Unannotated Feature Characterization

Unannotated Feature Characterization

Summary

Quantify and prioritize unannotated metabolomic features within natural extracts by computing specificity-based ratios and filtering annotations against curated thresholds. This skill identifies novel chemical signals that lack structural database matches, enabling prioritization of samples with high novelty potential.

When to use

Apply this skill when you have aligned feature tables from LC–MS/MS, corresponding in silico annotations (from GNPS/ISDB or SIRIUS), and metadata describing sample origin. Use it to rank extracts by the proportion of sample-specific, unannotated features—a proxy for structural novelty. Typical triggers: (1) screening natural product libraries for bioactive leads; (2) identifying extracts enriched in rare metabolites; (3) computing a Feature Component score as part of a composite novelty index (Inventa Priority Score).

When NOT to use

• Input is already a pre-computed novelty or FC score — skip re-computation to avoid redundancy.
• Annotation results are unavailable or highly incomplete (>70% unaligned features) — FC becomes uninformative as it collapses to near-universal high values.
• Extract set contains <3 samples — feature specificity thresholds (e.g., 90%) become unrepresentative; consider lowering min_specificity or using absolute occurrence counts instead.

Inputs

• Quantitative feature table (MZmine2 or MZmine3 format: peak area, row m/z, row retention time columns)
• Metadata table (GNPS format with ATTRIBUTE_Species, ATTRIBUTE_Organe, sample identifiers)
• Annotation results (timaR ISDB output and/or SIRIUS compound_identification.tsv)
• Ion Identity grouping results (optional, to collapse redundant features)

Outputs

• Per-sample Feature Component (FC) ratio (0–1 scale)
• Feature annotation status table (annotated vs. unannotated, per-feature specificity scores)
• Feature-level breakdown (m/z, retention time, count in samples, specificity, annotation status)
• Ranked sample list by FC and optional Priority Score (if LC, CC, SC components included)

How to apply

Load quantitative feature tables (peak area, m/z, retention time from MZmine2/3) and filter annotations by quality thresholds: ppm_error ≤5 ppm (ISDB), cosine score ≥0.7, shared_peaks ≥10, min_score_final ≥0.0 (ISDB); min_ZodiacScore ≥0.9, min_ConfidenceScore ≥0.0 (SIRIUS). For each feature, compute specificity as the proportion of samples in the extract set where it is present; retain only features exceeding min_specificity (e.g., 90%). Mark features as annotated or unannotated based on filtered annotation results, optionally applying Ion Identity grouping to collapse redundant adducts/isotopologues. For each sample, calculate the Feature Component (FC) as: (count of specific, unannotated features) / (total features in sample). Output per-sample FC ratio (range 0–1), component breakdown, and ranked sample list. Rationale: high FC indicates extracts enriched in sample-unique metabolites lacking database coverage, suggesting higher chemical novelty potential.

Related tools

• MZmine2/MZmine3 (Feature detection, alignment, and quantification; exports peak area, m/z, and retention time tables required as input)
• timaR (Taxonomically informed annotation weighting; filters ISDB annotations by ppm_error, shared_peaks, cosine score, and final score thresholds) — https://taxonomicallyinformedannotation.github.io/tima-r/index.html
• SIRIUS (In silico metabolite annotation; generates compound_identification.tsv with ZodiacScore and ConfidenceScore for filtering) — https://bio.informatik.uni-jena.de/software/sirius/
• Ion Identity (Optional feature grouping to collapse redundant adducts and isotopologues, reducing total feature count before specificity computation) — https://github.com/luigiquiros/inventa
• Inventa (Complete workflow implementation: loads filtered annotations, computes per-feature specificity, calculates FC per sample, and optionally combines with LC, CC, SC into Priority Score) — https://github.com/luigiquiros/inventa

Examples

From the inventa README/workflow: Set min_specificity=90, ppm_error=5, shared_peaks=10, cosine=0.7, min_ZodiacScore=0.9, then run the Inventa Jupyter notebook after loading MZmine quantitative table, metadata (GNPS format), and ISDB/SIRIUS annotation files. Output includes per-sample FC values and ranked extract list by novelty potential.

Evaluation signals

• FC values for all samples fall in the valid range [0, 1] and sum of component counts matches total feature count per sample.
• Features marked 'unannotated' have zero matches in filtered ISDB/SIRIUS results; annotated features have at least one match passing all cutoffs (ppm_error, shared_peaks, cosine, ZodiacScore, ConfidenceScore).
• Specificity scores match expected proportions: a feature present in N out of M samples has specificity = 100×N/M; features below min_specificity threshold are excluded from FC computation.
• FC rank correlates with expected novelty: samples with highest FC contain rare, extract-specific, unannotated metabolites; low-FC samples are dominated by shared, annotated features.
• Reproducibility check: rerunning the workflow with identical parameters and annotation cutoffs produces identical FC values and sample rankings.

Limitations

• FC is sensitive to annotation quality and threshold choice (ppm_error, cosine, min_score_final, ZodiacScore, min_ConfidenceScore). Overly strict thresholds inflate unannotated counts and FC; lenient thresholds reduce both. Practitioners must validate thresholds against their instrument, library, and chemical context.
• Feature specificity (min_specificity = 90%) may be unachievable in very small extract sets (<3–5 samples) or in highly diverse libraries; consider absolute feature occurrence counts or lower thresholds in such cases.
• FC does not account for the biological/chemical plausibility of unannotated features (e.g., noise, adducts, fragments). Ion Identity grouping partially mitigates this but may not eliminate all redundancy.
• Sample-level confounders (extraction method, sample mass, storage, instrument settings) can inflate or deflate feature counts independently of chemical diversity, biasing FC rankings.
• High FC does not guarantee novel chemistry: unannotated features may represent known metabolites absent from the annotation library rather than truly novel compounds. Orthogonal validation (NMR, isolation, bioassay) is required.

Evidence

• [other] The Feature Component is computed as the ratio of the number of specific non-annotated features to the total number of features for each extract.: "The Feature Component (FC) is a ratio of the number of specific non-annotated features over the total number of features of each extract."
• [other] Load quantitative feature table from MZmine2 or MZmine3 with peak area, m/z, and retention time columns.: "MZmine output format using only the 'Peak area', 'row m/z' and 'row retention time' columns. -Inventa takes input directly from MZmine2 or MZmine 3"
• [other] Filter ISDB annotations by ppm_error (5 ppm), shared_peaks (10), cosine score (0.7), and min_score_final threshold; filter SIRIUS by min_ZodiacScore (0.9) and min_ConfidenceScore (0.0).: "For cleaning-up annotations from GNPS ppm_error = 5 # min error in ppm to consider an annotation valable shared_peaks = 10 # min number of shared peaks"
• [other] Compute feature specificity as the proportion of samples where a feature is present; retain features exceeding min_specificity threshold (90%).: "Feature_component min_specificity = 90 # minimun feature specificity to consider"
• [other] Mark features as annotated or unannotated based on filtered annotation results; optionally apply Ion Identity grouping to reduce redundant features.: "Inventa is capable to performe the calcultions based on the results from Ion Identity, reducing the total number of features."
• [methods] For each sample, calculate FC as (count of specific non-annotated features) / (total features in sample).: "The Feature Component (FC) is a ratio of the number of specific non-annotated features over the total number of features of each extract."