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Deep sequencing transcriptional fingerprinting of rice kernels for dissecting grain quality traits

Overview of attention for article published in BMC Genomics, December 2015
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Title
Deep sequencing transcriptional fingerprinting of rice kernels for dissecting grain quality traits
Published in
BMC Genomics, December 2015
DOI 10.1186/s12864-015-2321-7
Pubmed ID
Authors

Chiara Biselli, Paolo Bagnaresi, Daniela Cavalluzzo, Simona Urso, Francesca Desiderio, Gabriele Orasen, Alberto Gianinetti, Federico Righettini, Massimo Gennaro, Rosaria Perrini, Manel Ben Hassen, Gian Attilio Sacchi, Luigi Cattivelli, Giampiero Valè

Abstract

Rice represents one the most important foods all over the world. In Europe, Italy is the first rice producer and Italian production is driven by tradition and quality. All main rice grain quality traits, like cooking properties, texture, gelatinization temperature, chalkiness and yield, are related to the content and composition of starch and seed-storage proteins in the endosperm and to grain shape. In addition, a number of nutraceutical compounds and allergens are known to have a significant effect on grain quality determination. To investigate the genetic bases underlying the qualitative differences that characterize traditional Italian rice cultivars, a comparative RNA-Seq-based transcriptomic analysis of developing caryopsis was conducted at 14 days after flowering on six popular Italian varieties (Carnaroli, Arborio, Balilla, Vialone Nano, Gigante Vercelli and Volano) phenotypically differing for qualitative grain-related traits. Co-regulation analyses of differentially expressed genes showing the same expression patterns in the six genotypes highlighted clusters of loci up or down-regulated in specific varieties, with respect to the others. Among them, we detected loci involved in cell wall biosynthesis, protein metabolism and redox homeostasis, classes of genes affecting in chalkiness determination. Moreover, loci encoding for seed-storage proteins, allergens or involved in the biosynthesis of specific nutraceutical compounds were also present and specifically regulated in the different clusters. A wider investigation of all the DEGs detected in pair-wise comparisons revealed transcriptional variation, among the six genotypes, for quality-related loci involved in starch biosynthesis (e.g. GBSSI, starch synthases and AGPase), genes encoding for transcription factors, additional seed storage proteins, allergens or belonging to additional nutraceutical compounds biosynthetic pathways and loci affecting grain size. Putative functional SNPs associated to amylose content in starch, gelatinization temperature and grain size were also identified. The present work represents a more extended phenotypic characterization of a set of rice accessions that present a wider genetic variability than described nowadays in literature. The results provide the first transcriptional picture for several of the grain quality differences observed among the Italian rice varieties analyzed and reveal that each variety is characterized by the over-expression of a peculiar set of loci affecting grain appearance and quality. A list of candidates and SNPs affecting specific grain properties has been identified offering a starting point for further works aimed to characterize genes and molecular markers for breeding programs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 2%
Unknown 50 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 31%
Student > Ph. D. Student 6 12%
Student > Master 4 8%
Other 3 6%
Professor > Associate Professor 3 6%
Other 5 10%
Unknown 14 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 49%
Biochemistry, Genetics and Molecular Biology 4 8%
Computer Science 2 4%
Nursing and Health Professions 1 2%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 2 4%
Unknown 16 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 December 2015.
All research outputs
#15,352,477
of 22,836,570 outputs
Outputs from BMC Genomics
#6,694
of 10,655 outputs
Outputs of similar age
#228,307
of 389,451 outputs
Outputs of similar age from BMC Genomics
#243
of 324 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,655 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 389,451 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 324 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.