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Related Experiment Video

Updated: Dec 31, 2025

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Published on: December 28, 2015

Clonal reconstruction from time course genomic sequencing data.

Wazim Mohammed Ismail¹, Haixu Tang²

¹School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA. wazimoha@iu.edu.

|January 1, 2020

Summary

This summary is machine-generated.

Researchers developed fast and accurate algorithms to reconstruct bacterial clone evolution history from genomic data. This method aids in understanding evolutionary pressures on mutations in evolving bacterial populations.

Keywords:

Clonal reconstruction Long-term evolution experiment Maximum likelihood Time course

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Last Updated: Dec 31, 2025

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

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Area of Science:

Evolutionary Biology
Computational Biology
Genomics

Background:

Bacterial populations accumulate spontaneous mutations during replication, leading to novel clones and altered population composition over time.
Long-term evolution experiments (LTEEs) reveal dynamic changes in bacterial clonal composition.
Inferring clone haplotypes, frequencies, and evolutionary history is crucial for understanding evolutionary pressures on correlated mutations.

Purpose of the Study:

To computationally reconstruct bacterial clone haplotypes and their evolutionary history from time-course variant allele frequency data.
To develop and validate efficient algorithms for inferring clonal composition and evolutionary trajectories in bacterial populations.

Main Methods:

Formalized the reconstruction problem using a maximum likelihood function based on spontaneous mutation rates and clone frequencies.
Developed a series of heuristic algorithms for maximum likelihood inference.
Validated algorithms using both simulated data and experimental genomic data from *Escherichia coli* LTEEs.

Main Results:

The developed heuristic algorithms demonstrate fast performance and near-optimal accuracy in simulations.
The method achieves practically plausible results within the maximum likelihood framework.
Validation with *E. coli* experimental data confirms the method's effectiveness for real-world LTEE data.

Conclusions:

Efficient algorithms were developed for reconstructing clonal evolution history from time-course genomic sequencing data.
The algorithms can incorporate additional clonal sequencing data to improve reconstruction accuracy.
The method provides satisfactory results for genome sequencing data from long-term evolution experiments.