Classification of Genomic Sequences via Wavelet Variance and a Self-Organizing Map with an Application to Mitochondrial DNA
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Agnieszka E Jach
We present a new methodology for discriminating genomic symbolic sequences, which combines wavelet analysis and a self-organizing map algorithm. Wavelets are used to extract variation across various scales in the oligonucleotide patterns of a sequence. The variation is quantified by the estimated wavelet variance, which yields a feature vector. Feature vectors obtained from many genomic sequences, possibly of different lengths, are then classified with a nonparametric self-organizing map scheme. When applied to nearly 200 entire mitochondrial DNA sequences, or their fragments, the method predicts species taxonomic group membership very well, and allows the results to be visualized. When only thousands of nucleotides are available, wavelet-based feature vectors of short oligonucleotide patterns are more efficient in discrimination than frequency-based feature vectors of long patterns. This new data analysis strategy could be extended to numeric genomic data. The routines needed to perform the computations are readily available in two packages of software R.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Testing for Gene-Gene Interaction with AMMI Models
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- Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach
- Detecting Genotyping Error Using Measures of Degree of Hardy-Weinberg Disequilibrium
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- The Apportionment of Total Genetic Variation by Categorical Analysis of Variance
- Dealing with Heterogeneity between Cohorts in Genomewide SNP Association Studies
- An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data
- Parameter Estimation in Multiple-Hidden I.I.D. Models from Biological Multiple Alignment
- Asymptotic Distribution of the "Orthogonal" Quantitative Transmission Disequilibrium Test in a Structured Population: Exact Formula
- Comparing Spatial Maps of Human Population-Genetic Variation Using Procrustes Analysis
- An Internal Calibration Method for Protein-Array Studies
- Weighted-LASSO for Structured Network Inference from Time Course Data
- Trilocus Disequilibrium Analysis of Multiallelic Markers in Outcrossing Populations
- Sparse Partial Least Squares Classification for High Dimensional Data
- Reconstructability Analysis as a Tool for Identifying Gene-Gene Interactions in Studies of Human Diseases
- Sub-Modular Resolution Analysis by Network Mixture Models
- Space Oriented Rank-Based Data Integration
- The Generalized Odds Ratio as a Measure of Genetic Risk Effect in the Analysis and Meta-Analysis of Association Studies
- Network Enrichment Analysis in Complex Experiments
- Shrinkage Estimation of Effect Sizes as an Alternative to Hypothesis Testing Followed by Estimation in High-Dimensional Biology: Applications to Differential Gene Expression
- Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data
- A Random Coefficients Model for Regional Co-Expression Associated with DNA Copy Number
- Locating Multiple Interacting Quantitative Trait Loci with the Zero-Inflated Generalized Poisson Regression
- Classification of Genomic Sequences via Wavelet Variance and a Self-Organizing Map with an Application to Mitochondrial DNA
- Confidently Estimating the Number of DNA Replication Origins
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- The Detection of Blur in Affymetrix GeneChips
- Regression-Based Multi-Trait QTL Mapping Using a Structural Equation Model
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- Including Probe-Level Measurement Error in Robust Mixture Clustering of Replicated Microarray Gene Expression
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