Goals

The grapevine research community generates large omics datasets to address the major challenge for viticulture and oenology. These datasets are processed in the context of a single study trying to answer one biological question. The value of data from individual experiments is much enhanced when considered in a wider context through meta-analysis. The meta-analysis of these integrated datasets helps to identify mechanisms underlying the interactions between plants, their environment and plant management techniques. However, the interpretation and re-processing of data requires quality metadata to provide appropriate context and in general a compliance to the FAIR principles.

There are several reasons behind developing specific guidelines for the grapevine research community:

• Current guidelines and ontologies supporting data standardization cannot always be applied directly to grapevine because its unique traits differ from model organisms. For that reason the Integrape consortium (COST action 17111, funded by the Horizon 2020 framework of the European Union) has developed specifications for grapevine data. The guidelines for submission of nucleotide sequences to ENA is one of them.

• The grapevine researchers have generally a biologist background, with a very diverse level of skills in computational biology. This is true for the members of the grapevine research community that submit data to public archives, such as ENA while these archives of the INSDC consortium are among the most cited tools of the community for data archival and data reuse through associated data portals.

This tutorial aims at (i) facilitating data submission itself and (ii) enhancing the quality of the metadata associated to the submission having in mind that there is currently neither clear direct benefits into publishing good metadata since it is not a prerequisite for publication. That is why we are aiming as providing a tutorial which help into making this process as straightforward and simple as possible and balancing what should be mandatory in terms of metadata and what is optional.

About submitting data to ENA

ENA accepts sequence reads and associated analyses (e.g. genome assemblies, annotations). Each deposited dataset is composed of data about the dataset or study, about the samples, details about the experiment and the sequence data files.

Once the submitting person/institute decides to make a study public, all data submitted to ENA is exchanged between International Nucleotide Sequence Database Collaboration (INSDC) partners: NCBI and DDBJ. That means in turn, that if new data (read files, experiments) should be added to an already public study, these data get public immediately. Aside from documentation pages, ENA provides submission tutorials and FAQs.

BioProject , BioStudies, Study and BioSample

One important concept to understand before starting submitting data to ENA or its counterpart at NCBI, SRA is the meaning and level of hierarchy between BioStudy, Study and Biosample. As data submitted at ENA are shared at NCBI SRA, a Study will be referred to as a BioProject at SRA, the Bioproject and Study accession number remain the same. At requests of authors it is possible to create an umbrella study that may group projects that are part of a single collaborative effort but represent distinct studies that differ in methodology, sample material, or resulting data type. It is stored in the BioStudies database at EMBL-EBI.

A study is composed of (bio)samples, i.e. physical objects that are composed of biological material and has, or will have data associated to. Here the data are raw reads.

Both studies and samples have a set of associated metadata. Importantly, metadata associated to a study can be extrapolated to the sample: in that case they will be identical between samples. Samples has several type of associated metadata:

• Basics details and organism details: generally mandatory data about sample identification

• Collection events: description of the sample collection process.

• Plant material description: metadata ideally allowing the precise identification of the organism from which the sample come from

• Variation event in comparative studies: experimental factors or phenotypical trait that vary between sample, justifying the comparative study.

• In addition, it is possible to register environment parameters for each sample.

Metadata associated with a study

• Green: required by ENA to proceed with submission.

• Yellow: Information that should appear in any submission.

• Blue: data depending of the type of experiment and depth of information the authors wish to provide

Some metadata are modelled in ENA databases in specific fields, while others are treated as tags.

Tags: Other attributes can be provided to add a deeper description of the study

Data submission step 2: register all RNA samples

“Samples” means here individual nucleic acid samples or tubes, not sequence data. It is not a question in this section of submitting the raw reads (fastq files): it will be done in the next submission step. Sample submission involves describing how the samples were obtained and identifing as precisely as possible the plant material their are derived from.

This can be done either by building an excel spreedsheet for submission or by using default checklists

Submission from checklists

It is in another document

Tool to create a custom list of metadata

To generate the tabulated file that you need to fill with your sample data, we propose below a tool that asks you a serie of questions for generating the appropriate field.

The tables at the end of the guidelines will give you more details and help on what is expected in each fields

The file should look as follow.

Manually fill in the information for all of your RNA samples.

Data submission step 3: submit the raw reads for each RNA sample

You must give the exact file names of the files you have put on the ftp, along with their md5.

Then click on submit, you will receive an email. Your RNAseq dataset is submitted! you can put your project to public.