Bioinformatics I
/ PHARM 201
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Overview
Bioinformatics is driven by the need to understand complex biological systems for which data are accumulating at exponential or near exponential rates. Such an understanding relies of the effective representation of these data and the ability to analyze these data. This is a broad topic and we focus on macromolecular structure data, which is suitably complex, to introduce the principles of formal data representation, reductionism, comparison, classification, visualization and biological inference. As such the course also serves as an introduction to Structural Bioinformatics. Assessment
Weekly assignments: On Friday of each week students will receive a question paper on that weeks work which will be due 5pm the following Wed. in class. (50%). Final Exam: Students will be assigned one or more papers which cover a significant amount of the material covered in the course. They will be expected to critique that paper based on what they have learned and propose the next set of experiments (50%). Course Text
P.E.Bourne and H. Weissig Eds. Structural Bioinformatics. Wiley 2002 [from Amazon] The text is available for use in our Lab. Library 2011 Skaggs. Printed copies of the slides in notes form will be distributed with each lecture. Schedule
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Topic & Date |
Content |
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Workflow Overview of Course [slides] |
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Lecture 1: 09/26 Know Your Data - Principles of Protein Structure [slides] |
To model and analyze biological data it must first be
understood from a biological perspective. Goal: Refresh or achieve a better understanding of primary,
secondary, tertiary and quaternary protein structure. |
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Lecture 2: 10/01 Know Your Data -Principles of DNA and RNA structure [slides] |
To model and analyze biological data it must first be
understood from a biological perspective. Goal: Refresh or achieve a better understanding of the features
of DNA and RNA structure and its interaction with proteins. |
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Lecture 3:
10/06 Know the Limitations of Your Data - Experimental Methods of Structure Determination [slides] |
To effectively utilize biological data it is necessary to
understand the limitations of the experiments used to determine that data.
Structure determination is a relatively quantitative science and good
statistical measures exist. Goal:
Explore the quantitative and qualitative measures of data quality with
respect to data from X-ray crystallography, NMR and electron microscopy. |
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Lecture 4: 10/08 Know How Best to Represent Your Data - Data Representation [slides] |
Historically the PDB format expresses the Lingua Franca of structural bioinformatics,
but it has serious flaws. These will be explored and understood in the
context of the replacement - mmCIF. Goal: To understand why good data
representation is important. |
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Lecture 5: 10/10 Data Quality: The Annotation and Validation Process [slides] |
Public databases provide rich sources of data for all aspects
of bioinformatics study. Goal: To
understand the quality of these data through annotation and validation
practices using PDB and SwissProt as examples. |
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Lecture 6: 10/15 More on Data Representation – The Gene Ontology [slides] |
While a slight digression from structural bioinformatics,
the Gene Ontology (GO) is having a profound impact on bioinformatics
research. Goal: To understand the
structure and use of GO. |
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Lecture 7: 10/17 Applications of GO |
Study of research papers and subsequent discussion. Goal: To examine research applications of GO and what it means for biology. |
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Lecture 8: 10/22 Alternative Forms of Representation [slides] |
Most protein structure analysis is based upon the Cartesian atomic coordinates, but there are other forms of representation. We will consider a representation based on spherical harmonics and how it can be applied. Guest Lecturer: Dr. Apostol Gramada. |
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Lecture 9: 10/24 Classification is Always Ambiguous [slides] |
There is almost never a single answer when classifying biological
data. Goal: To understand this
statement by the analysis of techniques used to define protein domains from
3D structure. |
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Lecture 10: 10/29 Sequence-Structure-Function Relationships and Associated Reductionism [slides] * |
With so much data available it is necessary to produce
non-redundant sets for many bioinformatics tasks. However, given the complex
relationship between sequence, structure and function, non-redundancy means
different things in each case. Goal:
Understand this complex relationship and the associated meaning of
reductionism. |
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Lecture 11: 10/31 Reductionism and Classification Require Detailed Comparison [slides]* |
3D structure comparison is a difficult problem when trying
to achieve biologically meaning results. Goal:
Understand the problem and the methods used to address it and explore the use
of the distant sequence alignments arising from structure alignment. |
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Lecture 12: 11/05 From Reductionism comes New
Science [slides] |
New Science – traditionally protein structure has been studied through looking at evolution. Most recently evolution has been studied through looking at protein structure. Goal: Introduction to a new and exciting area. |
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Lecture 13: 11/07 Classification is Always Ambiguous – An Exception to the Rule? [slides] |
Secondary structure assignment may be an exception to the
rule. Goal: To explore the Kabsch-Sander algorithm and the impact it has had on the
community. Also to explore other methods of secondary structure assignment. |
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Lecture 14: 11/12 Studying Protein-Ligand Interactions [slides] |
One specific methodology for describing and searching for protein-ligand binding sites will be described along with how it is being used to study side effects and repositioning of existing pharmaceuticals. |
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Lecture 15: 11/14 Protein-protein interactions [slides] |
Goal: Understand the importance of the study of protein-protein interactions at the structural level. Review a paper in a journal club style that predicts interaction sites. |
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Lecture 16: 11/19 Protein Motion [slides] |
Protein motions can be vital for biological function. Motions range from complete disorder to subtle allosteric interactions. Goal: Understand methods for characterizing and predicting protein motion. |
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Lecture 17: 11/21 Bioinformatics in
Drug Discovery [slides] |
Review how bioinformatics is and could be used in the drug
discovery process. Guest Lecturer: Drs.
Peter Rose and Maria.Kontotianni |
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Lecture 18: 11/26 [slides] |
Structural genomics |
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Lecture 19: 12/3 [slides] |
Structural Immunology Lecturer: Dr. Julia Ponomarenko |
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Lecture 20: 12/5 |
Wrap-up and discussion of final |
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Finals 12/8-13 |
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