eShadow

eShadow identifies conserved functional elements in closely related nucleotide and protein sequences by applying phylogenetic shadowing to multiple sequence alignments, with emphasis on human-to-primate and mouse-to-rat comparisons.

Key Features:

• Identification of Selective Pressure Elements: Detects genomic regions under selective pressure from multiple sequence alignments between closely related species such as human-to-primate and mouse-to-rat comparisons.
• Integration of Statistical Methods: Implements two distinct statistical approaches to generate conservation profiles that enhance prediction accuracy and reliability.
• Dynamic Visualization: Produces dynamic visualizations of conservation profiles derived from multiple sequence alignments to represent conservation patterns across sequences.
• Optimization Module with Hidden Markov Model (HMM): Includes an optimization module to train an underlying Hidden Markov Model for predicting functional sequences.
• Flexibility Across Divergence Rates: Uses a trainable HMM to accommodate sequence comparisons with varying degrees of evolutionary divergence.

Scientific Applications:

• Functional Genomics: Identifies conserved regions under selective pressure to support discovery of genes and regulatory elements.
• Evolutionary Biology: Analyzes sequence conservation across species to provide insights into evolutionary relationships and mechanisms of genomic change.
• Comparative Genomics: Enables detailed comparisons between closely related genomes to study genetic variation and its implications for phenotype and disease susceptibility.

Methodology:

Applies phylogenetic shadowing to multiple sequence alignments, uses two statistical approaches to produce conservation profiles, and employs an optimization module to train a Hidden Markov Model.