An integrated strategy for target SSR genotyping with toleration of nucleotide variations in the SSRs and flanking regions.

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.

PidTools: Algorithm and web tools for crop pedigree identification analysis.

Crop pedigrees incorporate information on the kinship and genetic evolutionary history of breeding materials. Complete and accurate pedigree information is vital for effective genetic improvement of crops and maximal exploitation of heterosis in crop production. It is difficult for breeders to accurately extrapolate the selection of germplasm resources with missing genealogical information based on breeding experience. In this study, an algorithm called PidTools was developed, consisting of five sets of algorithms from three core modules, for accurate pedigree identification analysis. The algorithms and associated tools are suitable for all crops, for the reconstruction and visualization of a complete pedigree for breeding materials. The algorithm and tools were validated with the model crop maize. A genotype database was constructed using Maize6H-60K array data from 5791 maize inbred lines. The pedigree of the maize inbred line Jing72464 was identified without prior provision of any parental information. The pedigree information for Zheng58 was fully identified at the genome-wide scale. With regard to group identification, the parents of a doubled-haploid group were identified based on the genotyping data. The pedigree of 21 Dan340 derived lines were visualized using PidTools. The algorithms are incorporated into a user-friendly online analytical platform, PidTools-WS, with an associated customizable toolkit program, PidTools-CLI. These analytical tools and the present results provide useful information for future maize breeding. The PidTools online analysis platform is available at https://PidTools.plantdna.site/.

A chromosome-level genome assembly and annotation of the maize elite breeding line Dan340.

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.

HTPdb and HTPtools: Exploiting maize haplotype-tag polymorphisms for germplasm resource analyses and genomics-informed breeding.

Along with rapid advances in high-throughput-sequencing technology, the development and application of molecular markers has been critical for the progress that has been made in crop breeding and genetic research. Desirable molecular markers should be able to rapidly genotype tens of thousands of breeding accessions with tens to hundreds of markers. In this study, we developed a multiplex molecular marker, the haplotype-tag polymorphism (HTP), that integrates Maize6H-60K array data from 3,587 maize inbred lines with 6,375 blocks from the recombination block map. After applying strict filtering criteria, we obtained 6,163 highly polymorphic HTPs, which were evenly distributed in the genome. Furthermore, we developed a genome-wide HTP analysis toolkit, HTPtools, which we used to establish an HTP database (HTPdb) covering the whole genomes of 3,587 maize inbred lines commonly used in breeding. A total of 172,921 non-redundant HTP allelic variations were obtained. Three major HTPtools modules combine seven algorithms (e.g., chain Bayes probability and the heterotic-pattern prediction algorithm) and a new plotting engine named "BCplot" that enables rapid visualization of the background information of multiple backcross groups. HTPtools was designed for big-data analyses such as complex pedigree reconstruction and maize heterotic-pattern prediction. The HTP-based analytical strategy and the toolkit developed in this study are applicable for high-throughput genotyping and for genetic mapping, germplasm resource analyses, and genomics-informed breeding in maize.

PIDS: A User-Friendly Plant DNA Fingerprint Database Management 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.