Bayesian Bioinformatics Applications:
BALSA - Bayesian Algorithm for Local Sequence Alignment
BALSA is a Bayesian algorithm for local sequence alignment that takes into account the uncertainty associated with all unknown variables by incorporating in its forward sums a series of scoring matrices, gap parameters and all possible alignments.
Bayesian Phylogenetic Footprinting
The selection of a scoring matrix and gap penalty parameters continues to be an important problem in sequence alignment. The Bayesian Phylogenetic Footprinter bypasses this requirement. Instead of requiring a fixed set of parameter settings, this algorithm returns the Bayesian posterior probability for the number of gaps and for the scoring matrices in any series of interest.'
Gibbs Motif Sampler
The Gibbs Motif Sampler will allow you to identify motifs, conserved regions in DNA or protein sequences.
Block Gibbs Sampler for RNA Prediction
RNAG is a global RNA secondary structure alignment program. It is a blocked Gibbs Sampler for predicting consensus secondary structure of unaligned RNA sequences. As such, it has a theoretical advantage in convergence time. The algorithm iteratively samples from the conditional probability distributions P(Structure | Alignment) and P(Alignment | Structure). The samples drawn from this algorithm are used to more characterize the posterior space of structures and to assess the uncertainty of predictions.
Exact Bayesian Inference in Regression
EBIR is an exact Bayesian algorithm applicable to both variable selection and model averaging problems. It employs a fully Bayesian approach that provides a complete characterization of the posterior ensemble of possible sub-models and consequently, the marginal probability of including each of the predictor variables when the number of variables is not too large. Thus, this fully Bayesian model can be used for variable selection, model averaging applications, and examination of the shape of the posterior space.
Please visit the Center for Computational Molecular Biology