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Evolutionary Relationships through Genome Comparisons

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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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Related Experiment Video

Updated: Jun 4, 2025

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers MADM

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers MADM

Published on: May 8, 2020

Reconstructing Progenitor State Hierarchy and Dynamics Using Lineage Barcoding Data.

Weixiang Fang^1,2, Yi Yang^1,2, Hongkai Ji³

¹Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Methods in Molecular Biology (Clifton, N.J.)

|January 2, 2025

Summary

This summary is machine-generated.

Quantitative Fate Mapping (QFM) uses lineage barcoding to track cell development. This computational pipeline analyzes progenitor cell dynamics and fate restriction, providing a framework for understanding cell fate changes.

Keywords:

ICE-FASE Lineage barcoding pipeline Lineage tracing Phylotime Progenitor state dynamics Quantitative fate mapping (QFM)Time-scaled cell phylogeny

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Characterizing Mutational Load and Clonal Composition of Human Blood

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Published on: July 11, 2019

Area of Science:

Developmental Biology
Computational Biology
Genetics

Background:

Single-cell measurements of cell phylogeny using lineage barcodes are becoming more feasible.
Understanding progenitor cell dynamics and fate restriction is crucial for developmental studies.

Purpose of the Study:

To describe Quantitative Fate Mapping (QFM) and its computational pipeline.
To enable the interrogation of progenitor cell dynamics and fate restriction during development.

Main Methods:

Inferring cell phylogeny using the Phylotime model.
Reconstructing progenitor state hierarchy, commitment time, population size, and commitment bias with the ICE-FASE algorithm.
Evaluating sampling adequacy using progenitor state coverage statistics.

Main Results:

The Phylotime model infers cell phylogeny from lineage barcoding data.
The ICE-FASE algorithm reconstructs key progenitor cell dynamics.
Progenitor state coverage statistics are essential for interpreting QFM results.

Conclusions:

QFM provides a general framework for characterizing cell fate dynamics.
Lineage barcoding data, combined with computational tools, allows detailed analysis of developmental processes.
This approach facilitates a deeper understanding of cell commitment and population dynamics.