show_viterbi

show_viterbi computes the most probable sequence of hidden states in a Hidden Markov Model using the Viterbi algorithm to segment and interpret biological sequences for applications such as gene prediction and protein structure analysis.

Key Features:

• Viterbi Algorithm Implementation: Implements the Viterbi dynamic programming algorithm to compute the most likely path of hidden states given an observed sequence in an HMM.
• Integration with HMM Parameters: Integrates with show_emfit, which estimates HMM parameters via the Expectation-Maximization (EM) algorithm, enabling parameter estimation prior to Viterbi decoding.
• Application in Genomic Analysis: Applies state-path inference to genomic sequences, supporting analyses of genes, regulatory mechanisms, metabolic pathways, and adaptive strategies in organisms such as Lactobacillus sakei.

Scientific Applications:

• Genomic Sequencing Analysis: Analyzes gene sequences and predicts functional elements from microbial genomes, exemplified by Lactobacillus sakei’s 1,884,661-base-pair circular chromosome.
• Metabolic Pathway Exploration: Identifies probable metabolic pathways and specialized metabolic repertoires from sequence-derived state assignments.
• Adaptation Studies: Infers sequences of genetic or regulatory states relevant to organismal adaptation to environmental stresses, including changes in redox and oxygen levels during food processing.
• Biopreservation and Food Safety: Supports investigation of genetic bases for resilience and adaptation in Lactobacillus sakei relevant to meat biopreservation and food safety.
• Biofilm Formation Studies: Helps identify genes involved in biofilm formation through sequence-based state inference to study microbial colonization.

Methodology:

Model initialization using show_emfit to estimate HMM parameters from observed data, followed by application of the Viterbi algorithm to compute the most probable sequence of hidden states given the observations.