Grape-RNA: A Database for the Collection, Evaluation, Treatment, and Data Sharing of Grape RNA-Seq Datasets

Abstract

Since its inception, RNA sequencing (RNA-seq) has become the most effective way to study gene expression. After more than a decade of development, numerous RNA-seq datasets have been created, and the full utilization of these datasets has emerged as a major issue. In this study, we built a comprehensive database named Grape-RNA, which is focused on the collection, evaluation, treatment, and data sharing of grape RNA-seq datasets. This database contains 1529 RNA-seq samples, 112 microRNA samples from the public platform, and 485 RNA-seq in-house datasets sequenced by our lab. We classified these data into 25 conditions and provide the sample information, cleaned raw data, expression level, assembled unigenes, useful tools, and other relevant information to the users. Thus, this study provides data and tools that should be beneficial for researchers by allowing them to easily use the RNA-seq. The provided information can greatly contribute to grape breeding and genomic and biological research. This study may improve the usage of RNA-seq.

Vitis OneGenE: A Causality-Based Approach to Generate Gene Networks in Vitis vinifera Sheds Light on the Laccase and Dirigent Gene Families

“…A notable example of this approach is the PC algorithm [15], whose application to transcriptomic data could be done by iteratively subsetting the variables and combining the results afterward. This approach is adopted by NES 2 RA; a causal inference method applied to the entire VESPUCCI database to expand known local gene networks (LGNs) [16]. The high number of PC-algorithm runs required by the iterative subsetting is computed in a distributed way by the gene@home project that exploits the BOINC platform for volunteer computation [17].…”

“…The high number of PC-algorithm runs required by the iterative subsetting is computed in a distributed way by the gene@home project that exploits the BOINC platform for volunteer computation [17]. NES 2 RA produces an association network whose edges represent putative direct interactions between genes, as an interaction still exists despite a high number of attempts to remove it by individuating a separation set of variables.…”

“…Despite NES 2 RA's reported ability to identify gene interactions starting from a nondedicated large transcriptomic dataset, some limitations related to its practical use were, however, evident. First, the heavy computation of the LGN expansion cannot be requested directly by the user and may require up to several days to complete, even with the use of the BOINC distributed computing platform.…”

The Effect of Water Deficit on Two Greek Vitis vinifera L. Cultivars: Physiology, Grape Composition and Gene Expression during Berry Development

“…Considering that the overall phenotype, such as grape quality traits, is determined by the combinatorial action of the genotype and the environment, it is fundamental to understand the effect of environment on the gene expression networks that facilitate berry development and quality features. The availability of grapevine genome [ 24 ] and high-throughput transcriptomic methods, like microarrays and RNA sequencing, have enriched our knowledge about tissue-specific, development- or environment-controlled gene expression patterns [ 25 ]. Specifically, in the case of water deficit, it is known that approximately 13% of grape genes are differentially expressed, mainly in the skin and pulp [ 10 ].…”

Should Starch Metabolism Be a Key Point of the Climacteric vs. Non-climacteric Fruit Definition?

“…For apple, there is a similar web site, called AppleMDO, published recently by Da et al ( 2019 ). For collecting grape RNAseq data and making them searchable, a new platform has been created, called Grape-RNA (Wang et al, 2020 ) and for Citrus sinensis a recent data basis has been created too (Feng et al, 2019 ). Mining such databases and others, for other fruit species, with a focus on the few starch synthesis genes, by comparing developing stages of climacteric and non-climacteric fruit, would probably generate new insight into differences between these two fruit classes, and may reinforce the fact that starch should be a cornerstone of the definition of climacteric vs. non-climacteric.…”