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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

Graphical pan-genome analysis with compressed suffix trees and the Burrows-Wheeler transform.

Uwe Baier¹, Timo Beller¹, Enno Ohlebusch¹

¹Institute of Theoretical Computer Science, Ulm University, 89069 Ulm, Germany.

Bioinformatics (Oxford, England)

|October 28, 2015

Summary

New algorithms efficiently construct compressed de Bruijn graphs for population genomics. These methods improve upon existing techniques, enabling scalable analysis of large genomic datasets and revealing population genetic structure.

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Low-cost genome sequencing provides extensive population genetic structure data.
Population graphs represent variations across many individuals.
Compressed de Bruijn graphs are used for population genome representation.

Purpose of the Study:

To develop space-efficient algorithms for constructing compressed de Bruijn graphs.
To improve upon the existing splitMEM algorithm for population genome representation.

Main Methods:

Developed a linear-time suffix tree algorithm using a compressed suffix tree.
Utilized the Burrows-Wheeler transform to construct the compressed de Bruijn graph in O(n) time.
Applied algorithms to seven human genomes to demonstrate scalability.

Main Results:

Presented two novel algorithms that outperform splitMEM in theory and practice.
Achieved efficient construction of compressed de Bruijn graphs.
Demonstrated scalability with human genome data.

Conclusions:

The developed algorithms offer significant improvements for constructing population genome graphs.
These methods enable more scalable and efficient analysis of large-scale genomic data.
Facilitates deeper insights into population genetic structures.