Parameterization Studies for the SAM and HMMER Methods of Hidden Markov Model Generation

Proceedings Of The Fourth International Conference On Intelligent Systems For Molecular Biology

Volume

Proceedings Of The Fourth International Conference On Intelligent Systems For Molecular Biology

Contents

Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markov model approach offers a new, flexible method for the generation of multiple sequence alignments. The results of studies attempting to infer appropriate parameter constraints for the generation of de novo HMMs for globin, kinase, aspartic acid protease, and ribonuclease H sequences by both the SAM and HMMER methods are described.