10x10 with Stanford University
Representations and Algorithms for Computational Molecular Biology
The 10x10 at Stanford University will introduce the basic computational issues and methods used in molecular biology, including an overview of biological data sources available on the web.
10x10 with Stanford: Course Description
Curriculum and Competencies
Methods for Bioinformatics
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:
- Introduction to Bioinformatics and Computational Genomics
- Dynamic Programming Sequence Alignment
- Intro to Microarrays
- Microarrays, Clustering and Classification
- Basic 3D Computation and Structural Alignment
- 1D & 3D motifs
- Multiple Sequence Alignment
- Genome, Hapmap, SNPs, Phenotypes
- Hidden Markov Model
- Gibbs Sampling
- Molecular Energetics and Dynamics
- Protein Structure Prediction: homology modeling and ab initio
- Fold recognition and Introduction to RNA
- RNA Folding
- Phylogenetics
- Comparative Genomics
- 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.
| 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, support vector 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 of RNA 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 June 21st and cover two lectures per week (based on the above schedule of lectures) ending mid-August, 2012 The registration deadline is July 1st. Enrollments will be accepted after that date on a space-available basis.
Students will receive a complimentary membership to AMIA for the 2012 calendar year (a $300 value) which is good from Jan. 1st to Dec. 31st and includes a subscription to JAMIA and all back issues for 2012, and also be eligible for a 10x10 completion certificate.
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