Bioinformatics Teaching, Subjects, Courses

Bioinformatics, Teaching

International Society for Computational Biology (ISCB): [training]
Pacific Symposium on Bioinformatics (PSB): [www]
Inc' a description of a Bioinf' [subject] c1996 for (non-bio') Computer and Information Science Students, by Russ Altman and John Koza at Stanford (see below).
19 x 75 minute lectures, 6 x weekly assignments (1-3 hours each) from gathering data from the web to computing with HMMs, 3 x projects (to 20 hours) from a DPA algorithm to protein threading.
1. Intro, biol, entropy, energy, DNA, protein, 3D
2. Seq' alignment, DPA
3. Protein structure, 2ndry, 3ry, amino acids, folding
4. 3D structure, dihedrals, bond angles
5. Combinatorics of protein foldinh, simplification
6. Protein structures as mini computers: hemoglobin
7. Analysis of sequences, composition, coding regions, itrons/exons
8. Alternative representations: profiles, reg' exps, belief networkds
9. Simulating molecular dynamics (proteins)
10. Processing distances algorithmically and graphically
11. Transmembrane proteins, GA's, GP
12. Hidden Markov Models and multiple alignment
13. Extracting non-atomic features from protein structure
14. Computing molecular surfaces and volumes
15. Secondary structure predition
16. Genomic computing: fragment assembly
17. Minimum length principle (inductive inference MML / MDL) and evolutionary trees
18. Rep' storing and comp' metabolic pathway info'
19. Constraint satisfaction e.g. ribonucleoprotein complexes

J. Bioinformatics: [14(7)] A Curriculum for Bioinformatics: The Time [1998] is Ripe.

Pairwise sequence alignment (dynamic programming, heuristic methods, similarity matrices)
Multiple sequence alignment
Hidden Markov Models (construction, use in alignment, prediction)
Phylogenetic Trees
Fragment and map assembly and combinatorial approaches to sequencing
RNA Secondary structure prediction
Sequence feature extraction/annotation
Protein homology modeling
Protein threading
Protein molecular dynamics
Protein ab initio structure prediction
Integration of molecular biology databases
Support of laboratory biology (sequencing, structure determination, DNA arrays, etc.)
Design and implementation of biological databases/knowledge bases.

In addition, the computer science and statistics sections of the curriculum should introduce the student to certain key technologies that are commonly used within bioinformatics, including:

Optimization (Expectation Maximization, Monte Carlo, Simulated Annealing, gradient-based methods)
Dynamic programming
Bounded search algorithms
Cluster analysis
Classification
Neural Networks
Genetic Algorithms
Bayesian Inference
Stochastic Context Free Grammars.

Sloan Foundation: [www] report (1999), funding, hiring patterns.

Russ Altman: [www] Stanford Bioinf' Core courses in bioinformatics:

[Representations and Algorithms for Computational Molecular Biology] (Medical Information Science 214/Computer Science 274). A programming course, introduces nuts and bolts of basic algorithms.
[Computer Applications in Molecular Biology] (Biochemistry 218). A course for biologists, introduces key ideas in bioinformatics.

[...] accessing molecular databases, pattern search, classification of sequence and structure, alignment of sequences, rapid similarity searching, phylogenies, automated pattern learning, representing protein structure, modeling protein structure by homology, protein-protein docking and protein-ligand docking.[...]
[Protein Architecture, Dynamics and Structure Prediction] (Structural Biology 228). Introduces the basic concepts of molecular structure and how to compute with molecular structure.
[(Mathematical and Computational Molecular Biology] (Math/Bio 227). A course on the mathematical foundations of many approaches in bioinformatics, is inactive at this time.)
- 3/2000

Stanford Center for Prof' Dev', Certificate Program in Bioinformatics,

Computational Molecular Biology [MIS231/BioChem218], 3 units, (as of 3/2000)
Topics: Introduction to the internet, accessing molecular databases, pattern search, classification of sequence and structure, alignment of sequences, rapid similarity searching, phylogenies, consensus sequencing, physical mapping of DNA and genomes, representing protein structure and modeling protein structure by homology. The final project should utilize or analyze the methods presented.
Structural Biology 228 Protein Architecture, Dynamics and Structure Prediction, 3 units.
[CS274/MIS214] Representations and Algorithms for Computational Molecular Biology, 4 units (as of 3/2000):
Topics
Basic algorithms for alignment of biological sequences and structures, as well as more advanced representational and algorithmic issues in structure and sequence computation. These include, for example, dynamic programming algorithms for alignment, structural superposition algorithms, simplified representations, probabilistic representations of structural uncertainty, hidden Markov models, Bayesian networks, statistical feature detection, genetic algorithms, constraint satisfaction and minimum description length encoding. The guest lectures will, in part, showcase different computational approaches being pursued by research groups in several departments at Stanford.

- sources via Larry Hunter