Evidence map›Paper›PMID 27330851›Full record

ArticlePeerJ2016

Sparc: a sparsity-based consensus algorithm for long erroneous sequencing reads.

Chengxi Ye, Zhanshan Sam Ma

Abstract read
In one paragraph

Article in PeerJ, 2016. 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

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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

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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  5. Article
  6. Article
  7. Article
  8. High-Quality Genome Assembly ofFrontiers in plant science · 2022
    Article
  9. Article
  10. Article
  11. Draft Genome of the Mirrorwing Flyingfish (Frontiers in genetics · 2021
    Article
  12. Article
  13. [Reconstruction of tumor clonal haplotypes based on an improved spanning algorithm].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2019
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Genome Sequences ofGenome announcements · 2017
    Article
  20. 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

2 authors.

Chengxi YeDepartment of Computer Science, University of Maryland , College Park, MD , USA.
Zhanshan Sam MaComputational Biology and Medical Ecology Lab, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming, Yunnan , China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation. The third generation sequencing (3GS) technology generates long sequences of thousands of bases. However, its current error rates are estimated in the range of 15-40%, significantly higher than those of the prevalent next generation sequencing (NGS) technologies (less than 1%). Fundamental bioinformatics tasks such as de novo genome assembly and variant calling require high-quality sequences that need to be extracted from these long but erroneous 3GS sequences. Results. We describe a versatile and efficient linear complexity consensus algorithm Sparc to facilitate de novo genome assembly. Sparc builds a sparse k-mer graph using a collection of sequences from a targeted genomic region. The heaviest path which approximates the most likely genome sequence is searched through a sparsity-induced reweighted graph as the consensus sequence. Sparc supports using NGS and 3GS data together, which leads to significant improvements in both cost efficiency and computational efficiency. Experiments with Sparc show that our algorithm can efficiently provide high-quality consensus sequences using both PacBio and Oxford Nanopore sequencing technologies. With only 30× PacBio data, Sparc can reach a consensus with error rate <0.5%. With the more challenging Oxford Nanopore data, Sparc can also achieve similar error rate when combined with NGS data. Compared with the existing approaches, Sparc calculates the consensus with higher accuracy, and uses approximately 80% less memory and time. Availability. The source code is available for download at https://github.com/yechengxi/Sparc.

Indexed as

Consensus algorithmGenome assemblySingle molecular sequencingThird generation sequencing technologyVariant discovery

Identifiers

PMID27330851
PMCPMC4906657

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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.