The University of Texas at Austin
Department of Electrical and Computer Engineering

EE381V Genomic Signal Processing

Fall Semester 2008

Prerequisites: EE381J Probability and Stochastic Processes. Also, exposure to differential equations and familiarity with Matlab.
Official course description: Introduction to the fundamentals of genomic signal and information processing. Topics include sequence alignment; regulatory motif discovery; gene finding; biomolecular detection systems including DNA microarrays and quantitative polymerase chain reaction systems; clustering and classification of gene expression data; and modeling and inference for genetic regulatory networks.
Lecture notes and other handouts:

Lecture #1: Course outline and molecular biology primer.

Suggested reading:

R. Karp, ``Mathematical Challenges from Genomics and Molecular Biology,'' Notices of AMS, 49(5), pp. 544-553, May 2002.

D. Anastassiou, "Genomic Signal Processing," IEEE Signal Processing Magazine, vol. 18, no. 4, 2001, pp: 8-20.

L. Hunter, ``Molecular Biology for Computer Scientists.''
An overview of molecular biology concepts. The material most relevant to lecture #1 is in Section 4.3 and Section 5.

Lecture #2: Further background, sequencing.

Suggested reading:

N. Hall, ``Advanced sequencing technologies and their wider impact in microbiology,'' J. of Experimental Biology, 209, pp. 1518-1525, 2007.
A brief survey of existing and emerging sequencing techniques.

Lecture #3: Pair-wise alignment algorithms.

Suggested reading:

Needlman-Wunsch paper.

Ron Shamir's lecture notes.
Clement Gropl's lecture notes.

Lecture #4: Alignment heuristics, multiple sequence alignment.

Suggested reading:

C. Notredame, "Recent progresses in multiple sequence alignment: a survey."

Lecture #5: Markov Chains and Hidden Markov Models.

Suggested reading:

R. Durbin et. al., Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids .
Chapter 3 in this book (available on-line through Ebook Library via UT Libraries) contains relevant material.

Lecture #6: Decoding in HMMs.

Suggested reading:

L. R. Rabiner, A tutorial on HMMs and selected applications in speech recognition, Proceedings of the IEEE, vol. 77, no. 2, February 1989, pp: 257-286.
The most relevant material is on pages 1-10.

Lecture #7: Parameter estimation in HMMs.

Suggested reading:

L. R. Rabiner, A tutorial on HMMs and selected applications in speech recognition, Proceedings of the IEEE, vol. 77, no. 2, February 1989, pp: 257-286.
The most relevant material is on pages 1-10.

Lecture #8: Aligning sequences to a profile HMM.

Suggested reading:

R. Durbin et. al., Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids .
Chapter 5 in this book (available on-line through Ebook Library via UT Libraries) contains relevant material.

Lecture #9: Regulatory motif discovery.

Suggested reading:

B. Walsh, MCMC and Gibbs sampling, lecture notes.

B. Uddin, Gibbs sampling tutorial.

Lecture #10: Sequential MC for motif discovery.

Suggested reading:

A. Doucet and X. Wang, Monte Carlo methods for signal processing: a review in the statistical signal processing context, IEEE Signal Processing Magazine, vol. 22, no. 6, Nov. 2005, pp: 152-170.

K.-C. Liang, X. Wang, D. Anastassiou, A Sequential Monte Carlo Method for Motif Discovery, IEEE Transactions on Signal Processing, (to appear).
The most relevant material is on pages 1-11.

Lecture #11: Introduction to DNA microarrays.

Suggested reading:

A. K. Whitchurch, Gene expression microarrays, IEEE Potentials, Volume 21, Issue 1, Feb-Mar 2002, Page(s): 30-34.

Lecture #12: Modeling of DNA microarrays.

Suggested reading:

Lecture #13: Estimation in DNA microarrays.

Suggested reading:

J. Quackenbush, "Microarray data normalization and transformation", Nature Genetics 32, 496 - 501 (2002).
M. A. Newton et. al., "On differential variability of expression ratios", J. of Computational Biology, vol. 8, no. 1, 2001, pp: 37-52.

Lecture #14: Detecting differentially expressed genes. Modeling hybridization kinetics..

Suggested reading:

Lecture #15: Kinetics countinued. Clustering.

Suggested reading:

Y. Moreau et. al., "Functional bioinformatics of microarray data: from expression to regulation," Proceedings of the IEEE, Volume 90, Issue 11, Nov. 2002, Page(s): 1722 - 1743.

Lecture #16: Clustering algorithms.

Suggested reading:

D. Jian et. al., "Cluster analysis for gene expression data: a survey," IEEE Transactions on Knowledge and Data Engineering, Volume 16, Issue 11, Nov. 2004, Page(s): 1370 - 1386.

Lecture #17: Model based clustering.

Lecture #18: Sequencing by synthesis.

Lecture #19: Polymerase chain reaction (PCR) systems.

Suggested reading:

PCR primer.

Lecture #20: Modeling and estimation for PCR systems.

Suggested reading:

N. Lalam et. al., "Modelling the PCR amplification process by a size-dependent branching process and estimation of the efficiency", Adv. in Appl. Probab. Volume 36, Number 2 (2004), 602-615.

Lecture #21: Modeling gene networks.

Suggested reading:

D. Jian et. al., "Stochastic modeling and simulation of gene networks - A review of the state-of-the-art research on stochastic simulations," IEEE Signal Processing Magazine, Special Issue on Signal Processing in Genomics , vol. 24, no. 1, January 2007.

Lecture #22: Stochastic gene network models.

Suggested reading:

D. Jian et. al., "Stochastic modeling and simulation of gene networks - A review of the state-of-the-art research on stochastic simulations," IEEE Signal Processing Magazine, Special Issue on Signal Processing in Genomics , vol. 24, no. 1, January 2007.

Lecture #23: Deterministic ODE models of gene networks.

Suggested reading:

T. Chen et. al., "Modeling gene expression with differential equations," Pacific Symposium on Biocomputing, 1999.

F. Grognard et. al., "Piecewise-linear models of genetic regulatory networks: theory and examples," Lecture notes in control and information sciences (LNCIS), Springer-Verlag, 2007.

Lecture #24: Bayesian networks.

Suggested reading:

N. Friedman et. al., "Using Bayesian networks to analyze expression data", Proceedings of the 4th annual international conference on Computational Molecular Biology, 2000.

D. Pe'er, "Bayesian network analysis of signaling networks: a primer".

Lecture #25: Learning Bayesian networks.

Suggested reading:

N. Friedman et. al., "Using Bayesian networks to analyze expression data", Proceedings of the 4th annual international conference on Computational Molecular Biology, 2000.

D. Pe'er, "Bayesian network analysis of signaling networks: a primer".
Homeworks:

Problem Set Out Due Problems Solutions

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Problem Set	Out	Due	Problems	Solutions
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