Mark Gerstein, Yale University

1
BIOINFORMATICSSummary

• 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,
http//www.sciencecartoonsplus.com
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
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
• 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

• 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/ChipCoop
er/cartoon26.html)