1. BIOINFORMATICS Summary
Mark Gerstein, Yale University
gersteinlab.org/courses/452
(last edit in fall '06, handout version, including in-class changes)

2. Used in class M11 [2006,12.06]

3. You'll Forget…
[From S Harris's Science Cartoons,

4. … So I'll distill

5. What is Bioinformatics?
(Molecular) Bio - informatics
One idea for a definition? Bioinformatics is conceptualizing biology in terms of molecules (in the sense of physical-chemistry) and then applying “informatics” techniques (derived from disciplines such as applied math, CS, and statistics) to understand and organize the information associated with these molecules, on a large-scale.
Bioinformatics is “MIS” for Molecular Biology Information. It is a practical discipline with many applications.

6. Data Types
Sequences
Structures
Functional Genomics

7. "Core" Bioinformatics Core Stuff
Computing with sequences and structures
protein structure prediction
biological databases and mining them
New Stuff: Networks and Expression Analysis
`Will teach these in CS 545 (Data Mining) next semester
Fairly Speculative: simulating cells

8. Hierarchical Structure of Course Information
Memorize the previous summary
Good familarity with main points in lectures (quizzes)
Rest of overheads and readings for reference on projects and …

9. Cross-cutting Themes Algorithms for Comparison Predictions
Dynamic programming
Different measures of similarity (RMS vs. Structural similarity; PAM & Blossum vs %ID)
Generalized similarity matrix in threading
Statistical scoring schemes (with P-values)
For sequences, structures, sequence to structure, and even expression data
Time complexity of the comparisons
Predictions
LOD scores (# with features / expectation )
Progressive more complex features
Amount of features information IN vs. prediction OUT
Testing against benchmarks with cross-validation (sec. struc. prediction, seq. comparison scoring, datamining)
Other methods, need for heuristics

10. Cross-cutting Themes
Increasing the chemically reality and complexity of genes
Character strings, fold (just CAs), volumes and surfaces from all atom representation, energy and minimization, dynamics (time and velocity)
Simulation
Vector configuration boiled down a scalar E through potential
Compute intensive exploration of configurations (MC, MD)
Averages over correctly weighted configurations
Importance of simplification
The Survey Mode
Collecting information in DB tables
Importance of integration and interoperation
Organizing it around "part" classifications
Surveying it for useful statistics (taking into account biases)
Doing datamining to find more tenuous relationships

11. Anti-Themes

12. Depth v Breadth

13. Historical Perspective
1980
2005
2000
1990
1985
1995
Single Structures
Modeling & Geometry
Forces & Simulation
Docking
Sequences, Sequence-Structure Relationships
Alignment
Structure Prediction
Fold recognition
Genomics
Dealing with many sequences
Gene finding & Genome Annotation
Databases
Integrative Analysis
Expression & Proteomics Data
Datamining
Simulation again….

14. (from CooperToons, http://members.aol.com/ChipCooper/cartoon26.html)