6.092 | January IAP 2005 | Undergraduate

Bioinformatics and Proteomics

Readings

The table below maps the readings to specific lecture sessions. In addition, given below the table is a list of references for the course.

Papers

ses # TOPICS Readings
1

Introduction

Course Introduction, Review of Modern Biology I

Abstraction Level 1: Sequence 
Introduction to Bioinformatics Laboratory / Bioinformatics in the Computer Industry

Course Introduction, Review of Modern Biology I

Alterovitz, G., E. Afkhami, and M. Ramoni. “Robotics, Automation, and Statistical Learning for Proteomics.” In Focus on Robotics and Intelligent Systems Research. Edited by F. Columbus. Vol. 1. New York: Nova Science Publishers, Inc., 2005, sections I-II. (In press)

Introduction to Bioinformatics Laboratory / Bioinformatics in the Computer Industry

Moormans, M. W. Matlab on Athena, Technical Publication AC-71. Cambridge, MA: Massachusetts Institute of Technology, 2001.

Pochet, N., et al. “Systematic benchmarking of microarray data classification: assessing the role of nonlinearity and dimensionality reduction.” Bioinformatics, 2004.

Kasturi, J., R. Acharya, and M. Ramanathan. “An information theoretic approach for analyzing temporal patterns of gene expression.” Bioinformatics 19, no. 4 (2003): 449-58.

2

Abstraction Level 1: Sequence

Review of Modern Biology II

Sequence Analysis: Motif and Regulation

Review of Modern Biology II

Alterovitz, G., E. Afkhami, and M. Ramoni. “Robotics, Automation, and Statistical Learning for Proteomics.” In Focus on Robotics and Intelligent Systems Research. Edited by F. Columbus. Vol. 1. New York: Nova Science Publishers, Inc., 2005, sections I-II. (In press)

Sequence Analysis: Motif and Regulation

Kellis, M., et al. “Sequencing and comparison of yeast species to identify genes and regulatory elements.” Nature 423, no. 6937 (2003): 241-54.

Kellis, M., et al. “Methods in comparative genomics: genome correspondence, gene identification and regulatory motif discovery.” Journal of Computational Biology 11, no. 2-3 (2004): 319-55.

3

Abstraction Level 1: Sequence

Sequence Analysis: Genes and Genome

Sequence Analysis: Gene Evolution

Sequence Analysis: Genes and Genome

Kellis, M., B. W. Birren, and E. S. Lander. “Proof and evolutionary analysis of ancient genome duplication in the yeast Saccharomyces cerevisiae.” Nature 428, no. 6983 (2004): 617-24.

Jaillon, O., et al. “Genome duplication in the teleost fish Tetraodon nigroviridis reveals the early vertebrate proto-karyotype. Nature 431, no. 7011 (2004): 946-57.

Sequence Analysis: Gene Evolution

Kellis, M., B. W. Birren, and E. S. Lander. “Proof and evolutionary analysis of ancient genome duplication in the yeast Saccharomyces cerevisiae.” Nature 428, no. 6983 (2004): 617-24.

4

Abstraction Level 2: Expression 
Microarray Expression Data Analysis

Machine Learning: Bayesian Methodologies

Microarray Expression Data Analysis 
Sebastiani, P., I. S. Kohane, and M. F. Ramoni. “The role of machine learning in the post-genomic era.” Mach Learning 52, no. 1-2 (2003): 5-9.

Machine Learning: Bayesian Methodologies

Ramoni, M.F., P. Sebastiani, and I.S. Kohane. “Cluster analysis of gene expression dynamics.” Proc Natl Acad Sci U S A 99, no. 14 (2002): 9121-6.

5

Abstraction Level 2: Expression

Bioinformatics in the Biotech Industry

Abstraction Level 4: Systems/Misc

Control and Feedback in Systems

Bioinformatics in the Biotech Industry

Kramer, R., and D. Cohen. “Functional genomics to new drug targets.” Nature Reviews Drug Discovery 3, no. 11 (2004): 965-72.

Lawler, A. “Diabetes research. Broad-Novartis venture promises a no-strings, public gene database.” Science 306, no. 5697 (2004): 795.

Control and Feedback in Systems

Rangel, C., et al. “Modeling T-cell activation using gene expression profiling and state-space models.” Bioinformatics 20, no. 9 (2004): 1361-72.

6

Abstraction Level 4: Systems/Misc

Scale-free Networks I

Scale-free Networks II

Scale-free Networks I

Goh, K. I., et al. “Classification of scale-free networks.” Proc Natl Acad Sci U S A 99, no. 20 (2002): 12583-8.

Bilke, S., and C. Peterson. “Topological properties of citation and metabolic networks.” Phys Rev E Stat Nonlin Soft Matter Phys 64, no. 3 Pt 2 (2001): 036106.

Scale-free Networks II

Goh, K. I., et al. “Classification of scale-free networks.” Proc Natl Acad Sci U S A 99, no. 20 (2002): 12583-8.

Rzhetsky, A., and S. M. Gomez. “Birth of scale-free molecular networks and the number of distinct DNA and protein domains pergenome.” Bioinformatics 17, no. 10 (2001): 988-96.

7

Abstraction Level 3: Proteomics

Statistical Models and Stochastic Processes in Proteomics

Signal Processing for Proteomics

Statistical Models and Stochastic Processes in Proteomics

Alterovitz, G., E. Afkhami, and M. Ramoni. “Robotics, Automation, and Statistical Learning for Proteomics.” In Focus on Robotics and Intelligent Systems Research. Edited by F. Columbus. Vol. 1. New York: Nova Science Publishers, Inc., 2005, sections IV-V. (In press)

Signal Processing for Proteomics

Baggerly, K. A., J. S. Morris, and K. R. Coombes. “Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments.” Bioinformatics 20, no. 5 (2004): 777-85.

8

Abstraction Level 3: Proteomics 
Biological Methods, Automation, Robotics

Conclusion

Project Discussion and Wrap-up

Biological Methods, Automation, Robotics

Alterovitz, G., E. Afkhami, and M. Ramoni. “Robotics, Automation, and Statistical Learning for Proteomics.” In Focus on Robotics and Intelligent Systems Research. Edited by F. Columbus. Vol. 1. New York: Nova Science Publishers, Inc., 2005, section III. (In press)

References

Book Chapter

Alterovitz, G., E. Afkhami, and M. Ramoni. “Robotics, Automation, and Statistical Learning for Proteomics.” In Focus on Robotics and Intelligent Systems Research. Edited by F. Columbus. Vol. 1. New York: Nova Science Publishers, Inc., 2005. (In press).

Texts

Oppenheim, A. V., A. S. Willsky, and H. Nawab. Signals and Systems. 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1997. ISBN: 0138147574.

Papoulis, A., and S. U. Pillai. Probability, Random Variables and Stochastic Processes: Sanitary and Water Resources Engineering (Sanitary & Water Resources Engineering S). New York, NY: McGraw-Hill, 2002. ISBN: 0072817259.

Buy at MIT Press Kohane, I. S., A. T. Kho, and A. J. Butte. Microarrays for an Integrative Genomics. Cambridge, MA: MIT Press, 2002. ISBN: 026211271X.

Hunter, Lawrence. Introduction to Molecular Biology for the Computer Scientist. (PDF)