Home

AMIA 10x10 Partner Programs

AMIA 10x10 Stanford University

10x10 at Stanford | Course Description | Beyond 10x10 | Why Stanford? | Register Here

Course Description

Curriculum and Competencies

Methods for Bioinformatics

Representations and Algorithms for Computational Molecular Biology

This course will introduce the basic computational issues and methods used in molecular biology though a series of core lectures. At the completion of the course, participants will have a basic understanding of how various computational algorithms have been used to solve key biological problems by the manipulation of biomedical data sources.

The course will introduce and use biological data sources available on the World Wide Web. Topics will include 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, computing with distance information, 3D motif definition and computation, hidden Markov models, phylogenetic trees, statistical feature detection, genetic algorithms, design of data resources, automated analysis of biological literature, database integration, and collaborative environments for supporting biology.

Overview of the topics covered in the program:

1. Introduction to Bioinformatics and Computational Genomics

2. Dynamic Programming Sequence Alignment

3. Intro to Microarrays

4. Microarrays, Clustering and Classification

5. Basic 3D Cmputation and Structural Alignment

6. 1D & 3D motifs

7. Multiple Sequence Alignment

8. Genome, Hapmap, SNPs, Phenotypes

9. Hidden Markov Model

10. Gibbs Sampling

11. Molecular Energetics and Dynamics

12. Protein Structure Prediction: homology modeling and ab initio

13. Fold recognition and Introduction to RNA

14. RNA Folding

15. Phylogenetics

16. Comparative Genomics

17. Natural Language Processing in Biology

Prerequisite

Participants should have previous coursework in biology since the course will quickly move through many biology topics. We recommend a course that provides the following information: Covers macromolecules (proteins, lipids, carbohydrates, and nucleic acids) and how their structure relates to function and higher order assembly; molecular biology, genome structure and dynamics, gene expression from transcription to translation. It may be useful to have a textbook of molecular biology to reference during the course, for those with less experience with biological concepts.

Complete Curriculum

The table below lists the course lecture topics and associated learning objectives. Each lecture is 75 minutes long and is presented by Professor Russ Altman.

Complete Curriculum

1. Introduction to Bioinformatics and Computational Genomics Introduction to bioinformatics approaches, overview of biological data and databases, types of bioinformatics problems covered in the course
2. Dynamic Programming Sequence Alignment Understanding of the fundamental algorithm used in sequence alignment, differences between global and local alignment, differences in implementation and effect on performance with implications for use. Examples include Needleman-Wunsch, Smith-Waterman, BLAST, FASTA, etc…
3. Intro to Microarrays Overview of microarray construction, usage and data collection. Biological interpretation of microarray data and implications for analysis. Data grouping and feature reduction as strategies for data analysis. Introduction to microarray data repositories and resources.
4. Microarrays, Clustering and Classification Review of algorithms for microarray analysis including hierarchical clustering, self-organizing maps, supportvector machines. Methods to classify results, e.g. GO terms
5. Basic 3D Computation and Structural Alignment Motivation for experimental and computational structural studies. Review of 3D structures for biological macromolecules. Review of repositories and resources for 3D structures and structural analysis.
6. 1D and 3D motifs Using highly conserved biological features (motifs) to identify related sequences.

Overview of conserved structures. Using highly conserved structures to align sequences, derive physical properties of amino acids.
7. Multiple Sequence Alignment Methods for aligning multiple sequences, including multi-dimensional dynamic programming and progressive alignment methods.
8. Genome, Hapmap, SNPs, Phenotypes Beyond the genome: Single nucleotide polymorphisms (SNP's), allelic variants, and phenotypes, linkage disequilibrium and population studies. Computational approaches to inferring variants from the Hapmap project, and linkage disequilibrium, data repositories and resources for allelic variants and phenotypes.
9. Hidden Markov Models The Hidden Markov Model with applications in sequence alignment, protein structure classification, etc…
10. Gibbs Sampling The Gibbs sampling algorithm applied to motif-based searches.
11. Molecular Energetics and Dynamics Biological implications of macromolecular motion. Study of protein structure and motion using physics-based approach, e.g. simulation.
12. Protein structure prediction: homology modeling and ab initio Biological implications of macromolecular structure. Review of major approaches to protein structure prediction.
13. Fold recognition and Intro to RNA Algorithms for prediction of protein structure based on recognizing the compatibility of a sequence with a 3D structure.

Introduction to RNA Folding
14. RNA Folding Importance ofRNA structure and function. Bioinformatics approaches to RNA structure and function prediction.
15. Phylogenetics Methods for creating phylogenetic trees, such as the tree of life using DNA sequences.
16. Comparative Genomics Whole genome alignment and analysis
17. Natural Language Processing in Biology Extracting information from the literature using automated approaches. Machine readable controlled terminologies and ontologies, e.g., the Gene Ontology

Logistics

The AMIA 10x10 program at Stanford University course consists of 10 weeks of online training. The delivery method includes web-based streaming video which is composed of video of the presentations and the accompanying Powerpoint slides. The streaming video of the course will be supplemented by interactions with course faculty and TAs. The course will be provided through the Stanford Center for Profession Development.

Reading assignments consist of chapters from the required textbook:

Mount, D.W., Bioinformatics : sequence and genome analysis. 2nd edition (July, 2004), Cold Spring Harbor Laboratory Press. ISBN: 0879696877.

Other Recommended books that are more focused:
Kohane, I.S., Kho, A., Butte, A.J., Microarrays for an Integrative Genomics (Computational Molecular Biology). 2002, MIT Press. ISBN: 026211271X.

Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G., Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids. 1999, Cambridge Univ Pr. ISBN: 0521629713

Bourne, P.E., Weissig, H. (editors), Structural Bioinformatics. 2004, John Wiley & Sons. ISBN: 047120199

Schedule

This 10x10 course offering, in partnership with the Stanford University and the Stanford Center for Professional Development, will start on April 1, 2008, and cover two lectures per week (based on the above schedule of lectures) ending June 6th. The registration deadline is March 28, 2008. Enrollments will be accepted after that date on a space-available basis.

Registered students will receive a free one day of tutorials at the AMIA 2008 Annual Symposium (two half-day or one full-day tutorial).