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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

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Computing the Rearrangement Distance of Natural Genomes.

Leonard Bohnenkämper¹, Marília D V Braga¹, Daniel Doerr¹

¹Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|January 4, 2021

Summary

This study introduces a new algorithm for calculating genomic distances in natural genomes with arbitrary marker duplicates. It enables direct analysis of any genome configuration, overcoming previous limitations in comparative genomics.

Keywords:

DCJ-indel distance comparative genomics genome rearrangements

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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:

Computational biology
Genomics
Bioinformatics

Background:

Genomic distance computation is crucial in comparative genomics.
Existing methods assume identical marker sets or balanced duplicates, requiring data preprocessing.
The generalized genomic distance problem with duplicate markers is NP-hard.

Purpose of the Study:

To develop an algorithm for calculating genomic distances in natural genomes with arbitrary marker occurrences.
To lift restrictions on genome configurations for uncompromising rearrangement analysis.
To enable direct analysis of any marker sequence.

Main Methods:

Introduced a novel multi-relational diagram graph data structure.
Extended the integer linear programming (ILP) approach by Shao et al.
Developed a method to count under- or over-represented marker runs for insertion/deletion analysis.

Main Results:

The new algorithm handles genomes with arbitrary numbers of duplicate markers.
Previous restrictions on balanced genomes and data preprocessing are eliminated.
The method is efficient for genomes with up to tens of thousands of markers.

Conclusions:

This algorithm provides the first uncompromising method for genomic rearrangement analysis in natural genomes.
It significantly advances the field of computational comparative genomics by enabling direct analysis of complex genomic data.
The approach is validated on both simulated and real-world genomic datasets.