Transcriptomic and proteomic data in developing tomato fruit.

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For transcriptome analysis, Illumina-sequenced libraries were mapped on the tomato genome with the aim to obtain absolute quantification of mRNA abundance. To achieve this, spikes were added at the beginning of the RNA extraction procedure. From 34,725 possible transcripts identified in the tomato, 22,877 were quantified in at least one of the nine developmental stages. For the proteome analysis, label-free liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) was used. Peptide ions, and subsequently the proteins from which they were derived, were quantified by integrating the signal intensities obtained from extracted ion currents (XIC) with the MassChroQ software. Absolute concentrations of individual proteins were estimated for 2375 proteins by using a mixed effects model from log10-transformed intensities and normalized to the total protein content. Transcriptomics data are available via GEO repository with accession number GSE128739. The raw MS output files and identification data were deposited on-line using the PROTICdb database (http://moulon.inra.fr/protic/tomato_fruit_development) and MS proteomics data have also been deposited to the ProteomeXchange with the dataset identifier PXD012877. The main added value of these quantitative datasets is their use in a mathematical model to estimate protein turnover in developing tomato fruit.", "claims": [{"id": "claim-method", "statement": "Briefly, peptide ion intensities were log10 transformed and quantified using a mixed effect model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31909114", "locator": "sections[13] \"Peptide and protein quantification\" paragraph 3", "quote": "Briefly, peptide ion intensities were log10 transformed and quantified using a mixed effect model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Transcriptomic data are available via GEO with accession number GSE128739. 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One sample results from the pooling of at least 15 fruits. Replicates 1, 2 and 3 correspond to the 5th, 6th and 7th truss respectively."], ["Data source location", "INRA France."], ["Data accessibility", "Transcriptomics data are available via Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo) an international public repository with accession number GSE128739For proteomics data, the raw MS output files and identification data were deposited on-line using the PROTICdb database (http://moulon.inra.fr/protic/tomato_fruit_development)The proteomics data have also been deposited to the ProteomeXchange with the dataset identifier PXD128739 (http://proteomecentral.proteomexchange.org)"], ["Related research article", "[2] Isma Belouah, Christine Nazaret, Pierre Pétriacq, Sylvain Prigent, Camille Bénard, Virginie Mengin, Mélisande Blein-Nicolas, Alisandra K. Denton, Thierry Balliau, Ségolène Augé, Olivier Bouchez, Jean-Pierre Mazat, Mark Stitt, Björn Usadel, Michel Zivy, Bertrand Beauvoit, Yves Gibon, Sophie ColombiéModeling Protein Destiny in Developing FruitPlant Physiology 2019. DOI: https://doi.org/10.1104/pp.19.00086"]], "row_count": 10, "column_count": 2, "numeric_cells": 5, "header_rows": 0, "locator": "section[1]/table-wrap[1]#undtbl1"}}, {"id": "asset:table:pmid:31909114:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:e2867b8c5a21692e7f6763882677225dc91194a36338aed6007360319c994a84", "bytes": 645, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[1]/table-wrap[2]#undtbl2", "version": null, "note": null, "structure": {"pmcid": "PMC6938935", "section_index": 1, "section_title": "p", "row_count": 1, "column_count": 1}, "table": {"label": null, "caption": null, "rows": [["Value of the data•The paired quantitative transcript-protein data with a sufficient resolution in time are rather rare, making it a valuable dataset for the plant science community.•The dataset should be of interest to researchers looking for time-series and quantitative data of both transcripts and proteins.•The dataset constitute a great potential for using this data set to compute not only protein turnover rates but also deduct regulatory mechanisms and identify candidate genes."]], "row_count": 1, "column_count": 1, "numeric_cells": 0, "header_rows": 0, "locator": "section[1]/table-wrap[2]#undtbl2"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "regression_modelling", "specific": "mixed_effects", "label": "Regression modelling", "dsl_operations": [], "missing_operations": ["regression_modelling_mixed_effects"], "expressible": false, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Briefly, peptide ion intensities were log10 transformed and quantified using a mixed effect model."}}], "targets": [], "gaps": ["method.regression_modelling_mixed_effects"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["regression_modelling_mixed_effects"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: mixed_effects."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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