Template Builder - SDRF-Proteomics

1 What type of proteomics experiment?

Choose the technology platform used in your study.

Why does this matter? ▾

MS-based and affinity-based proteomics have fundamentally different data types. MS experiments produce spectra files and need columns for instrument settings, fragmentation, and search parameters. Affinity experiments (Olink, SomaScan) produce protein-level measurements and need panel/assay identifiers. They cannot be combined in a single SDRF.

Read the full specification →

2 What organism are you studying?

Select the organism group that best matches your samples.

Why does this matter? ▾

Different organisms require different metadata. Human samples need ancestry and age information. Plant samples need developmental stage and growth conditions. Model organisms need strain identifiers.

See sample metadata guidelines →

3 Any specialized experiment type?

If your experiment uses a specialized technique, select it here. Otherwise skip this step.

Why does this matter? ▾

Specialized experiments need extra metadata for analysis tools. DIA experiments need isolation window parameters. Crosslinking-MS needs crosslinker chemistry details. Single-cell proteomics needs cell-level annotations.

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3 Which affinity platform?

Select the specific affinity proteomics platform used.

Why does this matter? ▾

Each affinity platform has its own panel identifiers and normalization metadata. Olink uses NPX values and panel identifiers, while SomaScan uses SOMAmer reagent identifiers and dilution information.

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4 Review & customize your columns

Here is your SDRF template based on your selections. Add or remove optional columns as needed.

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Sample metadata Data / instrument Experimental factors

Add or remove columns

5 Select your factor values

Which sample characteristics are the experimental variables you are comparing? Factor values tell analysis tools what differs between your groups (e.g. disease, treatment, genotype).

Why does this matter? ▾

Factor values define what you are comparing in your experiment. If you are comparing healthy vs. disease samples, disease is your factor value. Analysis tools use these to set up the correct statistical contrasts. Each factor value column mirrors a characteristics column.