to Identify Gene-Gene Interactions in Genetic Association Studies
- Accuracy and Computational Efficiency of a Graphical Modeling Approach to Linkage Disequilibrium Estimation
- Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy
- A Three Component Latent Class Model for Robust Semiparametric Gene Discovery
- Log-Linear Modelling of Protein Dipeptide Structure Reveals Interesting Patterns of Side-Chain-Backbone Interactions
- A Robust Statistical Method to Detect Null Alleles in Microsatellite and SNP Datasets in Both Panmictic and Inbred Populations
- Large Sample Approximations of Probabilities of Correct Evolutionary Tree Estimation and Biases of Maximum Likelihood Estimation
- Interval Estimation of Familial Correlations from Pedigrees
- Information Metrics in Genetic Epidemiology
- Linear Combination Test for Hierarchical Gene Set Analysis
- Exploratory Analysis of Multiple Omics Datasets Using the Adjusted RV Coefficient
- Application of the Lasso to Expression Quantitative Trait Loci Mapping
- A Variance-Components Model for Distance-Matrix Phylogenetic Reconstruction
- Imputation Estimators Partially Correct for Model Misspecification
- On the Statistical Properties of SGoF Multitesting Method
- Meta-Analysis of Family-Based and Case-Control Genetic Association Studies that Use the Same Cases
- A Non-Parametric Method for Detecting Specificity Determining Sites in Protein Sequence Alignments
- Performance of Matrix Representation with Parsimony for Inferring Species from Gene Trees
- Disequilibrium Coefficient: A Bayesian Perspective
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- The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq
- Inferring Gene Networks using Robust Statistical Techniques
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- The Joint Null Criterion for Multiple Hypothesis Tests
- Multiple Imputation of Missing Phenotype Data for QTL Mapping
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- Surveying the Manifold Divergence of an Entire Protein Class for Statistical Clues to Underlying Biochemical Mechanisms
- Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes
- Entropy Based Genetic Association Tests and Gene-Gene Interaction Tests
- Weighted Lasso with Data Integration
- MA-SNP -- A New Genotype Calling Method for Oligonucleotide SNP Arrays Modeling the Batch Effect with a Normal Mixture Model
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