Biology 39/139: Computational Molecular Biology Syllabus
In addition to reading primary papers, there will be one text book recommended: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, 3nd Edition (Paper), by Andreas D. Baxevanis, B. F. Francis Ouellette, John Wiley & Sons, Inc., Third Edition, 2005, ISBN: 0-471-47878-4. This book is available at Wheelock Books. This book is to be used mainly for reference during the term. It will help you understand some of the material we go over in class and will provide a different perspective.
Grading will be based on a series of homework assignments and the term project. The homework will consist of solving computational biology problems and many will be typical of those faced in a research situation. The homework assignments will range in difficulty and length depending on the subject matter. Because of the nature of the subject area, these homework assignments will take the place of formal exams. In fact, the homework assignments may be thought of as a series of take home exams and will be graded accordingly.
| Grade Distribution | ||
| homework#1 (tools) | 80 points | |
| homework#2 (algorithms) | 100 points | |
| homework#3 (analysis) | 130 points | |
| homework#4 (scoring tables) | 130 points | |
| homework#5 (alignment and searches) | 100 points | |
| term project | 360 points | |
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total:
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900 points | |
Instructions for submitting homework. Note that the term project is due in class at 10 AM on the last day of class (March 10).
| Date | Day | Topic (click links to see notes/slides) | Homework Assigned | Homework Due |
| Jan 6 | Tue | Introduction to Computational Biology -- An introduction to the course and an overview of the field. slides | ||
| Jan 8 | Thu | The Tools of Computational Biology -- A discussion of the tools that will be used in the course and that are available on the web. Class will meet in the Starr Instructional Center in Berry Library -- Level 2 for hands-on training. You will also be able to initiate work on your term project. | ||
| Jan 13 | Tue | |||
| Jan 15 | Thu | Programming and Algorithms -- How does it all work? What is a good algorithm? Considering speed and accuracy. slides |
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| Jan 20 | Tue | Finding Genes -- With all of the DNA sequences available, how can we determine what is a gene? slides |
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homework#1
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| Jan 22 | Thu | Bioinformatics Databases -- An exploration of online databases. What kinds of information is available and how are the databases related? slides pt1, slides pt2 Kristen Anton - Director, Bioinformatics, DMS |
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| Jan 27 | Tue | Analyzing Sequences (3) -- A look at many of the common analyses. What are their strengths and weaknesses? When should each analysis be used? How can results be interpreted? Neural Nets. Markov Modeling. slides |
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homework#2
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| Jan 29 | Thu |
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| Feb 3 | Tue |
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| Feb 5 | Thu | Comparing Sequences (3) -- How do we measure sequence similarity? How can we tell when sequences are truly related? How are multiple sequence alignment results interpreted? The power of dot matrix comparisons. Searching databases for similar sequences. slides |
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| Feb 10 | Tue |
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homework#3
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| Feb 12 | Thu |
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| Feb 17 | Tue | Multiple Sequence Alignments, Finding Patterns -- ClustalW, MEME, Meta-MEME, Profiles, Gibbs Sampling, etc. |
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| Feb 19 | Thu | Cellular Computing - Albert Erives slides |
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| Feb 24 | Tue | Computational Genomics -- Comparing lancelet to vertebrate genomes slides |
homework#4
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| Feb 26 | Thu | Semantic Similarity -- Another way to consider how similar two sets of data are to each other and to a complete dataset - Viktor Martyanov slides |
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| Mar 3 | Tue | Gene Expression Analysis -- Analyzing gene expression data. SAGE data, DNA microarrays. What questions can be answered? Data manipulation and clustering. Mike Whitfield (Mar 3) slides |
homework#5
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| Mar 5 | Thu | More gene expression analysis. slides |
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| Mar 10 | Tue | term project presentations (5-10 minutes each); course wrapup/evaluation | term project due by 11:59 PM |
Homework assignments will be explained in class and will be posted on the web. You can see the assignment by clicking on the links in the table above as the homeworks are assigned. All assignments are to be handed in as electronic documents (instructions are given in each assignment) by 10 AM on the due date (before class). Any files placed after the 11:59 PM deadline will have 10% deducted. An additional 10% will be deducted for each 24 hours late.
I am not setting up any formal office hours. Please feel free to drop in to ask questions at any time. My office is Remsen 210. If I am free when you stop in I will be glad to help you right then. On the other hand, if I am busy, please don't take it personally if I ask you to come back at another time -- it just means that I am busy. This "open door" policy will hopefully work best for all of us. If you prefer to set up an appointment, please call (x6-2059) or email me to see when I will be available. I'd like to talk to you often during the term about your term projects and what you are discovering.
Any student with a documented disability needing academic adjustments or accommodations is requested to speak to me and give me a copy of your accommodations form by the end of the second week of the term. All discussions will remain confidential, although the Director of Student Disabilities may be consulted if questions arise. I want to work with you to put appropriate accommodations in place, but can only do so if you let me know there is a need.
At the beginning of the term, you will be given an unknown genomic sequence to analyze. As we learn new techniques, you should apply them to your sequence to try and understand what your sample DNA. You should also explore the web for any kind of analysis you might find useful. During the term you should stop in regularly to talk with me about your progress and to discuss what to do next -- this should be a collaborative effort! At the end of the term, you will hand in a report detailing what you have learned about your sequence. The report should include specifics on the methods/algorithms you used, what the results of each analysis was and what conclusions you were able to draw about your sequence from each analysis. More details are available on the unknowns.
This year, because of the genome we are exploring (Branchiostoma floridae) and its role at the origin of vertebrates, you should also include comparisons of your unknown to corresponding genes in different vertebrate.
The Dartmouth College Student Handbook (page iii) states "Fundamental to the principle of independent learning are the requirements of honesty and integrity in the performance of academic assignments, both in the classroom and outside. Dartmouth operates on the principle of academic honor, without proctoring of examinations. Students who submit work which is not their own or who commit other acts of academic dishonesty forfeit the opportunity to continue at Dartmouth."
The provisions of the Academic Honor Principle are published in the Student Handbook and the bulletin of Organization, Regulations, and Courses. This Principle is an important part of your Dartmouth experience. Honesty is the foundation of the academic pursuit of knowledge. In recognition of this, the faculty and staff of Biology 15 will not overlook any violations of the Academic Honor Principle. Indeed, the Faculty Handbook of Dartmouth College states explicitly that College Faculty are obligated to report potential violations of the Academic Honor Principle to the Dartmouth College Committee on Standards.
You are encouraged to talk about different algorithms with others in the class or with me as they apply to your term project, but the homework and term project that you hand in should be entirely your own. Some of the homework assignments include developing algorithms... these should be developed entirely by you and NOT in consultation with others. Part of the purpose of the homework is to make you clearly think through some of the tasks. We may go over some of the homework in class to discuss different ideas that surface.
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