Evidence map›Paper›PMID 31156670›Full record

ArticleFrontiers in plant science2019

Computational Analysis of AmpSeq Data for Targeted, High-Throughput Genotyping of Amplicons.

Jonathan Fresnedo-Ramírez, Shanshan Yang, Qi Sun, Avinash Karn, Bruce I Reisch, Lance Cadle-Davidson

Abstract read
In one paragraph

Article in Frontiers in plant science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
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  4. 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

6 authors.

Jonathan Fresnedo-RamírezDepartment of Horticulture and Crop Science, The Ohio State University, Wooster, OH, United States.
Shanshan YangSchool of Integrative Plant Science, Cornell AgriTech, Geneva, NY, United States.
Qi SunBiotechnology Resource Center, Bioinformatics Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, United States.
Avinash KarnSchool of Integrative Plant Science, Cornell AgriTech, Geneva, NY, United States.
Bruce I ReischSchool of Integrative Plant Science, Cornell AgriTech, Geneva, NY, United States.
Lance Cadle-DavidsonSchool of Integrative Plant Science, Cornell AgriTech, Geneva, NY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amplicon sequencing (AmpSeq) is a practical, intuitive strategy with a semi-automated computational pipeline for analysis of highly multiplexed PCR-derived sequences. This genotyping platform is particularly cost-effective when multiplexing 96 or more samples with a few amplicons up to thousands of amplicons. Amplicons can target from a single nucleotide to the upper limit of the sequencing platform. The flexibility of AmpSeq's wet lab methods make it a tool of broad interest for diverse species, and AmpSeq excels in flexibility, high-throughput, low-cost, accuracy, and semi-automated analysis. Here we provide an open science framework procedure to output data out of an AmpSeq project, with an emphasis on the bioinformatics pipeline to generate SNPs, haplotypes and presence/absence variants in a set of diverse genotypes. Open-access tutorial datasets with actual data and a containerization open source software instance are provided to enable training in each of these genotyping applications. The pipelines presented here should be applicable to the analysis of various target-enriched (e.g., amplicon or sequence capture) Illumina sequence data.

Indexed as

amplicon read countshaplotype resolutionheterozygositymolecular marker developmentSNP markerstargeted amplicon sequencing

Identifiers

PMID31156670
PMCPMC6528068

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.