Evidence map›Paper›PMID 28749987›Full record

ArticlePloS one2017

DUDE-Seq: Fast, flexible, and robust denoising for targeted amplicon sequencing.

Byunghan Lee, Taesup Moon, Sungroh Yoon, Tsachy Weissman

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

  1. Trial
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  4. Longitudinal remodeling of gastric microbiota followingFrontiers in cellular and infection microbiology · 2026
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  15. Influence ofJournal of ginseng research · 2022
    Article
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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

4 authors.

Byunghan LeeElectrical and Computer Engineering, Seoul National University, Seoul, Korea.
Taesup MoonCollege of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea.
Sungroh YoonElectrical and Computer Engineering, Seoul National University, Seoul, Korea.ORCID http://orcid.org/0000-0002-2367-197X
Tsachy WeissmanElectrical Engineering, Stanford University, Stanford, California, United States of America.

Funding

Genomic Compression: From Information Theory to Parallel AlgorithmsU01CA198943 · NCI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI MILENKOVIC, OLGICA, WEISSMAN, TSACHY · 2015 to 2017
$1.4M
NCI NIH HHS U01 CA198943
6 · The paper itself

Abstract

We consider the correction of errors from nucleotide sequences produced by next-generation targeted amplicon sequencing. The next-generation sequencing (NGS) platforms can provide a great deal of sequencing data thanks to their high throughput, but the associated error rates often tend to be high. Denoising in high-throughput sequencing has thus become a crucial process for boosting the reliability of downstream analyses. Our methodology, named DUDE-Seq, is derived from a general setting of reconstructing finite-valued source data corrupted by a discrete memoryless channel and effectively corrects substitution and homopolymer indel errors, the two major types of sequencing errors in most high-throughput targeted amplicon sequencing platforms. Our experimental studies with real and simulated datasets suggest that the proposed DUDE-Seq not only outperforms existing alternatives in terms of error-correction capability and time efficiency, but also boosts the reliability of downstream analyses. Further, the flexibility of DUDE-Seq enables its robust application to different sequencing platforms and analysis pipelines by simple updates of the noise model. DUDE-Seq is available at http://data.snu.ac.kr/pub/dude-seq.

Indexed as

AlgorithmsBase SequenceComputer SimulationDatabases, Nucleic AcidHigh-Throughput Nucleotide Sequencing

Identifiers

PMID28749987
PMCPMC5531809

What OpenQuestion holds

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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.