Evidence map›Paper›PMID 22537038›Full record

ArticleBMC bioinformatics2012

Exploiting sparseness in de novo genome assembly.

Chengxi Ye, Zhanshan Sam Ma, Charles H Cannon, Mihai Pop, Douglas W Yu

Abstract read
In one paragraph

Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.

0numbers the graph read from it
0cells of the map it votes in
71citing 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

71 citing papers in PubMed.

  1. Article
  2. High-throughput approaches for the identification of ribosome heterogeneity.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
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  3. The Statistics of Parametrized Syncmers in a Simple Mutation Process Without Spurious Matches.Journal of computational biology : a journal of computational molecular cell biology · 2024
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  6. Density and Conservation Optimization of the Generalized Masked-Minimizer Sketching Scheme.Journal of computational biology : a journal of computational molecular cell biology · 2024
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  7. Creating and Using Minimizer Sketches in Computational Genomics.Journal of computational biology : a journal of computational molecular cell biology · 2023
    Review
  8. Article
  9. Article
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  11. Article
  12. Differentiable Learning of Sequence-Specific Minimizer Schemes with DeepMinimizer.Journal of computational biology : a journal of computational molecular cell biology · 2022
    Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Sequence-specific minimizers via polar sets.Bioinformatics (Oxford, England) · 2021
    Article
  18. Metagenome SNP calling via read-colored de Bruijn graphs.Bioinformatics (Oxford, England) · 2021
    Article
  19. Lower Density Selection Schemes via Small Universal Hitting Sets with Short Remaining Path Length.Journal of computational biology : a journal of computational molecular cell biology · 2021
    Article
  20. Article

11 more citing papers are in PubMed but not listed here.

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

5 authors.

Chengxi YeEcology & Evolution of Plant-Animal Interaction Group, Xishuangbanna Tropical Botanic Garden, Chinese Academy of Sciences, Menglun, Yunnan 666303 China. cxy@umd.edu
Zhanshan Sam Ma
Charles H Cannon
Mihai Pop
Douglas W Yu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe very large memory requirements for the construction of assembly graphs for de novo genome assembly limit current algorithms to super-computing environments.

methodsIn this paper, we demonstrate that constructing a sparse assembly graph which stores only a small fraction of the observed k-mers as nodes and the links between these nodes allows the de novo assembly of even moderately-sized genomes (~500 M) on a typical laptop computer.

resultsWe implement this sparse graph concept in a proof-of-principle software package, SparseAssembler, utilizing a new sparse k-mer graph structure evolved from the de Bruijn graph. We test our SparseAssembler with both simulated and real data, achieving ~90% memory savings and retaining high assembly accuracy, without sacrificing speed in comparison to existing de novo assemblers.

Indexed as

Computer Storage DevicesGenomeSoftwareAlgorithmsEscherichia coliGenome, HumanHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNA

Identifiers

PMID22537038
PMCPMC3369186

What OpenQuestion holds

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LicenceCC BY
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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.