ArticleBioinformatics (Oxford, England)2018
Integrating long-range connectivity information into de Bruijn graphs.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Maast: genotyping thousands of microbial strains efficiently.Genome biology · 2023Pooled it
- Article
- Article
- RLSuccSite: succinylation sites prediction based on reinforcement learning dynamic with balanced reward mechanism and three-peaks enhanced method for physicochemical property scores.Journal of cheminformatics · 2025Article
- Efficient and robust search of microbial genomes via phylogenetic compression.Nature methods · 2025Article
- A gentle introduction to pangenomics.Briefings in bioinformatics · 2024Review
- Previously unmeasured genetic diversity explains part of Lewontin's paradox in abioRxiv : the preprint server for biology · 2024Article
- Unlocking plant genetics with telomere-to-telomere genome assemblies.Nature genetics · 2024Review
- Label-guided seed-chain-extend alignment on annotated De Bruijn graphs.Bioinformatics (Oxford, England) · 2024Article
- Selection of mutantApplied and environmental microbiology · 2023Article
- On the origin and structure of haplotype blocks.Molecular ecology · 2023Article
- Cone-setting in spruce is regulated by conserved elements of the age-dependent flowering pathway.The New phytologist · 2022Article
- Minos: variant adjudication and joint genotyping of cohorts of bacterial genomes.Genome biology · 2022Article
- Pangenome-based genome inference allows efficient and accurate genotyping across a wide spectrum of variant classes.Nature genetics · 2022Article
- NovelMicroorganisms · 2022Article
- Review
- KOMB: K-core based de novo characterization of copy number variation in microbiomes.Computational and structural biotechnology journal · 2022Article
- CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure.PLoS computational biology · 2021Article
- Topology-based sparsification of graph annotations.Bioinformatics (Oxford, England) · 2021Article
- Disk compression of k-mer sets.Algorithms for molecular biology : AMB · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Motivation: The de Bruijn graph is a simple and efficient data structure that is used in many areas of sequence analysis including genome assembly, read error correction and variant calling. The data structure has a single parameter k, is straightforward to implement and is tractable for large genomes with high sequencing depth. It also enables representation of multiple samples simultaneously to facilitate comparison. However, unlike the string graph, a de Bruijn graph does not retain long range information that is inherent in the read data. For this reason, applications that rely on de Bruijn graphs can produce sub-optimal results given their input data. Results: We present a novel assembly graph data structure: the Linked de Bruijn Graph (LdBG). Constructed by adding annotations on top of a de Bruijn graph, it stores long range connectivity information through the graph. We show that with error-free data it is possible to losslessly store and recover sequence from a Linked de Bruijn graph. With assembly simulations we demonstrate that the LdBG data structure outperforms both our de Bruijn graph and the String Graph Assembler (SGA). Finally we apply the LdBG to Klebsiella pneumoniae short read data to make large (12 kbp) variant calls, which we validate using PacBio sequencing data, and to characterize the genomic context of drug-resistance genes. Availability and implementation: Linked de Bruijn Graphs and associated algorithms are implemented as part of McCortex, which is available under the MIT license at https://github.com/mcveanlab/mccortex. Supplementary information: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.