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

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...

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Autores principales: Fresnedo-Ramírez, Jonathan, Yang, Shanshan, Sun, Qi, Karn, Avinash, Reisch, Bruce I., Cadle-Davidson, Lance
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6528068/
https://www.ncbi.nlm.nih.gov/pubmed/31156670
http://dx.doi.org/10.3389/fpls.2019.00599
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author Fresnedo-Ramírez, Jonathan
Yang, Shanshan
Sun, Qi
Karn, Avinash
Reisch, Bruce I.
Cadle-Davidson, Lance
author_facet Fresnedo-Ramírez, Jonathan
Yang, Shanshan
Sun, Qi
Karn, Avinash
Reisch, Bruce I.
Cadle-Davidson, Lance
author_sort Fresnedo-Ramírez, Jonathan
collection PubMed
description 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.
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spelling pubmed-65280682019-05-31 Computational Analysis of AmpSeq Data for Targeted, High-Throughput Genotyping of Amplicons Fresnedo-Ramírez, Jonathan Yang, Shanshan Sun, Qi Karn, Avinash Reisch, Bruce I. Cadle-Davidson, Lance Front Plant Sci Plant Science 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. Frontiers Media S.A. 2019-05-14 /pmc/articles/PMC6528068/ /pubmed/31156670 http://dx.doi.org/10.3389/fpls.2019.00599 Text en Copyright © 2019 Fresnedo-Ramírez, Yang, Sun, Karn, Reisch and Cadle-Davidson. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Fresnedo-Ramírez, Jonathan
Yang, Shanshan
Sun, Qi
Karn, Avinash
Reisch, Bruce I.
Cadle-Davidson, Lance
Computational Analysis of AmpSeq Data for Targeted, High-Throughput Genotyping of Amplicons
title Computational Analysis of AmpSeq Data for Targeted, High-Throughput Genotyping of Amplicons
title_full Computational Analysis of AmpSeq Data for Targeted, High-Throughput Genotyping of Amplicons
title_fullStr Computational Analysis of AmpSeq Data for Targeted, High-Throughput Genotyping of Amplicons
title_full_unstemmed Computational Analysis of AmpSeq Data for Targeted, High-Throughput Genotyping of Amplicons
title_short Computational Analysis of AmpSeq Data for Targeted, High-Throughput Genotyping of Amplicons
title_sort computational analysis of ampseq data for targeted, high-throughput genotyping of amplicons
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6528068/
https://www.ncbi.nlm.nih.gov/pubmed/31156670
http://dx.doi.org/10.3389/fpls.2019.00599
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