Scalable pairwise whole-genome homology mapping of long genomes with BubbZ
Open accessAbstract
Pairwise whole-genome homology mapping is the problem of finding all pairs of homologous intervals between a pair of genomes. As the number of available whole genomes has been rising dramatically in the last few years, there has been a need for more scalable homology mappers. In this paper, we develop an algorithm (BubbZ) for computing whole-genome pairwise homology mappings, especially in the context of all-to-all comparison for multiple genomes. BubbZ is based on an algorithm for computing chains in compacted de Bruijn graphs. We evaluate BubbZ on simulated datasets, a dataset composed of 16 long mouse genomes, and a large dataset of 1,600 Salmonella genomes. We show up to approximately an order of magnitude speed improvement, compared with MashMap2 and Minimap2, while retaining similar accuracy.
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BubbZ identifies bubbles in the de Bruijn graph, which represent regions of potential structural variation.
“The algorithm identifies bubbles in the de Bruijn graph, which represent regions of potential structural variation.”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [47]
The Discussion says BubbZ uses a compacted de Bruijn graph, supporting the graph-based part of the claim. The Results’ Algorithm Overview says it outputs coordinates of maximal homologies, but neither that passage nor the passages found by searching for bubbles and structural variation says it identifies bubbles or interprets them as potential structural variation. No page numbers are provided in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredBubbZ is a bioinformatics algorithm designed for the identification and analysis of genomic structural variations (SVs), specifically large insertions and deletions (indels), in whole-genome sequencing data.
“BubbZ is a bioinformatics algorithm designed for the identification and analysis of genomic structural variations (SVs), specifically large insertions and deletions (indels), in whole-genome sequencing data.”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [47]
The Introduction identifies BubbZ as an algorithm for pairwise whole-genome homology mapping, and the Algorithm Overview says it outputs coordinates of homologous regions. The Introduction notes that homology maps can aid analysis of genome rearrangements, but our copy does not describe BubbZ as designed to identify or analyze structural variants, specifically large insertions and deletions, in whole-genome sequencing data. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAccuracy of WGA algorithms is influenced by the algorithm's ability to correctly identify homologous regions, handle genomic rearrangements, and align divergent sequences.
“Accuracy is influenced by the algorithm's ability to correctly identify homologous regions, handle genomic rearrangements, and align divergent sequences [39,47].”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [47]
The Introduction defines homology mapping as finding homologous intervals, and Results > Results on the Simulated Data reports that precision and recall decline as genomic divergence increases. But the paper describes rearrangement analysis as an application of mapping; it does not establish that handling rearrangements affects accuracy. It also distinguishes homology mapping from base-to-base whole-genome alignment, so the claim extends its findings to WGA algorithms. (LLM, conf=0.86)
Experimental — accuracy not yet measuredBubbZ is a fast whole-genome homology mapper that detects pairwise chains in homologous blocks using a de Bruijn graph.
“It is a fast whole-genome homology mapper which detects pairwise chains in homologous blocks using a de Bruijn graph [47].”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [47]
BubbZ performs variant calling to classify structural variations into categories such as insertions, deletions, duplications, or complex rearrangements.
“It performs variant calling to classify the structural variations into different categories, such as insertions, deletions, duplications, or complex rearrangements.”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [47]
The Algorithm Overview describes BubbZ as outputting coordinates of homologous genomic intervals, and the Discussion describes it as a pairwise genome-mapping method. Searches for variant calling and the listed structural-variation categories found no passage in our copy saying BubbZ classifies them; the output description alone does not establish that it cannot do so. (LLM, conf=0.72)
Experimental — accuracy not yet measured
BubbZ typically has a worst-case time complexity of O(n^2), where n is the length of the input sequences.
“It typically has a worst-case time complexity of O ( n 2 ), where n is the length of the input sequences.”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [47]
The Results on the Mouse Data report roughly linear empirical scaling but do not establish a worst-case bound. Searches for quadratic complexity and O(n²) found no passage in our copy stating BubbZ’s worst-case time complexity in terms of input-sequence length. Page numbers are unavailable in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measuredBubbZ expands each detected bubble by traversing alternative paths through the de Bruijn graph.
“Then, it expands each detected bubble by traversing alternative paths through the de Bruijn graph.”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [47]
The Algorithm Overview describes BubbZ as finding maximal homologies between sequences, and the Discussion describes chaining in a compacted de Bruijn graph. Neither passage says it detects bubbles or expands them by traversing alternative paths. Searches for bubble expansion and alternative-path traversal found no passage addressing that operation; the indexed copy points to a separate supplemental file for detailed methods. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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Whole-Genome Alignment: Methods, Challenges, and Future Directions
Saada, B., Zhang, T., Siga, E., Zhang, J., & Magalhães Muniz, M. M. (2024) · Applied Sciences · doi:10.3390/app14114837 · Cited by 15 · see citing works
- Authors
- Minkin, I., & Medvedev, P.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Minkin, Ilia
- Medvedev, Paul
- Venue
- iScience
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 23
- Issue
- 6
- Pages
- 101224
- ISSN
- 2589-0042 (ISSN-L)
- PMC
- PMC7303978
- OpenAlex
- W3033322505
- Cited by
- 15
- References
- 1
- Citations by year
- 2026: 1 · 2024: 5 · 2022: 7 · 2021: 1 · 2020: 1
- FWCI
- 0.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iScience (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (CCF-1439057, DBI-1356529, IIS-1453527)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genome Rearrangement Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Keywords
- de Bruijn graph, mouse genome
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