ArticleBioinformatics (Oxford, England)2017
deBGR: an efficient and near-exact representation of the weighted de Bruijn graph.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 41 citations in OpenAlex.
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- An incrementally updatable and scalable system for large-scale sequence search using the Bentley-Saxe transformation.Bioinformatics (Oxford, England) · 2022Article
- Review
- Data Structures to Represent a Set ofACM computing surveys · 2022Article
- Simplitigs as an efficient and scalable representation of de Bruijn graphs.Genome biology · 2021Article
- Representation ofJournal of computational biology : a journal of computational molecular cell biology · 2021Article
- An Efficient, Scalable, and Exact Representation of High-Dimensional Color Information Enabled Using de Bruijn Graph Search.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data.Genome biology · 2019Review
- Dynamic compression schemes for graph coloring.Bioinformatics (Oxford, England) · 2019Article
- Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index.Cell systems · 2018Article
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4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
motivationAlmost all de novo short-read genome and transcriptome assemblers start by building a representation of the de Bruijn Graph of the reads they are given as input. Even when other approaches are used for subsequent assembly (e.g. when one is using 'long read' technologies like those offered by PacBio or Oxford Nanopore), efficient k -mer processing is still crucial for accurate assembly, and state-of-the-art long-read error-correction methods use de Bruijn Graphs. Because of the centrality of de Bruijn Graphs, researchers have proposed numerous methods for representing de Bruijn Graphs compactly. Some of these proposals sacrifice accuracy to save space. Further, none of these methods store abundance information, i.e. the number of times that each k -mer occurs, which is key in transcriptome assemblers.
resultsWe present a method for compactly representing the weighted de Bruijn Graph (i.e. with abundance information) with essentially no errors. Our representation yields zero errors while increasing the space requirements by less than 18-28% compared to the approximate de Bruijn graph representation in Squeakr. Our technique is based on a simple invariant that all weighted de Bruijn Graphs must satisfy, and hence is likely to be of general interest and applicable in most weighted de Bruijn Graph-based systems. AVAILABILITY AND IMPLEMENTATION: https://github.com/splatlab/debgr . CONTACT: rob.patro@cs.stonybrook.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.