ABSTRACT. Colocasia esculenta cv. Xinmaoyu is an eddoe-type taro cultivar local to Taicang, Jiangsu Province, China; it is characterized by its pure flavor, glutinous texture, and high nutritional value. Due to its excellent qualities, the Trademark Office of the State Administration for Industry and Commerce of the People's Republic of China awarded Xinmaoyu, a geographical indication certification in 2014. Therefore, there is an urgent need to develop an efficient molecular marker for the specific identification of this cultivar, which would greatly facilitate the conservation and utilization of this unique germplasm resource. In the present study, amplifying the psbE-petL fragment from two dasheentype and seven eddoe-type taro cultivars revealed three conserved insertions/deletions among sequences from the two taro types. Based on these sequence differences, a pair of site-specific primers was designed targeting the psbE-petL sequence from the dasheen-type taro, which specifically amplified a DNA band in all individuals from cultivars of this type, but not in those from the seven eddoe-type cultivars. To discriminate Xinmaoyu from the other eddoe-type taro cultivars, a pair of simple sequence repeat-sequence characterized amplified region (SSR-SCAR) primers was further developed to specifically amplify a DNA band from all Xinmaoyu individuals, but not from individuals of other eddoe-type taro cultivars. In conclusion, through a twostep-screening procedure using psbE-petL and SSR-SCAR markers, we developed a pair of primers that could specifically discriminate Xinmaoyu from nine taro cultivars commonly cultivated in Jiangsu Province and Fujian Province.

6Mobile genetic elements contribute to bacterial adaptation and evolution; however, detecting 7 these elements in a high-throughput and unbiased manner remains challenging. Here, we demonstrate a 8 de novo approach to identify mobile elements from short-read sequencing data. The method identifies 9 the precise site of mobile element insertion and infers the identity of the inserted sequence. This is an 10 improvement over previous methods that either rely on curated databases of known mobile elements or 11 rely on 'split-read' alignments that assume the inserted element exists within the reference genome. We 12 apply our approach to 12,419 sequenced isolates of nine prevalent bacterial pathogens, and we identify 13 hundreds of known and novel mobile genetic elements, including many candidate insertion sequences. 14 We find that the mobile element repertoire and insertion rate vary considerably across species, and that 15 many of the identified mobile elements are biased toward certain target sequences, several of them being 16 highly specific. Mobile element insertion hotspots often cluster near genes involved in mechanisms of 17 antibiotic resistance, and such insertions are associated with antibiotic resistance in laboratory 18 experiments and clinical isolates. Finally, we demonstrate that mutagenesis caused by these mobile 19 elements contributes to antibiotic resistance in a genome-wide association study of mobile element 20 insertions in pathogenic Escherichia coli. In summary, by applying a de novo approach to precisely identify 21 elements, such as insertion sequences, MGEs can contain additional "passenger" genes ( Figure 1e). These 45 passenger genes can code for a variety of proteins, including virulence factors, antibiotic resistance genes, 46 detoxifying agents, and enzymes for secondary metabolism (Rankin, Rocha, and Brown 2011). 47 on shared homology with known MGEs or genes, or by requiring well-annotated reference genomes using 69 sequenced isolates that closely resemble the reference. 70Here, we sought to comprehensively identify complete MGEs, the genes they contained, and their 71 site of insertion with respect to a reference genome from short-read sequencing data. Our approach is 72 flexible, as it can use a database of MGEs when available, but it does not depend entirely on a database 73 of known MGEs and mobile genes. It is sensitive and precise enough to be used on both laboratory 74 samples and environmental isolates. We focus on MGE insertion sites, generate consensus sequences for 75 inserted elements from the clipped ends of locally-aligned reads, infer complete MGEs from sequence 76 assemblies, and build a de novo database of elements across all analyzed samples. We combine several 77 sequence inference approaches to identify large insertions, resulting in a highly sensitive and precise 78 overall approach. 79 By focusing on MGE insertion sites, we answer several questions about these elements that have 80 not been thoroughly addressed in the past. For example, we determine the target...