HMMCONVERTER 1.0: a toolbox for hidden Markov models.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Hidden Markov models (HMMs) are crucial for biological sequence analysis.
• Designing and implementing HMMs for novel applications is complex and error-prone.
• Existing methods require significant expert input for model architecture and parameter training.

Purpose of the Study:

• To present HMMConverter, a software package for easily setting up and training various HMMs.
• To automate the generation of efficient C++ code from user-defined XML specifications.
• To provide linear-memory algorithms for prediction and parameter training in HMMs.

Main Methods:

• Users define HMM architecture and algorithms via an XML file.
• HMMConverter translates XML into efficient C++ code.
• The package incorporates linear-memory prediction algorithms (e.g., Hirschberg) and novel training algorithms.

Main Results:

• HMMConverter supports probabilistic HMMs, pair-HMMs, and generalized HMMs.
• It offers computationally efficient, linear-memory algorithms for both prediction and parameter training.
• The software enables parameter training and data analysis for large datasets with minimal user effort.

Conclusions:

• HMMConverter significantly reduces the effort required to develop and apply HMMs in bioinformatics.
• It provides a robust and efficient solution for complex biological sequence analysis and parameter estimation.
• This tool empowers researchers to perform advanced analyses on large datasets more effectively.