Location: Grape Genetics Research Unit (GGRU)Title: Computational analysis of AmpSeq data for targeted, high-throughput genotyping of amplicons
|FRESNEDO-RAMIREZ, JONATHAN - Cornell University - New York|
|YANG, SHANSHAN - Cornell University - New York|
|SUN, QI - Cornell University - New York|
|KARN, AVI - Cornell University - New York|
|REISCH, BRUCE - Cornell University - New York|
Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/14/2019
Publication Date: 5/14/2019
Citation: Fresnedo-Ramirez, J., Yang, S., Sun, Q., Karn, A., Reisch, B., Cadle Davidson, L.E. 2019. Computational analysis of AmpSeq data for targeted, high-throughput genotyping of amplicons. Frontiers in Plant Science. 10:599. https://doi.org/10.3389/fpls.2019.00599.
Interpretive Summary: AmpSeq is a new DNA marker technology. Its flexibility makes AmpSeq a tool of broad interest for diverse species, and AmpSeq excels in speed, low-cost, accuracy, flexibility, and semi-automated analysis. These features allow a targeted genetic comparisons across diverse organisms. Here we describe the step-by-step computational procedures to generate data out of an AmpSeq project.
Technical 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 a thousand 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 describe the procedure to output data out of an AmpSeq project, with emphasis on the bioinformatics pipeline to generate SNPs, haplotypes and presence/absence variants in a set of diverse genotypes. Open-access tutorial datasets are provided to enable training in each of these genotyping applications.