Evidence map›Paper›PMID 30126356›Full record

ArticleBMC bioinformatics2018

Clustering of circular consensus sequences: accurate error correction and assembly of single molecule real-time reads from multiplexed amplicon libraries.

Felix Francis, Michael D Dumas, Scott B Davis, Randall J Wisser

Abstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Defining Blood Group Gene Reference Alleles by Long-Read Sequencing: Proof of Concept in theTransfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2020
    Article
  4. An ImprovedFrontiers in microbiology · 2020
    Article
  5. Article
  6. 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

4 authors.

Felix FrancisDepartment of Plant and Soil Sciences, University of Delaware, Newark, Delaware, 19716, USA.
Michael D DumasDepartment of Plant and Soil Sciences, University of Delaware, Newark, Delaware, 19716, USA.
Scott B DavisDepartment of Plant and Soil Sciences, University of Delaware, Newark, Delaware, 19716, USA.
Randall J WisserDepartment of Plant and Soil Sciences, University of Delaware, Newark, Delaware, 19716, USA. rjw@udel.edu.ORCID http://orcid.org/0000-0003-1075-0115

Funding

Predictive Modeling & Optimal Control Framework for Model-Based Epidemic Response in DelawareP20GM103446 · NIGMS · UNIVERSITY OF DELAWARE · PI Anjana Narayan Bhat · 2012 to 2026
$67.2M
Division of Integrative Organismal Systems 1127076NIGMS NIH HHS P20 GM103446
6 · The paper itself

Abstract

backgroundTargeted resequencing with high-throughput sequencing (HTS) platforms can be used to efficiently interrogate the genomes of large numbers of individuals. A critical issue for research and applications using HTS data, especially from long-read platforms, is error in base calling arising from technological limits and bioinformatic algorithms. We found that the community standard long amplicon analysis (LAA) module from Pacific Biosciences is prone to substantial bioinformatic errors that raise concerns about findings based on this pipeline, prompting the need for a new method.

resultsA single molecule real-time (SMRT) sequencing-error correction and assembly pipeline, C3S-LAA, was developed for libraries of pooled amplicons. By uniquely leveraging the structure of SMRT sequence data (comprised of multiple low quality subreads from which higher quality circular consensus sequences are formed) to cluster raw reads, C3S-LAA produced accurate consensus sequences and assemblies of overlapping amplicons from single sample and multiplexed libraries. In contrast, despite read depths in excess of 100X per amplicon, the standard long amplicon analysis module from Pacific Biosciences generated unexpected numbers of amplicon sequences with substantial inaccuracies in the consensus sequences. A bootstrap analysis showed that the C3S-LAA pipeline per se was effective at removing bioinformatic sources of error, but in rare cases a read depth of nearly 400X was not sufficient to overcome minor but systematic errors inherent to amplification or sequencing.

conclusionsC3S-LAA uses a divide and conquer processing algorithm for SMRT amplicon-sequence data that generates accurate consensus sequences and local sequence assemblies. Solving the confounding bioinformatic source of error in LAA allowed for the identification of limited instances of errors due to DNA amplification or sequencing of homopolymeric nucleotide tracts. For research and development in genomics, C3S-LAA allows meaningful conclusions and biological inferences to be made from accurately polished sequence output.

Indexed as

Genetic TestingGenomicsHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNADivide and conquerLong-range PCRPacBio amplicon analysisResequencingSequence errorTarget enrichment

Identifiers

PMID30126356
PMCPMC6102811

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.