PeachVar-DB: A Curated Collection of Genetic Variations for the Interactive Analysis of Peach Genome Data

Genetic dissection of fruit maturity date in apricot (P. armeniaca L.) through a Single Primer Enrichment Technology (SPET) approach

Background

Single primer enrichment technology (SPET) is an emerging and increasingly popular solution for high-throughput targeted genotyping in plants. Although SPET requires a priori identification of polymorphisms for probe design, this technology has potentially higher reproducibility and transferability compared to other reduced representation sequencing (RRS) approaches, also enabling the discovery of closely linked polymorphisms surrounding the target one.

Results

The potential for SPET application in fruit trees was evaluated by developing a 25K target SNPs assay to genotype a panel of apricot accessions and progenies. A total of 32,492 polymorphic sites were genotyped in 128 accessions (including 8,188 accessory non-target SNPs) with extremely low levels of missing data and a significant correlation of allelic frequencies compared to whole-genome sequencing data used for array design. Assay performance was further validated by estimating genotyping errors in two biparental progenies, resulting in an overall 1.8% rate. SPET genotyping data were used to infer population structure and to dissect the architecture of fruit maturity date (MD), a quantitative reproductive phenological trait of great agronomical interest in apricot species. Depending on the year, GWAS revealed loci associated to MD on several chromosomes. The QTLs on chromosomes 1 and 4 (the latter explaining most of the phenotypic variability in the panel) were the most consistent over years and were further confirmed by linkage mapping in two segregating progenies.

Conclusions

Besides the utility for marker assisted selection and for paving the way to in-depth studies to clarify the molecular bases of MD trait variation in apricot, the results provide an overview of the performance and reliability of SPET for fruit tree genetics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08901-1.

Less is more: natural variation disrupting a miR172 gene at the di locus underlies the recessive double-flower trait in peach (P. persica L. Batsch)

BACKGROUND

With the domestication of ornamental plants, artificial selective pressure favored the propagation of mutations affecting flower shape, and double-flower varieties are now readily available for many species. In peach two distinct loci control the double-flower phenotype: the dominant Di2 locus, regulated by the deletion of the binding site for miR172 in the euAP2 PETALOSA gene Prupe.6G242400, and the recessive di locus, of which the underlying factor is still unknown.

RESULTS

Based on its genomic location a candidate gene approach was used to identify genetic variants in a diverse panel of ornamental peach accessions and uncovered three independent mutations in Prupe.2G237700, the gene encoding the transcript for microRNA miR172d: a ~5.0 Kb LTR transposable element and a ~1.2 Kb insertion both positioned upstream of the sequence encoding the pre-miR172d within the transcribed region of Prupe.2G237700, and a ~9.5 Kb deletion encompassing the whole gene sequence. qRT-PCR analysis confirmed that expression of pre-miR172d was abolished in di/di genotypes homozygous for the three variants.

CONCLUSIONS

Collectively, PETALOSA and the mutations in micro-RNA miR172d identified in this work provide a comprehensive collection of the genetic determinants at the base of the double-flower trait in the peach germplasms.

CottonGVD: A Comprehensive Genomic Variation Database for Cultivated Cottons

Cultivated cottons are the most important economic crop, which produce natural fiber for the textile industry. In recent years, the genetic basis of several essential traits for cultivated cottons has been gradually elucidated by decoding their genomic variations. Although an abundance of resequencing data is available in public, there is still a lack of a comprehensive tool to exhibit the results of genomic variations and genome-wide association study (GWAS). To assist cotton researchers in utilizing these data efficiently and conveniently, we constructed the cotton genomic variation database (CottonGVD; http://120.78.174.209/ or http://db.cngb.org/cottonGVD). This database contains the published genomic information of three cultivated cotton species, the corresponding population variations (SNP and InDel markers), and the visualized results of GWAS for major traits. Various built-in genomic tools help users retrieve, browse, and query the variations conveniently. The database also provides interactive maps (e.g., Manhattan map, scatter plot, heatmap, and linkage disequilibrium block) to exhibit GWAS and expression GWAS results. Cotton researchers could easily focus on phenotype-associated loci visualization, and they are interested in and screen for candidate genes. Moreover, CottonGVD will continue to update by adding more data and functions.

Identifying sources of metabolomic diversity and reconfiguration in peach fruit: taking notes for quality fruit improvement

The metabolomic content determines many of the important features of a fruit, such as its taste, flavor, color, nutritional value, and abiotic or biotic resistance. Peach (Prunus persica (L.) Batsch) is one of the best genetically characterized species used as a model for Rosaceae, the drupes of which are a source of minerals, vitamins, fiber, and antioxidant compounds for healthy diets around the world. During the last few years, a great advance in the analysis of the metabolic diversity and reconfiguration in different peach varieties in response to developmental and environmental factors has occurred. These studies have shown that the great phenotypic diversity among different peach varieties is correlated with differential metabolomic content. Besides, the fruit metabolome of each peach variety is not static; on the contrary, it is drastically configured in response to both developmental and environmental signals, and moreover, it was found that these metabolic reconfigurations are also variety dependent. In the present review, the main sources of metabolic diversity and conditions that induce modifications in the peach fruit metabolome are summarized. It is postulated that comparison of the metabolic reconfigurations that take place among the fruits from different varieties may help us better understand peach fruit metabolism and their key drivers, which in turn may aid in the future design of high‐quality peach fruits.

The Di2/pet Variant in the PETALOSA Gene Underlies a Major Heat Requirement-Related QTL for Blooming Date in Peach [Prunus persica (L.) Batsch]

Environmental adaptation of deciduous fruit trees largely depends on their ability to synchronize growth and development with seasonal climate change. Winter dormancy of flower buds is a key process to prevent frost damage and ensure reproductive success. Temperature is a crucial environmental stimulus largely influencing the timing of flowering, only occurring after fulfillment of certain temperature requirements. Nevertheless, genetic variation affecting chilling or heat-dependent dormancy release still remains largely unknown. In this study, a major QTL able to delay blooming date in peach by increasing heat requirement was finely mapped in three segregating progenies, revealing a strict association with a genetic variant (petDEL) in a PETALOSA gene, previously shown to also affect flower morphology. Analysis of segregating genome-edited tobacco plants provided further evidence of the potential ability of PET variations to delay flowering time. Potential applications of the petDEL variant for improving phenological traits in peach are discussed.

BnaGVD: A genomic variation database of rapeseed (Brassica napus)

Rapeseed (Brassica napus L.) is a typical polyploid crop and one of the most important oilseed crops worldwide. With the rapid progress on high-throughput sequencing technologies and the reduction of sequencing cost, large-scale genomic data of a specific crop have become available. However, raw sequence data are mostly deposited in the sequence read archive of the National Center of Biotechnology Information (NCBI) and the European Nucleotide Archive (ENA), which is freely accessible to all researchers. Extensive tools for practical purposes should be developed to efficiently utilize these large raw data. Here, we report a web-based rapeseed genomic variation database (BnaGVD, http://rapeseed.biocloud.net/home) from which genomic variations, such as single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) across a world-wide collection of rapeseed accessions, can be referred. The current release of the BnaGVD contains 34,591,899 high-quality SNPs and 12,281,923 high-quality InDels and provides search tools to retrieve genomic variations and gene annotations across 1,007 accessions of worldwide rapeseed germplasm. We implement a variety of built-in tools (e.g., BnaGWAS, BnaPCA, and BnaStructure) to help users perform in-depth analyses. We recommend this web resource for accelerating studies on the functional genomics and screening of molecular markers for rapeseed breeding.

RAP: A Web Tool for RNA-Seq Data Analysis

Since 1950 main studies of RNA regarded its role in the protein synthesis. Later insights showed that only a small portion of RNA codes for proteins where the rest could have different functional roles. With the advent of Next Generation Sequencing (NGS) and in particular with RNA-seq technology the cost of sequencing production dropped down. Among the NGS application areas, the transcriptome analysis, that is, the analysis of transcripts in a cell, their quantification for a specific developmental stage or treatment condition, became more and more adopted in the laboratories. As a consequence in the last decade new insights were gained in the understanding of both transcriptome complexity and involvement of RNA molecules in cellular processes. For what concerns computational advances, bioinformatics research developed new methods for analyzing RNA-seq data. The comparison among transcriptome profiles from several samples is often a difficult task for nonexpert programmers. Here, in this chapter, we introduce RAP (RNA-Seq Analysis Pipeline), a completely automated web tool for transcriptome analysis. It is a user-friendly web tool implementing a detailed transcriptome workflow to detect differential expressed genes and transcript, identify spliced junctions and constitutive or alternative polyadenylation sites and predict gene fusion events. Through the web interface the researchers can get all this information without any knowledge of the underlying High Performance Computing infrastructure.

ELIXIR-IT HPC@CINECA: high performance computing resources for the bioinformatics community

BACKGROUND

The advent of Next Generation Sequencing (NGS) technologies and the concomitant reduction in sequencing costs allows unprecedented high throughput profiling of biological systems in a cost-efficient manner. Modern biological experiments are increasingly becoming both data and computationally intensive and the wealth of publicly available biological data is introducing bioinformatics into the "Big Data" era. For these reasons, the effective application of High Performance Computing (HPC) architectures is becoming progressively more recognized also by bioinformaticians. Here we describe HPC resources provisioning pilot programs dedicated to bioinformaticians, run by the Italian Node of ELIXIR (ELIXIR-IT) in collaboration with CINECA, the main Italian supercomputing center.

RESULTS

Starting from April 2016, CINECA and ELIXIR-IT launched the pilot Call "ELIXIR-IT HPC@CINECA", offering streamlined access to HPC resources for bioinformatics. Resources are made available either through web front-ends to dedicated workflows developed at CINECA or by providing direct access to the High Performance Computing systems through a standard command-line interface tailored for bioinformatics data analysis. This allows to offer to the biomedical research community a production scale environment, continuously updated with the latest available versions of publicly available reference datasets and bioinformatic tools. Currently, 63 research projects have gained access to the HPC@CINECA program, for a total handout of ~ 8 Millions of CPU/hours and, for data storage, ~ 100 TB of permanent and ~ 300 TB of temporary space.

CONCLUSIONS

Three years after the beginning of the ELIXIR-IT HPC@CINECA program, we can appreciate its impact over the Italian bioinformatics community and draw some considerations. Several Italian researchers who applied to the program have gained access to one of the top-ranking public scientific supercomputing facilities in Europe. Those investigators had the opportunity to sensibly reduce computational turnaround times in their research projects and to process massive amounts of data, pursuing research approaches that would have been otherwise difficult or impossible to undertake. Moreover, by taking advantage of the wealth of documentation and training material provided by CINECA, participants had the opportunity to improve their skills in the usage of HPC systems and be better positioned to apply to similar EU programs of greater scale, such as PRACE. To illustrate the effective usage and impact of the resources awarded by the program - in different research applications - we report five successful use cases, which have already published their findings in peer-reviewed journals.

Deletion of the miR172 target site in a TOE-type gene is a strong candidate variant for dominant double-flower trait in Rosaceae

Double flowers with supernumerary petals have been selected by humans for their attractive appearance and commercial value in several ornamental plants, including Prunus persica (peach), a recognized model for Rosaceae genetics and genomics. Despite the relevance of this trait, knowledge of the underlying genes is limited. Of two distinct loci controlling the double-flower phenotype in peach, we focused on the dominant Di2 locus. High-resolution linkage mapping in five segregating progenies delimited Di2 to an interval spanning 150 858 bp and 22 genes, including Prupe.6G242400 encoding an euAP2 transcription factor. Analyzing genomic resequencing data from single- and double-flower accessions, we identified a deletion spanning the binding site for miR172 in Prupe.6G242400 as a candidate variant for the double-flower trait, and we showed transcript expression for both wild-type and deleted alleles. Consistent with the proposed role in controlling petal number, Prupe.6G242400 is expressed in buds at critical times for floral development. The indelDi2 molecular marker designed on this sequence variant co-segregated with the phenotype in 621 progenies, accounting for the dominant inheritance of the Di2 locus. Further corroborating the results in peach, we identified a distinct but similar mutation in the ortholog of Prupe.6G242400 in double-flower roses. Phylogenetic analysis showed that these two genes belong to a TARGET OF EAT (TOE)-type clade not represented in Arabidopsis, indicating a divergence of gene functions between AP2-type and TOE-type factors in Arabidopsis and other species. The identification of orthologous candidate genes for the double-flower phenotype in two important Rosaceae species provides valuable information to understand the genetic control of this trait in other major ornamental plants.