Evidence map›Paper›PMID 27956617›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2016

Assembly of long error-prone reads using de Bruijn graphs.

Yu Lin, Jeffrey Yuan, Mikhail Kolmogorov, Max W Shen, Mark Chaisson, Pavel A Pevzner

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 221 papers.

0numbers the graph read from it
0cells of the map it votes in
221citing 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

221 citing papers in PubMed.

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161 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

6 authors.

Yu LinDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92092.
Jeffrey YuanDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92092.
Mikhail KolmogorovDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92092.
Max W ShenDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92092.
Mark ChaissonDepartment of Genome Sciences, University of Washington, Seattle, WA 98105.
Pavel A PevznerDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92092; ppevzner@ucsd.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent breakthroughs in assembling long error-prone reads were based on the overlap-layout-consensus (OLC) approach and did not utilize the strengths of the alternative de Bruijn graph approach to genome assembly. Moreover, these studies often assume that applications of the de Bruijn graph approach are limited to short and accurate reads and that the OLC approach is the only practical paradigm for assembling long error-prone reads. We show how to generalize de Bruijn graphs for assembling long error-prone reads and describe the ABruijn assembler, which combines the de Bruijn graph and the OLC approaches and results in accurate genome reconstructions.

Indexed as

AlgorithmsBenchmarkingEscherichia coliGenomicsHigh-Throughput Nucleotide SequencingReproducibility of ResultsSequence Analysis, DNASoftwareXanthomonasde Bruijn graphgenome assemblysingle-molecule sequencing

Identifiers

PMID27956617
PMCPMC5206522

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.