1. Secondary Structure Prediction. Implement a secondary structure prediction method and compare its accuracy to known methods. Common choices for your implementation include: Neural Networks, Hidden Markov Models, and possibly decision trees.
  2. Structure comparison methods. Review the literature on 3D structure comparison. Implement at least one algorithm. Input: 2 three-dimensional structures, output: a measure of distance (typically root-mean-square deviation), and a list of equivalent residues.
  3. Bio-Ethics. Bioinformatics deals with biological and medical data, according there are numerous related ethical issues: should patenting genes be allowed? how to handle patient data? how to deal with genomic data, imagine that the analysis of a data set allows to draw conclusions about a population, a religious group, people who live in a specific region, etc. The consequences can be sever: it could be that this group will be more likely to suffer from certain diseases, such information could be used by insurance companies, employers, etc. to screen candidate.
  4. Simultaneous alignment and structure prediction for two RNA sequences. Implement a simplified version of dynalign, where the secondary structure prediction is calculated using the Nissinov algorithm; i.e. finds the maximum number of base pairs.