seq++: analyzing biological sequences with a range of Markov-related models

Area of Science:

• Bioinformatics and Computational Biology
• Sequence Analysis
• Statistical Modeling

Background:

• Biological sequence data analysis requires robust computational tools.
• Existing software may lack flexibility in handling diverse alphabets and advanced statistical models.
• There is a need for an extensible library for sequence statistics.

Purpose of the Study:

• To introduce the seq++ package, a comprehensive library for sequence statistics.
• To highlight its capabilities in handling various sequence alphabets and Markov models.
• To provide a tool for studying biological processes through sequence modeling.

Main Methods:

• Development of the seq++ package with an extensible library.
• Implementation of support for multiple sequence alphabets (nucleotides, amino acids, codons).
• Integration of various Markov models, including variable-length and parsimonious Markov models.
• Inclusion of simulation modules for Monte Carlo methods.

Main Results:

• The seq++ package offers a reference set of programs and an extensible library.
• It handles sequences described with any alphabet, enhancing its generality.
• Supports advanced sequence modeling with phased Markov models.
• Provides simulation modules for Monte Carlo methods.

Conclusions:

• The seq++ package is a valuable, general-purpose toolbox for sequence statistics.
• It empowers biologists and developers to model diverse biological processes.
• Its flexibility with alphabets and Markov models facilitates advanced sequence analysis.