Evidence map›Paper›PMID 31510647›Full record

ArticleBioinformatics (Oxford, England)2019

Building large updatable colored de Bruijn graphs via merging.

Martin D Muggli, Bahar Alipanahi, Christina Boucher

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Enhancing Data Compression: Recent Innovations in LZ77 Algorithms.Journal of computational biology : a journal of computational molecular cell biology · 2025
    Review
  3. Article
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  5. Article
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  8. Article
  9. Article
  10. Succinct dynamic de Bruijn graphs.Bioinformatics (Oxford, England) · 2021
    Article
  11. Constructing small genome graphs via string compression.Bioinformatics (Oxford, England) · 2021
    Article
  12. Topology-based sparsification of graph annotations.Bioinformatics (Oxford, England) · 2021
    Article
  13. Article
  14. Article
  15. Article
  16. Buffering updates enables efficient dynamic de Bruijn graphs.Computational and structural biotechnology journal · 2021
    Article
  17. Data structures based onGenome research · 2021
    Review
  18. Article
  19. Article
  20. Portable nanopore analytics: are we there yet?Bioinformatics (Oxford, England) · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Martin D MuggliDepartment of Computer Science, Colorado State University, Fort Collins, CO, USA.
Bahar AlipanahiDepartment of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.
Christina BoucherDepartment of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.

Funding

Developing Computational Methods for Surveillance of Antimicrobial Resistant AgentsR01AI141810 · NIAID · UNIVERSITY OF FLORIDA · PI BOUCHER, CHRISTINA, PROSPERI, MATTIA · 2019 to 2023
$2.1M
NIAID NIH HHS R01 AI141810
6 · The paper itself

Abstract

motivationThere exist several large genomic and metagenomic data collection efforts, including GenomeTrakr and MetaSub, which are routinely updated with new data. To analyze such datasets, memory-efficient methods to construct and store the colored de Bruijn graph were developed. Yet, a problem that has not been considered is constructing the colored de Bruijn graph in a scalable manner that allows new data to be added without reconstruction. This problem is important for large public datasets as scalability is needed but also the ability to update the construction is also needed.

resultsWe create a method for constructing the colored de Bruijn graph for large datasets that is based on partitioning the data into smaller datasets, building the colored de Bruijn graph using a FM-index based representation, and succinctly merging these representations to build a single graph. The last step, merging succinctly, is the algorithmic challenge which we solve in this article. We refer to the resulting method as VariMerge. This construction method also allows the graph to be updated with new data. We validate our approach and show it produces a three-fold reduction in working space when constructing a colored de Bruijn graph for 8000 strains. Lastly, we compare VariMerge to other competing methods-including Vari, Rainbowfish, Mantis, Bloom Filter Trie, the method of Almodaresi et al. and Multi-BRWT-and illustrate that VariMerge is the only method that is capable of building the colored de Bruijn graph for 16 000 strains in a manner that allows it to be updated. Competing methods either did not scale to this large of a dataset or do not allow for additions without reconstruction. AVAILABILITY AND IMPLEMENTATION: VariMerge is available at https://github.com/cosmo-team/cosmo/tree/VARI-merge under GPLv3 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

AlgorithmsSequence Analysis, DNASoftwareGenomicsMetagenome

Identifiers

PMID31510647
PMCPMC6612864

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.