HST.508 | Fall 2002 | Graduate, Undergraduate

Genomics and Computational Biology


Project Background

The full written project in the form of an article or grant proposal as well as the figures ready for (Microsoft® Powerpoint® or Microsoft Word) presentation is due the day of lecture 12. We recommended that you start choosing a topic and team before the end of lecture 7.

The oral presentation will be limited to 6 minutes per person on each project team. This will give us 2 minutes (per person) for questions at the end. The presentations will be loaded on the computer in order of the schedule below, unless special requests are made.

Each team must have at least one computational “result”. This can be as simple as checking a table in a published article or as complex as a new computational-biology algorithm and associated graphics.

There should be critical assessment of at least one previous relevant article.

Please cite and link pubmed or web references wherever possible.

The role that each member played in the team should be clearly stated in the written version. Each team member should present a substantial contribution orally, not merely introduce the final speaker(s).

The overall course grade will be 12% per problem set and 28% for the project.

The late policy is 5% (of 100%) off per day after the deadline of lecture 12 at noon. (If you are in the first group, you should get your slides emailed to us and confirm functioning in our hands by the end of lecture 12.)

Grading Rubric

This rubric is designed to be as explicit as possible to ensure that all students are graded consistently. Each component of the project will be graded on a scale from 1 to 5. The scale is explicitly defined for each component but is roughly as follows: 1 = poor, 2 = needs improvement, 3 = good, 4 = excellent, 5 = outstanding. Download complete rubric file. (PDF)

2002 Project Topic Ideas

  1. Protein-Protein Interactions: Network Structures.

  2. To correlate microarray data with the promoter site consensus sequence for a specific transcription factor.

  3. Genomic analysis of parasitic human pathogens, particularly Plasmodium falciparum, and Leishmania major.

  4. Simulation of the recombination of antibody genes by using perl to predict the amino acid sequences of the variable region of the antibody.

  5. Dynamic Programming analysis of Th2 chemokine receptors and ligands nucleotide and protein sequences.

  6. The Determination of a General Set of Fine-Grained Selection Criteria for the Discovery of siRNA in Humans.

  7. How to we manage cases in which conflicting, contradicting or “speculative” functional predictions are contributed by the various information sources used to build a network model.

  8. Develop an engine (or program) to predict protein function on context basis (non-homologous approach).

  9. TNF Receptor Biomining.

  10. Comparing Variable Selection Methods for Microarray Classification Models Based on Logistic Regression.

  11. Using the Index of Coincidence to identify Open Reading Frames.

  12. Transcriptional control mediated by cleansing of short sequences from gene regulatory regions.

  13. Software solution that provides a visual interface to nucleotide mutations.

  14. Identification of Potential Transcriptional Regulatory Elements by Comparison of Human and Pufferfish Genomic Sequences.

  15. Overlaying Clustering Results from PCA with Clustering Results from Self-Organizing Maps.

Course Info

As Taught In
Fall 2002