Metadata Reporting Standards

Assay for Transposase-Accessible Chromatin using Sequencing (ATAC-seq)

ATACseq maps open, regulatory regions of the genome in cells or bulk tissue by exploiting the preference of a hyperactive Tn5 transposase for nucleosome-free chromatin. The Tn5 enzyme inserts sequencing adapters directly into accessible chromatin regions, and the resulting DNA fragments are sequenced to generate a genome-wide map of open chromatin. The data reveals the landscape of active regulatory elements—such as promoters and enhancers—providing insight into gene regulation and cell identity.

DOI: https://doi.org/10.35079/HBM846.LZCV.363

Published: August 25, 2026

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MD5 hash: aed7c453b82e7099a495ec56ddf4b7fe

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Schema

Legend: Text field Allowable value Radio Numeric

* Required

Attribute Type Description Allowable Values
Parent sample ID * The unique identifier from HuBMAP or SenNet for the sample (such as a block, section, or suspension) used to perform the assay. For instance, in an RNAseq assay, the parent sample would be the suspension, while in imaging assays, it would be the tissue section. If the assay is derived from multiple parent samples, this field should contain a comma-separated list of identifiers. Example: HBM386.ZGKG.235, HBM672.MKPK.442  
Lab ID An identifier assigned by the data provider to reference an external metadata record for the dataset. The external record may be maintained independently and can support dataset traceability and provenance tracking. Leave this field empty if no such identifier exists.  
Preparation protocol DOI * The DOI for the protocols.io page that details the assay or the procedures used for sample procurement and preparation. For example, in the case of an imaging assay, the protocol may start with tissue section staining and end with the generation of an OME-TIFF file. The documented protocol should also include any image processing steps involved in producing the final OME-TIFF. Example: https://dx.doi.org/10.17504/protocols.io.eq2lyno9qvx9/v1  
Dataset type * The specific type of dataset being produced. Example: RNAseq HiFi-Slide SNARE-seq2 COMET Visium (no probes) DESI Confocal Stereo-seq Visium (with probes) Molecular Cartography Virtual Histology DBiT-seq Seq-Scope CosMx Transcriptomics CyCIF Light Sheet iCLAP seqFISH ATACseq CosMx Proteomics Singular Genomics G4X Visium HD MERFISH 10X Multiome 4i PhenoCycler Second Harmonic Generation (SHG) Thick section Multiphoton MxIF MACSima CyTOF Olink MIBI Auto-fluorescence FACS Xenium DNA Methylation SIMS Cell DIVE CODEX GeoMx (NGS) MUSIC Pixel-seqV2 MALDI 2D Imaging Mass Cytometry Histology Enhanced Stimulated Raman Spectroscopy (SRS) Illumina Spatial ver0 DART-FISH Resolve RNAseq LC-MS nanoSPLITS GeoMx (nCounter) Raman Imaging RNAseq (with probes) Paired-Tag MS Lipidomics STARmap MPLEx
Analyte class * The analyte class which is the target molecule that the assay is measuring. Example: DNA DNA + RNA Nucleic acid + protein Chromatin Lipid + metabolite + protein RNA + protein RNA Metabolite Unsaturated lipid Lipid + metabolite Saturated lipid Lipid Protein Fluorochrome Collagen DNA Peptide Polysaccharide Endogenous fluorophore
Is targeted? * Indicates whether a specific molecule or set of molecules is targeted for detection or measurement by the assay. Example: Yes Yes No
Acquisition instrument vendor * The company that manufactures or supplies the acquisition instrument. An acquisition instrument is a device equipped with signal detection hardware and signal processing software. It captures signals produced by assays, such as variations in light intensity or color, or signals corresponding to molecular mass. If the instrument was custom-built or developed internally, enter “In-House”. Example: Illumina BGI Genomics Cytiva Thermo Fisher Scientific Zeiss Microscopy Complete Genomics 3DHISTECH GE Healthcare Leica Microsystems Akoya Biosciences NanoString Element Biosciences Andor Huron Digital Pathology Illumina Ionpath Waters In-House Resolve Biosciences Singular Genomics Vizgen Standard BioTools (Fluidigm) Sciex Bruker Evident Scientific (Olympus) Keyence Miltenyi Biotec Leica Biosystems Revvity Cytek Biosciences 10x Genomics Microscopes International Hamamatsu Motic
Acquisition instrument model * The specific model of the acquisition instrument, as manufacturers often offer various versions with differing features or sensitivities. These differences may be relevant to the processing or interpretation of the data. If the instrument was custom-built or developed internally, enter “In-House”. If the model is unknown, enter “Unknown”. Example: HiSeq 4000 SCN400 STELLARIS 5 BZ-X710 Pannoramic MIDI II Digital Scanner Not applicable MoticEasyScan One EVOS M7000 Orbitrap Astral NovaSeq X LSM 710 Confocal Microscope NanoZoomer 2.0-HT timsTOF Ultra 2 Lightsheet 7 Phenocycler-Fusion 1.0 DNBSEQ-T7 timsTOF Pro Unknown AVITI DMi8 Opera Phenix Plus HCS timsTOF Pro 2 Q Exactive UHMR Q Exactive timsTOF SCP Zyla 4.2 sCMOS Helios uScopeHXII-20 Orbitrap Fusion Tribrid Custom: Multiphoton QTRAP 5500 timsTOF Ultra BZ-X800 CyTOF 2 G4X Spatial Sequencer NextSeq 500 NanoZoomer S360 Hyperion Imaging System NovaSeq X Plus CyTOF XT NanoZoomer-SQ NextSeq 550 Axio Zoom.V16 Digital Spatial Profiler timsTOF FleX timsTOF FleX MALDI-2 NanoZoomer S210 BZ-X810 Axio Observer 7 Cytek Northern Lights iScan System Opera Phenix HCS Zeiss LightSheet Z.1 IN Cell Analyzer 2200 timsTOF HT PhenoImager Fusion DM6 B Phenocycler-Fusion 2.0 Aperio CS2 Orbitrap Fusion Lumos Tribrid Resolve Biosciences Molecular Cartography MALDI timsTOF Flex Prototype TissueScope LE Slide Scanner VS200 Slide Scanner Axio Observer 5 NanoZoomer 2.0-RS Axio Observer 3 HiSeq 2500 Orbitrap Eclipse Tribrid Cell DIVE MERSCOPE NextSeq 2000 NovaSeq 6000 In-House HiSeq 4000 Q Exactive HF-X solariX Panoramic 150 Digital Scanner Aperio AT2 MIBIscope SYNAPT G2-Si MACSima System Biomark HD NanoZoomer S60 CosMx Spatial Molecular Imager MERSCOPE Ultra Axio Scan.Z1 Juno System Q Exactive HF Xenium Analyzer
Source storage duration value * The length of time the sample was stored prior to processing it. For assays performed on tissue sections, this refers to how long the tissue section (e.g., slide) was stored before the assay began (e.g., imaging). For assays performed on suspensions, such as sequencing, it refers to how long the suspension was stored before library construction started. Example: 12  
Source storage duration unit * The unit of measurement used to specify the source storage duration value. Example: hour hour month year day minute
Time since acquisition instrument calibration value The length of time since the acquisition instrument was last serviced or calibrated. This provides a metric for assessing drift in data capture. Example: 10  
Time since acquisition instrument calibration unit The unit of measurement used to specify the time since acquisition instrument calibration value. Example: month month year day
Contributors path * The name of the file containing the ORCID IDs for all contributors to this dataset. Example: ./contributors.csv  
Data path * The top-level directory containing the raw and/or processed data. For a single dataset upload, this might be represented as “.”, whereas for a data upload containing multiple datasets, this would be the directory name for the respective dataset. For example, if the data is within a directory named “TEST001-RK”, use the syntax “./TEST001-RK” for this field. If there are multiple directory levels, use the format “./TEST001-RK/Run1/Pass2”, where “Pass2” is the subdirectory where the single dataset’s data is stored. This is an internal metadata field used solely for data ingestion. Example: ./TEST001-RK  
Barcode offset * The position within a sequencing read where a barcode sequence begins. In sequencing libraries, barcodes—short DNA sequences used to tag individual cells or molecules—are often arranged in a pattern where each barcode is separated by a constant sequence of known length. These constant sequences create predictable spacing between barcodes, known as offsets. For example, if barcodes are 8 base pairs (bp) long and separated by 30 bp constant regions, the barcodes might start at positions 0, 38, and 76 within the read. If the source material is not barcoded, this field should be marked as “Not applicable”. Example: 0,38,76 1 8 20 0,14 1,27 0,38,76 10,48,78 10,48,86 0,20-23,51-54 Not applicable
Barcode read * The sequencing read file contains the cell or capture spot barcode sequences. This information is essential when constructing sequencing libraries with a non-commercial kit, as it ensures that barcodes are correctly extracted during data processing. If the source material is not barcoded, this field should be marked as “Not applicable”. Example: Read 1 (R1) Read 1 (R1) Not applicable Read 2 (R2)
Barcode size * The length of each cell or capture spot barcode in base pairs (bp). If the source material is not barcoded, enter “Not applicable”. Example: 16 14 16 40 8,6 10,10 8,8,8 14-17,14,14 Not applicable
UMI offset * The position within a sequencing read where the Unique Molecular Identifier (UMI) sequence begins. UMIs are short, random nucleotide sequences added to DNA or RNA fragments during library preparation to uniquely tag each original molecule. If no UMI is used, enter “Not applicable”. Example: 16 16 36 Not applicable
UMI read * Specifies which sequencing read file contains the Unique Molecular Identifier (UMI) barcode. If no UMI is used, enter “Not applicable”. Example: Read 1 (R1) Read 1 (R1) Not applicable Read 2 (R2)
UMI size * The length of the Unique Molecular Identifier (UMI) in base pairs (bp). If no UMI is used, enter “Not applicable”. Example: 8 8 9 10 12 Not applicable
Assay input entity * The entity from which the analyte is captured. For example, in bulk sequencing, the source entity would be “tissue” while in single-cell sequencing, it would be “single cell”. Example: single cell single cell tissue (bulk) spot single nucleus area of interest
Number of input cells or nuclei The number of cells or nuclei that were input into the assay. This information is typically not available for assays performed on bulk tissue preparations, and therefore this field may be left blank. Example: 1000  
Library adapter sequence * The 5’ and/or 3’ adapter sequences used during library preparation to make the library compatible with the sequencing protocol and instrumentation. Adapter sequences should be provided as a comma-separated list of key-value pairs in the format: “adapter name: sequence”. Example: Nextera Read 1: CTGTCTCTTATACACATCT  
Library average fragment size * The average size of sequencing library fragments, measured in base pairs (bp), as estimated by gel electrophoresis, BioAnalyzer, or TapeStation. Example: 200  
Library input amount value The amount of cDNA, after amplification, that was used for library construction. Example: 60  
Library input amount unit The unit of measurement used to specify the library input amount value. Example: ng ul ng
Library output amount value The total amount of library following the clean-up step after the final PCR amplification. To calculate, multiply the Qubit-measured concentration (ng/µl) by the elution volume (µl) obtained after the final clean-up. Example: 125  
Library output amount unit The unit of measurement used to specify the library output amount value. Example: ng ul ng
Library concentration value * The concentration of the pooled library submitted for sequencing. Example: 2.5  
Library concentration unit * Unit of library concentration value. ng/ul pg/ul nM
Library layout * Indicates whether the sequencing library was prepared for single-end or paired-end sequencing. This information determines how reads are generated and aligned during data processing. Example: single-end single-end paired-end
Number of PCR cycles for indexing * The number of PCR cycles used specifically to add sequencing adapters and amplify the library during the indexing step. This count does not include earlier cDNA amplification cycles, which are recorded separately under the “Number of iterations of cDNA amplification” field. Example: 8  
Library preparation kit * The reagent kit used to construct the sequencing library, including both the kit name and, if available, version and product number. If the library was prepared using a custom protocol, enter “Custom”. Example: 10X Genomics; Chromium Next GEM Single Cell 5’ Kit v2, 16 rxns; PN 1000263 10X Genomics; Chromium Next GEM Single Cell 3' HT Kit v3.1, 8 rxns; PN 1000370 10X Genomics; Chromium Next GEM Automated Single Cell 5' Kit v2, 4 rxns; PN 1000298 10x Genomics; Chromium Next GEM Single Cell Fixed RNA Sample Preparation Kit, 16 rxns; PN 1000414 10X Genomics; Visium Spatial Gene Expression Slide and Reagent Kit, 4 slides, 16 reactions; PN 1000184 Parse Biosciences; Evercode WT v2 Kit, 48 rxns; PN ECW02030 10X Genomics; Chromium Next GEM Single Cell 5' Kit v2, 16 rxns; PN 1000263 10X Genomics; Visium Spatial for FFPE Gene Expression Kit, Human Transcriptome, 1 slides, 4 reactions; PN 1000338 10X Genomics; Chromium Next GEM Single Cell 3' Kit v3.1, 16 rxns; PN 1000268 10x Genomics; Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit, 16 rxns; PN 1000175 10x Genomics; Library Construction Kit C, 16 rxns; PN 1000694 10x Genomics; Visium CytAssist Spatial Gene Expression for FFPE, Mouse Transcriptome, 6.5mm, 4 rxns; PN 1000521 10X Genomics; Chromium Next GEM Single Cell 3' HT Kit v3.1, 48 rxns; PN 1000348 10X Genomics; Chromium Next GEM Single Cell 3' GEM, Library & Gel Bead Kit v3.1, 16 rxns; PN 1000121 10x Genomics; Library Construction Kit C, 4 rxns; PN 1000689 10X Genomics; Visium CytAssist Spatial Gene Expression for FFPE, Human Transcriptome, 11 mm, 2 reactions; PN 1000522 Illumina; TruSeq Stranded mRNA Library Prep (48 samples); PN 20020594 10X Genomics; Automated Library Construction Kit, 24 rxns; PN 1000428 New England BioLabs; NEBNext Ultra II RNA Library Prep Kit for Illumina; PN E7770 10X Genomics; Chromium Next GEM Automated Single Cell 5' Kit v2, 24 rxns; PN 1000290 10X Genomics; Visium Spatial Gene Expression Slide and Reagent Kit, 1 slides, 4 reactions; PN 1000187 10x Genomics; Chromium GEM-X Single Cell 5' Kit v3, 16 rxns; PN 1000699 10x Genomics; Chromium Single Cell ATAC Library & Gel Bead Kit, 16 rxns; PN 1000110 10x Genomics; Library Construction Kit, 16 rxns; PN 1000190 Parse Biosciences; Evercode WT Mini v2 Kit, 12 rxns; PN ECW02010 10x Genomics; Chromium Next GEM Single Cell 5' HT Kit v2, 48 rxns; PN 1000356 Illumina; TruSeq Stranded mRNA Library Prep (96 samples); PN 20020595 10X Genomics; Chromium Next GEM Single Cell 5' Kit v2, 4 rxns; PN 1000265 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 4 rxn; PN 1000285 10X Genomics; Chromium Next GEM Single Cell Fixed RNA Hybridization & Library Kit, 4 rxns; PN 1000415 Custom 10X Genomics; Chromium Single Cell 3' GEM, Library & Gel Bead Kit v3, 4 rxns; PN 1000092 10x Genomics; Chromium Single Cell 3ʹ GEM, Library & Gel Bead Kit v3, 16 rxns; PN 1000075 10X Genomics; Chromium Single Cell 3' Library & Gel Bead Kit, 4 rxns; PN 120267 10X Genomics; Visium Spatial for FFPE Gene Expression Kit, Mouse Transcriptome, 4 rxns; PN 1000339 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 16 rxn; PN 1000283 Epigenome Technologies; Multiplex Droplet Paired-Tag 16 Reaction Kit; PN MDP16101 10X Genomics; Chromium Next GEM Single Cell 3' Kit v3.1, 4 rxns; PN 1000269 10x Genomics; GEM-X Flex GEM & Library Kit, 4 rxns; PN 1000782 10x Genomics; Chromium Next GEM Single Cell 5' HT Kit v2, 8 rxns; PN 1000374 10X Genomics; Visium CytAssist Spatial Gene Expression for FFPE, Human Transcriptome, 6.5mm, 4 reactions; PN 1000520
Sample indexing kit * The reagent kit used to add index sequences to samples. Indexes enable multiplexing, allowing multiple libraries to be pooled and sequenced together on the same Illumina flow cell. Each indexing kit includes a set of compatible index sequences (often called “indexing sets”) used to uniquely label a specific number of samples, depending on the kit’s design. If the library was prepared using a custom protocol, enter “Custom”. If no indexing was performed, enter “Not applicable”. Example: 10X Genomics; Dual Index Kit TS, Set A; PN 1000251 10x Genomics; Chromium i7 Multiplex Kit, 96 rxns; PN 120262 Illumina; TruSeq RNA CD Index Plate (96 Indexes, 96 Samples); PN 20019792 Not applicable Illumina; TruSeq RNA Single Indexes Set A (12 Indexes, 48 Samples); PN 20020492 Illumina; TruSeq RNA Single Indexes Set B (12 Indexes, 48 Samples); PN 20020493 10X Genomics; Chromium i7 Sample Index Plate (96 rxn); PN 220103 NanoString Technologies; GeoMx Seq Code Pack; PN GMX-NGS-SEQ-AB NanoString Technologies; GeoMx Seq Code Pack; PN GMX-NGS-SEQ-CD NanoString Technologies; GeoMx Seq Code Pack; PN GMX-NGS-SEQ-EF NanoString Technologies; GeoMx Seq Code Pack; PN GMX-NGS-SEQ-GH Custom 10x Genomics; Chromium i7 Multiplex Kit N, Set A, 96 rxn; PN 1000084 Parse Biosciences; Fragmentation Reagents; PN WX100 Integrated DNA Technologies: Custom DNA Oligos Parse Biosciences; UDI Plate - WT; PN UDI1001 10X Genomics; Dual Index Kit TS, Set A; PN 1000251 Illumina; IDT for Illumina - TruSeq RNA UD Indexes v2 (96 Indexes, 96 Samples); PN 20040871 10X Genomics; Single Index Kit N, Set A (96 rxn); PN 1000212 10X Genomics; Dual Index Kit TT, Set A (96 rxn); PN 1000215
Sample indexing set * The specific sequencing barcode index set used, chosen from the sample indexing kit. For example, in the case of 10x, this might be “SI-GA-A1”, whereas for Nextera, it could be “N505 - CTCCTTAC”. Example: SI-GA-A1  
Is technical replicate? * Indicates whether the sequencing reaction was run in replicate. If “Yes,” the corresponding FASTQ files in the dataset should be merged for analysis. Example: Yes Yes No
Expected entity capture count The number of cells, nuclei, or capture spots expected to be captured by the assay. Example: 14336  
Sequencing reagent kit * The reagent kit used to perform the sequencing run, including the platform, kit name, version, and product number when available. If the reagent kit was prepared using a custom protocol, enter “Custom”. Example: Illumina; NextSeq 500/550 Hi Output Kit 150 Cycles; v2.5; PN 20024907 Illumina; NovaSeq X Series 25B Reagent Kit (100 Cycle); PN 20125967 Illumina; NextSeq 1000/2000 P2 XLEAP-SBS Reagent Kit (100 Cycles); PN 20100987 Illumina; NovaSeq X Series 1.5B Reagent Kit (300 Cycle); PN 20104705 Illumina; NovaSeq 6000 S1 Reagent v1.5 Kit (200 Cycles); PN 20028318 Illumina; NextSeq 2000 P3 Reagent Kit (300 Cycles); PN 20040561 Illumina; NextSeq 1000/2000 P2 Reagent v3 Kit (200 Cycles); PN 20046812 Illumina; NovaSeq X Series 25B Reagent Kit (200 Cycle); PN 20125968 Illumina; NovaSeq X Series 1.5B Reagent Kit (200 Cycle); PN 20104704 Illumina; NovaSeq X Series 10B Reagent Kit (300 Cycle); PN 20085594 Element Biosciences; AVITI 2x75 Sequencing Kit Cloudbreak FS High Output (184 Cycles); PN 860-00015 Illumina; NovaSeq X Series 1.5B Reagent Kit (100 Cycle); PN 20104703 Illumina; NovaSeq X Series 25B Reagent Kit (300 Cycle); PN 20104706 Illumina; NovaSeq 6000 S1 Reagent v1.5 Kit (100 Cycles); PN 20028319 Illumina; NovaSeq 6000 SP Reagent v1.5 Kit (100 Cycles); PN 20028401 Illumina; HiSeq 3000/4000 PE Cluster Kit PE-410-1001; PN 1000283 Illumina; NextSeq 1000/2000 P2 Reagent v3 Kit (300 Cycles); PN 20046813 Illumina; NextSeq 2000 P3 Reagents Kit (100 Cycles); PN 20040559 Illumina; NovaSeq 6000 S1 Reagent v1.5 Kit (300 Cycles); PN 20028317 Custom Illumina; NextSeq 1000/2000 P2 Reagent v3 Kit (100 Cycles); PN 20046811 Illumina; NovaSeq X Series 10B Reagent Kit (200 Cycle); PN 20085595 Illumina; NextSeq 500/550 Hi Output Kit 150 Cycles; v2.5; PN 20024907 Illumina; NextSeq 500/550 Mid Output Kit 150 Cycles v2.5; PN 20024904 Illumina; NovaSeq 6000 S1 Reagent Kit (200 Cycles); PN 20012864 Illumina; NovaSeq 6000 SP Reagent v1.5 Kit (300 Cycles); PN 20028400 Illumina; NovaSeq 6000 S4 Reagent v1.5 Kit (200 Cycles); PN 20028313 Illumina; NextSeq 500/550 Hi Output Kit 75 Cycles v2.5; PN 20024906 Illumina; NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles); PN 20028312 Illumina; NovaSeq X Series 10B Reagent Kit (100 Cycle); PN 20085596 Illumina; NovaSeq 6000 S2 Reagent v1.5 Kit (100 Cycles); PN 20028316
Sequencing read format * The number of sequencing cycles for each read and index in the sequencing run, typically reported as a comma-separated list in the order: Read 1, i7 index, i5 index, and Read 2. Example: 50,8,16,50  
Transposition reagent kit * The reagent kit used for the transposition step, particularly if Tn5 transposase was sourced from a commercial kit, including the platform, kit name, version, and product number when available. If the reagent kit was prepared using a custom protocol, enter “Custom”. if no transposition step, enter “Not applicable”. Example: 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 16 rxn; PN 1000283 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 16 rxn; PN 1000283 Not applicable 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 4 rxn; PN 1000285 10x Genomics; Chromium Next GEM Single Cell ATAC Library & Gel Bead Kit, 16 rxns; PN 1000175 Custom 10x Genomics; Chromium Single Cell ATAC Library & Gel Bead Kit, 16 rxns; PN 1000110 Illumina; Illumina Tagment DNA TDE1 Enzyme and Buffer Small Kit; PN 20034197
Transposition method * The method or approach used to capture accessible chromatin regions, typically reflecting the type of kit or protocol applied. Example: bulkATACseq bulkATACseq sciATACseq scATACseq Custom
Sequencing batch ID A lab-assigned identifier for the sequencing run, such as a chip ID, that enables users to determine which samples were processed together. To avoid ID conflicts across institutions, it is recommended to prefix the ID with the name of the sequencing center. Example: Broad_Chip1234  
Capture batch ID A lab-assigned identifier used to indicate which cells or nuclei were captured together in the same run. For example, in a 10X Genomics Chromium Controller workflow, this could be the chip ID used to trace datasets derived from a single capture event. This ID helps users identify samples processed together on the same device. To avoid conflicts across institutions, it is recommended to prefix the ID with the name of the sequencing center. Example: Broad_Batch1234  
Preparation instrument vendor The company that manufactures the instrument used to prepare the sample (e.g., for staining or other processing steps) prior to the assay. If the instrument was custom-built or developed internally, enter “In-House”. If no sample preparation occurred, enter “Not applicable”. Example: 10X Genomics In-House Leica Biosystems Not applicable Thermo Fisher Scientific Roche Diagnostics HTX Technologies 10x Genomics Hamamatsu Ionpath Akoya Biosciences SunChrom
Preparation instrument model The specific model of the instrument used for sample preparation, such as staining. Manufacturers may offer multiple models with varying features or sensitivities, which can influence how the sample is processed and how the resulting data is interpreted. If no sample preparation occurred, enter “Not applicable”. Example: Chromium X NanoZoomer S210 Not applicable Sublimator EVOS M7000 Chromium Controller Custom NanoZoomer S360 NanoZoomer S60 Chromium X AutoStainer XL Visium CytAssist SunCollect Sprayer M3+ Sprayer Discovery Ultra ST5020 Multistainer Chromium iX Chromium Connect M5 Sprayer TM-Sprayer
Preparation instrument kit The reagent kit used in conjunction with the preparation instrument for sample processing (e.g., staining or labeling). If the reagent kit was prepared using a custom protocol, enter “Custom”. Example: 10X Genomics; Chromium Next GEM Chip G Single Cell Kit, 48 rxns; PN 1000120 10X Genomics; Chromium Next GEM Chip K Automated Single Cell Kit, 48 rxns; PN 1000289 10x Genomics; Chromium Single Cell B Chip Kit, 16 rxns; PN 1000074 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 4 rxn; PN 1000285 10x Genomics; Visium HD, Mouse Transcriptome, 6.5 mm, 4 runs; PN 1000676 10X Genomics; Chromium Next GEM Chip K Single Cell Kit, 48 rxns; PN 1000286 10x Genomics; Chromium Chip E Single Cell ATAC Kit, 48 rxns; PN 1000155 10X Genomics; Chromium Next GEM Chip G Single Cell Kit, 48 rxns; PN 1000120 Custom 10x Genomics; Visium HD, Human Transcriptome, 6.5 mm, 4 runs; PN 1000675 10X Genomics; Chromium Next GEM Chip K Single Cell Kit, 16 rxns; PN 1000287 10x Genomics; Chromium Next GEM Chip J Single Cell Kit, 16 rxns; PN 1000230 10X Genomics; Chromium Next GEM Chip G Single Cell Kit, 16 rxns; PN 1000127 10X Genomics; Chromium NextGem Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 16 rxn; PN 1000283 10x Genomics; Visium HD, Human Transcriptome, 11 mm, 4 runs; PN 1001037 10x Genomics; Chromium GEM-X Single Cell 3' Kit v4, 16 rxns; PN 1000691 10X Genomics; Visium FFPE Reagent Kit v2-Small, PN 1000436 10X Genomics; Chromium Next GEM Chip Q Single Cell Kit, 16 rxns; PN 1000422 10x Genomics; Chromium Next GEM Chip J Single Cell Kit, 48 rxns; PN 1000234 10x Genomics; GEM-X Flex Gene Expression Chip Kit, 4 chips; PN 1000791 10x Genomics; Visium HD, Mouse Transcriptome, 11 mm, 4 runs; PN 1001038
Metadata schema ID * The unique string identifier for the metadata specification version, which is easily interpretable by computers for purposes of data validation and processing. Example: 22bc762a-5020-419d-b170-24253ed9e8d9  
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HuBMAP Datasets

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Citation

Fisher SA, Hardi J, Morgan R, Nordgren E, Kant PM, Honick B, Rosario J, O'Connor MJ, Turner ML, DCWG Members, Gehlenborg N, Blood PD, Silverstein JC, Musen MA. 2026. The HuBMAP Framework for Advancing Data FAIRness. submitted. https://doi.org/10.64898/2026.06.01.728946


Reuse

This standard may be reused, expanded, or referenced by external repositories. It represents version 3.0 of the metadata schema and version 2.2 of the file directory structure. If you are uploading data, the latest versions of both are available at the "Latest Versions" link below. If you are querying or using HuBMAP or SenNet metadata, a harmonized view of the metadata specification is available at the "Harmonized Version" link below.


Latest Versions    Harmonized Version

Contributors

Below is the information for the individuals who contributed to the HuBMAP and SenNet metadata reporting standards.

For questions about this standard, email HuBMAP Helpdesk. You can alternatively reach out to the individuals listed below, either via the email address listed in the table or via contact information provided on their ORCID profile page.

Name Affiliation Contact ORCID
Stephen A Fisher University of Pennsylvania, Philadelphia PA, USA safisher@upenn.edu 0000-0001-8034-7685
Josef Hardi Stanford University, Stanford, CA, USA johardi@stanford.edu 0000-0002-2533-6681
Richard Morgan University of Pittsburgh, Pittsburgh, PA, USA rsm66@pitt.edu 0009-0003-1800-8545
Mark A Musen Stanford University, Stanford, CA, USA musen@stanford.edu 0000-0003-3325-793X
Jonathan C Silverstein University of Pittsburgh, Pittsburgh, PA, USA j.c.s@pitt.edu 0000-0002-9252-6039
Erik Nordgren University of Pennsylvania, Philadelphia PA, USA   0000-0002-5024-0278
Peter M Kant University of Pittsburgh, Pittsburgh, PA, USA; Currently - Otsuka Precision Health, Princeton, NJ, USA   0009-0002-6510-5041
Brendan John Honick Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, USA   0000-0001-6128-9854
Martin J O’Connor Stanford University, Stanford, CA, USA   0000-0002-2256-2421
Jean G Rosario University of Pennsylvania, Philadelphia PA, USA   0000-0002-6116-5058
Nils Gehlenborg Harvard Medical School, Boston, MA, USA   0000-0003-0327-8297
Philip D Blood Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, USA   0000-0002-9129-1223
Kyung Jin Ahn Children’s Hospital of Philadelphia, Philadelphia, PA, USA   0000-0002-4184-482X
Christopher R Anderton Pacific Northwest National Laboratory, Richland, WA, USA   0000-0002-6170-1033
Shovik Bandyopadhyay Children’s Hospital of Philadelphia, Philadelphia, PA, USA; Brigham and Women’s Hospital, Boston, MA, USA   0000-0003-3919-3914
Kenneth C Bedi University of Pennsylvania, Philadelphia PA, USA   0000-0003-3588-9324
Maigan Brusko University of Florida, Gainesville, FL, USA   0000-0002-4331-2202
Martha Campbell-Thompson University of Florida, Gainesville, FL, USA   0000-0001-6878-1235
James Carson The University of Texas at Austin, Austin, TX, USA   0000-0001-9009-5645
Chase M Carver Mayo Clinic, Rochester, MN, USA   0000-0003-4002-2418
Jing Chen University of Florida, Gainesville, FL, USA   0000-0001-8008-8062
Anthony M Corbett University of Rochester Medical Center, Rochester, NY, USA   0000-0001-9545-0853
Alexandra E Cuaycal University of Florida, Gainesville, FL, USA   0000-0002-9060-6326
Penny Cuda Carnegie Mellon University, Pittsburgh, PA, USA   0009-0002-6547-2650
Dinh Diep University of California, San Diego, CA, USA; Currently - Altos Labs, San Diego, CA, USA   0000-0001-6057-4119
Sergii Domanskyi The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA   0000-0002-6847-6019
Sean Donahue Carnegie Mellon University, Pittsburgh, PA, USA   0000-0002-4072-2046
Michael P Duffy University of Pennsylvania, Philadelphia PA, USA   0000-0001-5325-3683
Michael T Eadon Indiana University School of Medicine, Indianapolis, IN, USA   0000-0003-3066-2876
Jean Fan Johns Hopkins University, Baltimore, MD, USA   0000-0002-0212-5451
Melissa A Farrow Vanderbilt University, Nashville, TN, USA   0000-0002-1602-2082
Kathleen M Fisch University of California San Diego, La Jolla, CA, USA   0000-0002-0117-7444
William F Flynn The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA   0000-0001-6533-0340
James M Fulcher Pacific Northwest National Laboratory, Richland, WA, USA   0000-0001-9033-3623
Soumya Ghose GE HealthCare, Niskayuna, NY, USA   0000-0002-2730-1482
Fiona Ginty GE HealthCare, Niskayuna, NY, USA   0000-0001-6638-683X
Joana P Gonçalves Delft University of Technology, Delft, The Netherlands   0000-0001-6072-9627
Yongqun He University of Michigan, Ann Arbor, MI, USA   0000-0001-9189-9661
Po Hu Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA   0000-0003-2422-1652
Sanjay Jain Washington University School of Medicine, St. Louis, MO, USA   0000-0003-2804-127X
Thomas V Karathanos Stanford University, Stanford, CA, USA   0000-0003-1754-3872
Madhurima Kaushal Washington University School of Medicine   0000-0003-2760-0586
Angela RS Kruse Vanderbilt University, Nashville, TN, USA; Currently - The Ohio State University, Columbus, OH, USA   0000-0001-8776-2769
Yumi Kwon Pacific Northwest National Laboratory, Richland, WA, USA   0000-0003-0523-6197
Blue B Lake University of California San Diego, La Jolla, CA, USA; Currently - Altos Labs, San Diego, CA, USA   0000-0002-8637-9044
Roy Lardenoije Delft University of Technology, Delft, The Netherlands   0000-0002-9026-7870
Shin Lin Emory University, Atlanta, GA, USA   0000-0003-0118-0413
Yiing Lin Washington University, St. Louis, MO, USA   0000-0002-0317-7608
Scott A Lindsay University of California, San Diego, CA, USA   0000-0002-2929-7755
Peiran Lu Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA   0009-0001-5096-3046
Clayton Mathews University of Florida, Gainesville, FL, USA   0000-0002-8817-6355
Elizabeth McDonough GE HealthCare, Niskayuna, NY, USA   0000-0001-7524-8260
Ricardo Melo Ferreira Indiana University School of Medicine, Indianapolis, IN, USA   0000-0003-2063-9744
Emma M Monte Stanford University, Stanford, CA, USA   0000-0003-2566-1967
Kathleen O’Neill University of Pennsylvania, Philadelphia PA, USA   0000-0003-1980-6840
Minxing Pang University of Pennsylvania, Philadelphia PA, USA   0000-0001-5208-5972
Mana Parast University of California San Diego, La Jolla, CA, USA   0000-0001-5963-2246
Liming Pei Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA   0000-0002-1924-0333
Samuel Peters University of Minnesota, Minneapolis, MN, USA   0000-0003-1479-8087
Ajay Pillai National Institute of Health, Bethesda, MD, USA   0000-0002-9789-7189
Gloria Pryhuber University of Rochester Medical Center, Rochester, NY, USA   0000-0002-9185-3994
Ling Qin University of Pennsylvania, Philadelphia PA, USA   0000-0002-2582-0078
Presha Rajbhandari Columbia University, NYC, NY, USA   0000-0003-2184-7238
Matthew M Ruffalo Carnegie Mellon University, Pittsburgh, PA, USA   0000-0003-2222-6169
Pinaki Sarder University of Florida, Gainesville, FL, USA   0000-0003-2450-5233
Diane C Saunders Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA; Northwestern University, Chicago, IL, USA   0000-0002-8849-6746
Kevin Schneider Buck Institute, Novato, CA, USA   0009-0001-8046-0167
Lingyan Shi University of California San Diego, La Jolla, CA, USA   0000-0003-1373-3206
Santhosh Sivajothi The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA   0000-0002-8854-4517
David Smith Children’s Hospital of Philadelphia, Philadelphia, PA, USA   0000-0001-7858-3785
Jeff M Spraggins Vanderbilt University, Nashville, TN, USA   0000-0001-9198-5498
Valentina Stanley University of California, San Diego, CA, USA   0000-0002-2212-7796
Kai Tan Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA   0000-0002-9104-5567
Anusha Thadi Children’s Hospital of Philadelphia, Philadelphia, PA, USA   0000-0002-1271-0398
Hua Tian Columbia University Medical Center, NYC, NY, USA   0000-0002-3598-0219
Morgan L Turner Harvard Medical School, Boston, MA, USA   0000-0002-1512-9742
Ioannis S Vlachos Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Cancer Center, Boston, MA, USA; Broad Institute of MIT and Harvard, Boston, MA, USA   0000-0002-8849-808X
Seth Winfree University of Nebraska Medical Center, Omaha, NE, USA; Currently - QCDx Inc, Farmington, CT, USA    
Pei-Hsun Wu Johns Hopkins University, Baltimore, MD, USA   0000-0002-7371-2960
Kevin J Zemaitis Pacific Northwest National Laboratory, Richland, WA, USA   0000-0002-3524-9776
Mowei Zhou Pacific Northwest National Laboratory, Richland, WA, USA; Currently - Zhejiang University, Hangzhou, Zhejiang 310058, China   0000-0003-3575-3224
Chenchen Zhu Department of Genetics, Stanford University, Stanford, CA, USA   0000-0003-2165-9456