Spiked human substantia nigra proteome data set for use as a spectral library for protein modelling and protein mapping.

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The data set can be used as a spectral library for analysis of the human brain; especially for analysis of human substantia nigra, for example, in the context of Parkinson's disease. Obtaining a sufficient amount of high-quality substantia nigra tissue is the key limiting factor for establishing a brain region-specific spectral library. Hence, combining existing spectral libraries for data-independent acquisition analysis (DIA) can overcome this major limitation. Moreover, these data can be used to map brain region-specific proteins and to model brain region-specific pathways. Both can improve our understanding of the functioning of the brain in greater depth. In addition, these data can also be used to determine the optimal settings for measuring proteins and peptides of interest. To create the substantia nigra-specific spectral library, the tissue was first homogenized and then fractionated via different types of SDS gel electrophoresis, resulting in 18 fractions. These fractions were analysed in triplicate by nanoHPLC-ESI-MS/MS, resulting in 54 data files. 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The samples were spiked with iRT peptides.•The spiked data set can be used as a spectral library for human brain tissue analysis in DIA mode, for example, in the context of Parkinson’s disease.•The library can be used to map proteins in different brain regions.•The library may help to determine optimized parameters for the identification of proteins/peptides of interest.•The library can be used to model pathways located within specific brain regions."]], "row_count": 1, "column_count": 1, "numeric_cells": 0, "header_rows": 0, "locator": "section[1]/table-wrap[2]#undtbl2"}}, {"id": "asset:table:pmid:31372383:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:463e0fcaf3fd05cee195ffa01955bf416160e85cd8ad6ec208bacee708693d5c", "bytes": 444, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[9]/table-wrap[1]#tbl1", "version": null, "note": "Solvent gradient profile for the elution of amino acids.", "structure": {"pmcid": "PMC6660433", "section_index": 9, "section_title": "Amino acid analysis", "row_count": 10, "column_count": 3}, "table": {"label": "Table 1", "caption": "Solvent gradient profile for the elution of amino acids.", "rows": [["Time (min)", "% Solvent A", "% Solvent B"], ["0", "99.9", "0.1"], ["0.54", "99.9", "0.1"], ["5.74", "90.9", "0.1"], ["7.74", "78.8", "21.2"], ["8.04", "40.4", "59.6"], ["8.05", "10.0", "90"], ["8.64", "10.0", "90"], ["8.73", "99.9", "0.1"], ["9.50", "99.9", "0.1"]], "row_count": 10, "column_count": 3, "numeric_cells": 27, "header_rows": 1, "locator": "section[9]/table-wrap[1]#tbl1"}}, {"id": "asset:table:pmid:31372383:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:5dbf12d4001b376dac2c606f84fa817b13c7c24c8b24c29f5ce02200ca1fb426", "bytes": 493, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#tbl2", "version": null, "note": "Solvent gradient profile for the elution of peptides.", "structure": {"pmcid": "PMC6660433", "section_index": 11, "section_title": "nanoHPLCsettings", "row_count": 22, "column_count": 2}, "table": {"label": "Table 2", "caption": "Solvent gradient profile for the elution of peptides.", "rows": [["Time (min)", "% Solvent B"], ["0", "5"], ["7", "5"], ["20", "8"], ["30", "10"], ["45", "12"], ["75", "15"], ["105", "19"], ["113", "21"], ["118", "23"], ["121", "25"], ["124", "27"], ["127", "30"], ["129", "34"], ["132", "37"], ["136", "43"], ["142", "52"], ["148", "60"], ["149", "95"], ["154", "95"], ["155", "5"], ["160", "5"]], "row_count": 22, "column_count": 2, "numeric_cells": 42, "header_rows": 1, "locator": "section[11]/table-wrap[1]#tbl2"}}], "gaps": ["asset:data:geo:PXD011076 not downloaded", "asset:data:geo:PXD011076 licence not read"]}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "operations"]}, "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": "method", "blocked_on": "method:not_determinable", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (not named), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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