Advent of genomics in blueberry

Die, José V.; Rowland, Lisa J.

doi:10.1007/s11032-013-9893-1

Cited by 21 publications

(18 citation statements)

References 43 publications

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“…In addition 9 NBS type genes, which lacked the LRR domain, were detected containing TCN, CN and XN (Table 1 ). Approximately 25,000 protein-encoding genes are estimated in the draft genome assembly 23 . Therefore, the NBS-LRR genes accounted for approximately 0.4% of the predicted ORFs.…”

Section: Resultsmentioning

confidence: 99%

Genome-scale examination of NBS-encoding genes in blueberry

Die

Román

et al. 2018

Sci Rep

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Blueberry is an important crop worldwide. It is, however, susceptible to a variety of diseases, which can lead to losses in yield and fruit quality. Although screening studies have identified resistant germplasm for some important diseases, still little is known about the molecular basis underlying that resistance. The most predominant type of resistance (R) genes contains nucleotide binding site and leucine rich repeat (NBS-LRR) domains. The identification and characterization of such a gene family in blueberry would enhance the foundation of knowledge needed for its genetic improvement. In this study, we searched for and found a total of 106 NBS-encoding genes (including 97 NBS-LRR) in the current blueberry genome. The NBS genes were grouped into eleven distinct classes based on their domain architecture. More than 22% of the NBS genes are present in clusters. Ten genes were mapped onto seven linkage groups. Phylogenetic analysis grouped these genes into two major clusters based on their structural variation, the first cluster having toll and interleukin-1 like receptor (TIR) domains and most of the second cluster containing a coiled-coil domain. Our study provides new insight into the NBS gene family in blueberry and is an important resource for the identification of functional R-genes.

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Section: Resultsmentioning

confidence: 99%

Genome-scale examination of NBS-encoding genes in blueberry

Die

Román

et al. 2018

Sci Rep

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“…But the use of molecular markers in blueberry research was limited until the recent advent of next-generation sequencing (NGS) technologies, due to the complexity of this group [ 25 , 26 , 27 ]. Most of the genomic data obtained to date for blueberry have been summarized and are available on different websites [ 28 ]. A genome-sequencing project of a diploid V .…”

Section: Introductionmentioning

confidence: 99%

Genetic diversity assessed by genotyping by sequencing (GBS) and for phenological traits in blueberry cultivars

Čampa

Fernández

2018

PLoS ONE

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Blueberry is a small fruit crop which includes a complex group of different Vaccinium species of various ploidy levels. Commercial blueberries have been grown in Europe most recently, so there is not much information available about their adaptation into new regions. In this work we investigated adaptation to the environmental conditions of northern Spain, in terms of flowering and ripening seasons, of a set of 70 blueberry cultivars including several of the most important cultivated American species (V. corymbosum, V. virgatum, V. macrocarpon and V. uliginosum) in order to identify which types are best-suited in this geographical area of Europe. Most materials showed high chilling requirements for flowering under local conditions, while materials with low-chilling requirements showed problems in the maturation process of the flowers. Most cultivars were early or mid-season while a relative lack of late-season cultivars was observed. GBS was used for the analysis of genetic diversity in this sample of 70 cultivars. A total of 5255 SNP markers were obtained and a cluster analysis revealed three main groups associated with the ploidy level of the species. A Principal Component Analysis revealed a grouping of the V. corymbosum cultivars according to their chilling requirements. A total of 29 SNPs were identified as being highly informative for diversity analysis and potentially useful for cultivar identification and for breeding purposes. The results obtained from this research should contribute to the expansion of this crop, as well as providing data about genetic diversity useful for the preservation of genetic resources or for future breeding programs.

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“…Since its introduction about 20 years ago, RT-qPCR has become the most common technique for RNA expression measurements. Despite the introduction of next-generation sequencing (NGS) for gene expression analysis in plant genomics (Jain, 2012 ; Die and Rowland, 2013 ), qPCR remains an essential technique for confirmation of NGS findings. One decade ago, increased awareness of problems associated with producing high-quality and reliable data from RT-qPCR came to the forefront (Bustin, 2002 ).…”

Section: Discussionmentioning

confidence: 99%

Design and Sampling Plan Optimization for RT-qPCR Experiments in Plants: A Case Study in Blueberry

et al. 2016

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The qPCR assay has become a routine technology in plant biotechnology and agricultural research. It is unlikely to be technically improved, but there are still challenges which center around minimizing the variability in results and transparency when reporting technical data in support of the conclusions of a study. There are a number of aspects of the pre- and post-assay workflow that contribute to variability of results. Here, through the study of the introduction of error in qPCR measurements at different stages of the workflow, we describe the most important causes of technical variability in a case study using blueberry. In this study, we found that the stage for which increasing the number of replicates would be the most beneficial depends on the tissue used. For example, we would recommend the use of more RT replicates when working with leaf tissue, while the use of more sampling (RNA extraction) replicates would be recommended when working with stems or fruits to obtain the most optimal results. The use of more qPCR replicates provides the least benefit as it is the most reproducible step. By knowing the distribution of error over an entire experiment and the costs at each step, we have developed a script to identify the optimal sampling plan within the limits of a given budget. These findings should help plant scientists improve the design of qPCR experiments and refine their laboratory practices in order to conduct qPCR assays in a more reliable-manner to produce more consistent and reproducible data.

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Advent of genomics in blueberry

Cited by 21 publications

References 43 publications

Genome-scale examination of NBS-encoding genes in blueberry

Genome-scale examination of NBS-encoding genes in blueberry

Genetic diversity assessed by genotyping by sequencing (GBS) and for phenological traits in blueberry cultivars

Design and Sampling Plan Optimization for RT-qPCR Experiments in Plants: A Case Study in Blueberry

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