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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: Sep 21, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

COLT: Constrained Lineage Tree Generation from Sequence Data.

Keke Chen¹, Venkata Sai Abhishek Gogu¹, Di Wu²

¹Data Intensive Analysis and Computing Lab Department of Computer Science and Engineering Wright State University, Dayton, Ohio, USA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine

|May 27, 2022

Summary

Generating mutation lineage trees from sequence data is challenging due to complex constraints. The COnstrained Lineage Tree (COLT) framework efficiently builds valid lineage trees, satisfying expert-defined constraints.

Keywords:

Constrained Tree Construction Lineage Analysis Sequence Data

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Lineage analysis is crucial for understanding gene mutation patterns, particularly in antibody diversity.
Mutation lineages are commonly depicted as tree structures to illustrate potential mutation pathways.
Generating these trees from sequence data presents challenges in handling complex constraints and computational intractability.

Purpose of the Study:

To present a novel framework, COLT, for generating constrained lineage trees from sequence data.
To address the computational challenges associated with enumerating all possible valid lineage trees.
To incorporate domain expert-defined local and global constraints into the lineage tree generation process.

Main Methods:

Developed the COnstrained Lineage Tree (COLT) generation framework.
Integrated domain expert-defined constraints and heuristics derived from mutation processes.
Employed algorithms designed to formulate and maintain constraints on sequence data and tree structures.

Main Results:

Demonstrated the efficiency of the COLT framework in generating lineage trees.
Showcased the framework's ability to strictly satisfy specified constraints.
Validated the approach through formal analysis and experimental results.

Conclusions:

The COLT framework provides an efficient solution for constructing lineage trees from sequence data.
It effectively handles complex constraints, enabling accurate representation of mutation pathways.
This approach facilitates a deeper understanding of mutation patterns and genetic diversity.