- 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.
- 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.
- 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.
- 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.