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6.092

Bioinformatics and Proteomics

Undergraduate · Historical course

January IAP 2010 · Introduction to Programming in Java

This course is an introduction to software engineering, using the Java™ programming language. It covers concepts useful to 6.005. Students will learn the fundamentals of Java. The focus is on developing high quality, working software that solves real problems. The course is designed for students with some programming experience, but if you have none and are motivated you will do fine. Students who have taken 6.005 should not take this course. Each class is composed of one hour of lecture and one hour of assisted lab work. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

January IAP 2006 · Java Preparation for 6.170

Topics

  • Review, Question and Answer, Writing an Address Book Program that Loads and Stores Files to Disk

Readings

  • Flanagan, David. Java™ in a Nutshell. 5th ed. Cambridge, MA: O’Reilly, 2005. ISBN: 0596007736.
    Recommended
  • Flanagan, David, and Brett McLaughlin. Java™ 1.5 Tiger: A Developer’s Notebook . Cambridge, MA: O’Reilly, 2004. ISBN: 0596007388.
    Recommended
January IAP 2005

This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon.

Readings

  • Introduction
    Listed reading
  • Abstraction Level 1: Sequence Introduction to Bioinformatics Laboratory / Bioinformatics in the Computer Industry
    Listed reading
  • Course Introduction, Review of Modern Biology I
    Listed reading
  • 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)
    Listed reading
  • Introduction to Bioinformatics Laboratory / Bioinformatics in the Computer Industry
    Listed reading
  • Moormans, M. W. Matlab on Athena, Technical Publication AC-71 . Cambridge, MA: Massachusetts Institute of Technology, 2001. ↗
    Listed reading
  • Pochet, N., et al. “Systematic benchmarking of microarray data classification: assessing the role of nonlinearity and dimensionality reduction.” Bioinformatics, 2004.
    Listed reading
  • 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.
    Listed reading
  • Abstraction Level 1: Sequence
    Listed reading
  • Review of Modern Biology II
    Listed reading
  • Sequence Analysis: Motif and Regulation
    Listed reading
  • Kellis, M., et al. “Sequencing and comparison of yeast species to identify genes and regulatory elements.” Nature 423, no. 6937 (2003): 241-54.
    Listed reading
  • 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.
    Listed reading
  • Sequence Analysis: Genes and Genome
    Listed reading
  • 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.
    Listed reading
  • 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.
    Listed reading
  • Sequence Analysis: Gene Evolution
    Listed reading
  • Abstraction Level 2: Expression Microarray Expression Data Analysis
    Listed reading
  • 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.
    Listed reading
  • Machine Learning: Bayesian Methodologies
    Listed reading
  • 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.
    Listed reading
  • Abstraction Level 2: Expression
    Listed reading
  • Abstraction Level 4: Systems/Misc
    Listed reading
  • Bioinformatics in the Biotech Industry
    Listed reading
  • Kramer, R., and D. Cohen. “Functional genomics to new drug targets.” Nature Reviews Drug Discovery 3, no. 11 (2004): 965-72.
    Listed reading
  • Lawler, A. “Diabetes research. Broad-Novartis venture promises a no-strings, public gene database.” Science 306, no. 5697 (2004): 795.
    Listed reading
  • Control and Feedback in Systems
    Listed reading
  • Rangel, C., et al. “Modeling T-cell activation using gene expression profiling and state-space models.” Bioinformatics 20, no. 9 (2004): 1361-72.
    Listed reading
  • Scale-free Networks I
    Listed reading
  • Goh, K. I., et al. “Classification of scale-free networks.” Proc Natl Acad Sci U S A 99, no. 20 (2002): 12583-8.
    Listed reading
  • 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.
    Listed reading
  • 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.
    Listed reading
  • Abstraction Level 3: Proteomics
    Listed reading
  • Statistical Models and Stochastic Processes in Proteomics
    Listed reading
  • 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)
    Listed reading
  • Signal Processing for Proteomics
    Listed reading
  • 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.
    Listed reading
  • Abstraction Level 3: Proteomics Biological Methods, Automation, Robotics
    Listed reading
  • Biological Methods, Automation, Robotics
    Listed reading
  • 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)
    Listed reading
  • 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).
    Listed reading
  • Oppenheim, A. V., A. S. Willsky, and H. Nawab. Signals and Systems. 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1997. ISBN: 0138147574.
    Listed reading
  • 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.
    Listed reading
  • 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. ↗
    Listed reading
  • Hunter, Lawrence. Introduction to Molecular Biology for the Computer Scientist . ( PDF ) ↗
    Listed reading
Sources