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Related Concept Videos

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Updated: Oct 7, 2025

A Practical Guide to Phylogenetics for Nonexperts
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Deep distributed computing to reconstruct extremely large lineage trees.

Naoki Konno1,2,3, Yusuke Kijima1,4,5, Keito Watano1,2,3

  • 1Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.

Nature Biotechnology
|January 7, 2022
PubMed
Summary
This summary is machine-generated.

We developed FRACTAL, a deep distributed computing framework, to reconstruct large-scale cell lineages and evolutionary trees from millions of sequences. This scalable method significantly enhances phylogenetic analysis for complex biological systems.

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Area of Science:

  • Computational Biology
  • Genomics
  • Evolutionary Biology

Background:

  • Phylogeny estimation reconstructs evolutionary trees and is applied to CRISPR-based cell lineage tracing.
  • Current computational methods struggle with the massive datasets generated by these applications.
  • Scalability is a major limitation in reconstructing large, complex lineages.

Purpose of the Study:

  • To present FRACTAL, a deep distributed computing framework designed to overcome scalability limitations in lineage estimation.
  • To enable accurate phylogenetic tree construction from extremely large numbers of input sequences.
  • To enhance the capabilities of existing lineage estimation software.

Main Methods:

  • FRACTAL utilizes a deep distributed computing framework.
  • It reconstructs upstream lineages and recursively processes downstream lineages on independent computing nodes.
  • The framework is designed for high scalability and accuracy.

Main Results:

  • FRACTAL successfully reconstructed lineages from over 235 million simulated sequences.
  • It also processed over 16 million cells from a simulated CRISPR experiment.
  • The framework demonstrated utility in evolutionary tree reconstruction and large-scale sequence diversification experiments using error-prone PCR.

Conclusions:

  • FRACTAL significantly enhances the scalability of lineage estimation software.
  • The framework enables accurate phylogenetic reconstruction from massive datasets.
  • FRACTAL has broad applications in cell lineage tracing, evolutionary biology, and sequence diversification studies.