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Genomic Signal Processing

Genomic Signal Processing. Dr. C.Q. Chang Dept. of EEE. Outline. Basic Genomics Signal Processing for Genomic Sequences Signal Processing for Gene Expression Resources and Co-operations Challenges and Future Work. Basic Genomics. Genome.

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Genomic Signal Processing

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  1. Genomic Signal Processing Dr. C.Q. Chang Dept. of EEE

  2. Outline • Basic Genomics • Signal Processing for Genomic Sequences • Signal Processing for Gene Expression • Resources and Co-operations • Challenges and Future Work

  3. Basic Genomics

  4. Genome • Every human cell contains 6 feet of double stranded (ds) DNA • This DNA has 3,000,000,000 base pairs representing 50,000-100,000 genes • This DNA contains our complete genetic code or genome • DNA regulates all cell functions including response to disease, aging and development • Gene expression pattern: snapshot of DNA in a cell • Gene expression profile: DNA mutation or polymorphism over time • Genetic pathways: changes in genetic code accompanying metabolic and functional changes, e.g. disease or aging.

  5. DNA transcription mRNA translation Protein Gene: protein-coding DNA CCTGAGCCAACTATTGATGAA CCUGAGCCAACUAUUGAUGAA PEPTIDE

  6. In more detail (color ~state)

  7. Signal Processing for Genomic Sequences

  8. The Data Set

  9. The Problem • Genomic information is digital letters A, T, C and G • Signal processing deals with numerical sequences, character strings have to be mapped into one or more numerical sequences • Identification of protein coding regions • Prediction of whether or not a given DNA segment is a part of a protein coding region • Prediction of the proper reading frame • Comparing to traditional methods, signal processing methods are much quicker, and can be even more accurate in some cases.

  10. Sequence to signal mapping

  11. Signal Analysis • Spectral analysis (Fourier transform, periodogram) • Spectrogram • Wavelet analysis • HMT: wavelet-based Hidden Markov Tree • Spectral envelope (using optimal string to numerical value mapping)

  12. Spectral envelope of the BNRF1 gene from the Epstein-Barr virus • 1st section (1000bp), (b) 2nd section (1000bp), • (c) 3rd section (1000bp), (d) 4th section (954bp) • Conjecture: the 4th quarter is actually non-coding

  13. Signal Processing for Gene Expression

  14. Biological Question Data Analysis & Modeling Samplepreparation Microarray Life Cycle MicroarrayDetection Microarray Reaction Taken from Schena & Davis

  15. excitation scanning cDNA clones (probes) laser 2 laser 1 PCR product amplification purification emission printing mRNAtarget) overlay images and normalise 0.1nl/spot Hybridise target to microarray microarray analysis

  16. Image Segmentation • Simple way: fixed circle method • Advanced: fast marching level set segmentation Advanced Fixed circle

  17. Clustering and filtering methods Principal approaches: • Hierarchical clustering (kdb trees, CART, gene shaving) • K-means clustering • Self organizing (Kohonen) maps • Vector support machines • Gene Filtering via Multiobjective Optimization • Independent Component Analysis (ICA) Validation approaches: • Significance analysis of microarrays (SAM) • Bootstrapping cluster analysis • Leave-one-out cross-validation • Replication (additional gene chip experiments, quantitative PCR)

  18. ICA for B-cell lymphoma data Data: 96 samples of normal and malignant lymphocytes. Results: scatter-plotting of 12 independent components Comparison: close related to results of hierarchical clustering

  19. Resources and Co-operations Resources: databases on the internet such as • GeneBank • ProteinBank • Some small databases of microarray data Co-operations in need: • First hand microarray data • Biological experiment for validation

  20. Challenges and Future Work • Genomic signal processing opens a new signal processing frontier • Sequence analysis: symbolic or categorical signal, classical signal processing methods are not directly applicable • Increasingly high dimensionality of genetic data sets and the complexity involved call for fast and high throughput implementations of genomic signal processing algorithms • Future work: spectral analysis of DNA sequence and data clustering of microarray data. Modify classical signal processing methods, and develop new ones.

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