MEGA

MEGA performs phylogenetic and molecular evolutionary analyses of DNA and protein sequences to infer evolutionary relationships and estimate divergence times.

Key Features:

• Phylogenetic Analysis: Supports construction and analysis of phylogenies using maximum likelihood and other methods with parallel execution for large datasets.
• Timetree Inference: Implements the RelTime method and rapid relaxed-clock approaches to estimate divergence times, and supports node-dating and tip-dating with probability densities for calibration constraints and sequence sampling dates.
• Data Handling and Visualization: Assembles sequence datasets from files or web-based repositories and provides visualization of phylogenetic trees and evolutionary distance matrices.
• Batch Processing and Integration: Provides MEGA-CC (Computing Core) for batch processing of large numbers of datasets and integration with an analysis prototyper (MEGA-Proto) for automated analyses.
• Advanced Computational Methods: Incorporates methods for phylogenomics and phylomedicine, including rapid relaxed-clock methods for species, pathogens, and gene families.
• Machine Learning and Statistical Tools: Includes a Bayesian method for estimating neutral evolutionary probabilities from multispecies sequence alignments and machine learning techniques to test autocorrelation of evolutionary rates in phylogenies.
• Optimized Performance: Optimized for 64-bit computing with memory management improvements that reduce requirements for maximum likelihood analyses on very large datasets.

Scientific Applications:

• Molecular Evolution: Infers evolutionary relationships and patterns from DNA and protein sequence data.
• Phylogenomics: Builds phylogenies and timetrees for large-scale genomic and gene-family datasets.
• Phylomedicine: Applies timetree and evolutionary analyses to pathogens and medically relevant genes.
• Comparative Genomics: Compares homologous genes across species and aids prediction of gene duplication events.
• Divergence Time Estimation: Estimates divergence times across phylogenetic branching points using RelTime and relaxed-clock methods.
• High-throughput and Automated Analyses: Enables scripted and batch processing of large datasets via MEGA-CC and integration with automated workflows.

Methodology:

Maximum likelihood phylogenetic inference with parallel execution; RelTime divergence time estimation; rapid relaxed-clock methods; node-dating and tip-dating with probability densities for calibration constraints and sampling dates; assembly of sequence datasets from files and web repositories; batch processing via MEGA-CC and integration with MEGA-Proto; Bayesian estimation of neutral evolutionary probabilities from multispecies alignments; machine learning tests for autocorrelation of evolutionary rates; 64-bit optimization and memory management improvements for maximum likelihood analyses.