Identification of Botrytis paeoniae microsatellites using Ion Proton technology
Botrytis paeoniae is a putatively host-specific fungal pathogen causing early-season gray mold infection of peony (Paeonia spp.).Increased global transport of peony rootstock to remote regions of the world has spurred interest in tracking B.paeoniae movement through population genetics approaches using microsatellite markers.Due to the ease and low cost of genome sequencing technologies, draft genomes are often used to identify microsatellites.Questions remain regarding the usefulness of certain sequencing technologies, such as the Ion Proton platform, in microsatellite discovery.We developed a draft genome of a B.paeoniae isolate obtained from a rootstock producer in Washington State using the Ion Proton next-generation sequencing platform.We used assembled reads to identify and develop a panel of microsatellite markers to use on various B.paeoniae populations.We compared our ability to discover microsatellites and develop primers with our Ion Proton genome to an Illumina-sequenced genome of a B.paeoniae isolate from the Netherlands.Our results indicated that while the Ion Proton sequencing technology produced fewer total sequence base pairs than the lllumina system, both sequencing technologies yielded similar numbers of microsatellites for which primers could be developed.Furthermore, an analysis of the characteristics of the microsatellites indicated that the repeat type, repeat number and repeat motif were similar between the two platforms.Our results suggest that the Ion Proton may be useful in microsatellite marker development in fungi.
cut flower host-specific Ion Torrent next-generation sequencing population structure simple sequence repeat whole-genome sequencing
A.R.Garfinkel K.P.Coats G.A.Chastagner
Washington State University Puyallup Research and Extension Center, Puyallup, WA, USA
国际会议
The XII International Symposium on Flower Bulbs and Herbaceous Perennials(第12届国际球宿根花卉学术研讨会)
昆明
英文
341-347
2016-06-28（万方平台首次上网日期，不代表论文的发表时间）