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Novel Methods to Visualize Genome Evolution: Insights into Early Life on Earth

This project emphasizes innovative techniques for tracing the evolution of life on Earth and metabolic pathways' assembly. Last year, we developed a new algorithm for selecting gene families and created a tool for visualizing genome mosaicism via Self-Organizing Maps (SOM). Our work supports NASA's astrobiology objectives, helping to understand past life interactions with environmental changes. Plans for the next year include applying the SOM algorithm to new genomes and exploring advanced analysis methods like Principal Component Analysis (PCA) and Local Linear Embedding (LLE).

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Novel Methods to Visualize Genome Evolution: Insights into Early Life on Earth

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  1. Exploration of Novel Methods to Visualize Genome Evolution Project Summary Last Year’s Highlights Reconstruction of the early history of life on Earth, including the order in which metabolic pathways were assembled. • Development of the new algorithm to select gene families http://bioinformatics.org/branchclust/ • Creation of software tool for the visualization of genome mosaicism from SOM maps. J. Peter Gogarten University of Connecticut gogarten@uconn.edu Lutz Hammel University of Rhode Island Maria Poptsova University of Connecticut Neha Nahar University of Rhode Island Plans for Next Year • Applications The developed tools facilitate the analysis of microbial genomes, especially with respect to the early evolution of life and the evolution of metabolic pathways. The work addresses questions central to NASA’s astrobiology program: “Understand how past life on Earth interacted with its changing planetary and Solar System environment” and “Understand the evolutionary mechanisms and environmental limits of life”. • Apply the SOM algorithm for embedded quartets. • Explore Principle Component Analysis (PCA) and Local Linear Embedding (LLE) algorithms. • Apply the analyses to different selection of genomes, with emphasis on those groups that might allow correlation to the fossil and geological records Keywords: Self Organizing Maps, Local Linear Embedding, Molecular Phylogeny, Early Evolution of Life, Astrobiology, Comparative Genome Analysis NASA AISRP 2004-07 http://gogarten.uconn.edu/AISRP

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