Background With the broad application of high-throughput sequencing and its reduced cost, simple sequence repeat (SSR) genotyping by sequencing (SSR-GBS) has been widely used for interpreting genetic data across different fields, including population genetic diversity and structure analysis, the construction of genetic maps, and the investigation of intraspecies relationships. The development of accurate and efficient typing strategies for SSR-GBS is urgently needed and several tools have been published. However, to date, no suitable accurate genotyping method can tolerate single nucleotide variations (SNVs) in SSRs and flanking regions. These SNVs may be caused by PCR and sequencing errors or SNPs among varieties, and they directly affect sequence alignment and genotyping accuracy. Results Here, we report a new integrated strategy named the accurate microsatellite genotyping tool based on targeted sequencing (AMGT-TS) and provide a user-friendly web-based platform and command-line version of AMGT-TS. To handle SNVs in the SSRs or flanking regions, we developed a broad matching algorithm (BMA) that can quickly and accurately achieve SSR typing for ultradeep coverage and high-throughput analysis of loci with SNVs compatibility and grouping of typed reads for further in-depth information mining. To evaluate this tool, we tested 21 randomly sampled loci in eight maize varieties, accompanied by experimental validation on actual and simulated sequencing data. Our evaluation showed that, compared to other tools, AMGT-TS presented extremely accurate typing results with single base resolution for both homozygous and heterozygous samples. Conclusion This integrated strategy can achieve accurate SSR genotyping based on targeted sequencing, and it can tolerate single nucleotide variations in the SSRs and flanking regions. This method can be readily applied to divergent sequencing platforms and species and has excellent application prospects in genetic and population biology research. The web-based platform and command-line version of AMGT-TS are available at https://amgt-ts.plantdna.site:8445 and https://github.com/plantdna/amgt-ts, respectively.

Plant variety identification has profound meanings to ensure seed quality and food safety. DNA fingerprinting has its advantage in plant variety identification, and has transformed from indirect evidence into a mainstream method. A method for DNA fingerprinting called core loci combination was summarized in this study, which utilized a set of fixed core loci combination to identify different varieties. Since 2003, the project has been constantly improved and expanded. The first was to form combination of expanded loci by increasing the quantity of core loci to cope with increasing demand of variety identification of derived varieties. The second was to further decompose the core loci into group specific loci which played the function of fast and accurate clustering and species-specific loci which had stronger variety identification function. The third was to further propose the core loci combination of polyploidy crops based on the mature experience of diploid crop, which are diploidized single marker method and combination marker method. From the trend of future development, the rapid development of new technologies such as sequencing technology and gene editing technology will largely promote the improvement of DNA fingerprinting and improve the application effect in practice. This article has systematically elaborated the concept and characteristics of variety identification, summarized major types and identification methods of DNA fingerprinting, extracted technical methods for variety DNA fingerprinting and finally out-looked the future trend of DNA fingerprinting development. It is hoped that the study could provide guidances for the development of DNA fingerprinting standard of each crops, and the application of molecular technology in the structure and variety identification of DNA fingerprinting database.

Background Maize is an important model organism for genetics and genomics research. Though reference genomes of maize are available, some genomes of important genetic germplasms for maize breeding are still lacking, for instance, the cultivar Dan340, which is a backbone inbred line of the LvDa Red Cob Group with several desirable characteristics. In this study, we constructed a high-quality chromosome-level reference genome for Dan340 by using long HiFi reads, short reads, and Hi-C. The final assembly of the Dan340 genome was 2348.72 Mb, which was anchored to 10 chromosomes. Repeat sequences accounted for 73.40% of the genome and 39,733 protein-coding genes were annotated. Comparative genomic analysis between Dan340 and other maize lines identified that 1806 genes from 359 gene families were specific to Dan340. Conclusions Our genome assembly and annotation provide a valuable resource for improving maize breeding and further understanding the intraspecific genome diversity in maize.

A DNA fingerprint database is an efficient, stable, and automated tool for plant molecular research that can provide comprehensive technical support for multiple fields of study, such as pan-genome analysis and crop breeding. However, constructing a DNA fingerprint database for plants requires significant resources for data output, storage, analysis, and quality control. Large amounts of heterogeneous data must be processed efficiently and accurately. Thus, we developed plant SNP database management system (PSNPdms) using an open-source web server and free software that is compatible with single nucleotide polymorphism (SNP), insertion–deletion (InDel) markers, Kompetitive Allele Specific PCR (KASP), SNP array platforms, and 23 species. It fully integrates with the KASP platform and allows for graphical presentation and modification of KASP data. The system has a simple, efficient, and versatile laboratory personnel management structure that adapts to complex and changing experimental needs with a simple workflow process. PSNPdms internally provides effective support for data quality control through multiple dimensions, such as the standardized experimental design, standard reference samples, fingerprint statistical selection algorithm, and raw data correlation queries. In addition, we developed a fingerprint-merging algorithm to solve the problem of merging fingerprints of mixed samples and single samples in plant detection, providing unique standard fingerprints of each plant species for construction of a standard DNA fingerprint database. Different laboratories can use the system to generate fingerprint packages for data interaction and sharing. In addition, we integrated genetic analysis into the system to enable drawing and downloading of dendrograms. PSNPdms has been widely used by 23 institutions and has proven to be a stable and effective system for sharing data and performing genetic analysis. Interested researchers are required to adapt and further develop the system.

The high variability and somatic stability of DNA fingerprints can be used to identify individuals, which is of great value in plant breeding. DNA fingerprint databases are essential and important tools for plant molecular research because they provide powerful technical and information support for crop breeding, variety quality control, variety right protection, and molecular marker-assisted breeding. Building a DNA fingerprint database involves the production of large amounts of heterogeneous data for which storage, analysis, and retrieval are time and resource consuming. To process the large amounts of data generated by laboratories and conduct quality control, a database management system is urgently needed to track samples and analyze data. We developed the plant international DNA-fingerprinting system (PIDS) using an open source web server and free software that has automatic collection, storage, and efficient management functions based on merging and comparison algorithms to handle massive microsatellite DNA fingerprint data. PIDS also can perform genetic analyses. This system can match a corresponding capillary electrophoresis image on each primer locus as fingerprint data to upload to the server. PIDS provides free customization and extension of back-end functions to meet the requirements of different laboratories. This system can be a significant tool for plant breeders and can be applied in forensic science for human fingerprint identification, as well as in virus and microorganism research.