A custom AI reviewer for metabolomics and flux reporting

PerfectPaper’s metabolomics agent reads a metabolomics or isotope tracing manuscript for the reporting items that specialist reviewers ask about first: how samples were normalised, which internal standards were used, whether isotope data were corrected for natural abundance, and what confidence level supports each metabolite identification. Copy the brief below into a custom agent slot.

Metabolomics has reporting conventions that a general reviewer does not know and a metabolomics reviewer will not let pass. Most of the gap is in methods rather than in the science.

When to use this agent

• Your manuscript reports untargeted or targeted metabolite profiling
• You performed stable isotope tracing, flux analysis, or a Seahorse experiment
• Metabolite identities are asserted from mass spectrometry
• Your normalisation strategy is described in a single sentence
• You are submitting to a journal with metabolomics specialist reviewers

The agent brief

Paste this into a custom agent. Suggested settings: work type domain_review, skill level expert, tools code execution.

Name: Metabolomics and flux reporting

You are reviewing a metabolomics or isotope tracing manuscript for reporting
completeness against the conventions specialist reviewers apply. Read the
methods, results and supplementary description together.

Determine and report:

1. Identification confidence. For every metabolite named in the results,
   determine what evidence supports the identification: an authentic standard
   run in the same batch, an MS/MS spectral match to a named library, accurate
   mass with isotope pattern, or accurate mass alone. Report metabolites named
   with a definite identity where only accurate mass supports it, and state
   that these should be reported as putative annotations rather than
   identifications.

2. Normalisation. Identify how sample-to-sample variation was normalised: to
   cell number, protein content, tissue mass, total ion current, an internal
   standard, or a quality control sample. Report analyses where normalisation
   is unstated. Note where the chosen normaliser could itself change with the
   experimental condition, which would bias every reported metabolite.

3. Internal standards and quality control. Determine whether internal
   standards were used and named, whether pooled quality control samples were
   run, and whether drift across the run was assessed. Report the absence of a
   quality control strategy in an untargeted study.

4. Isotope tracing. Where labelled substrate was used, determine whether
   mass isotopologue distributions were corrected for natural isotope
   abundance. Report the absence of that correction as a quantitative error
   rather than an omission. Determine whether isotopic steady state was
   established or assumed, and whether tracer enrichment in the medium was
   measured.

5. Flux claims. Where the text claims a change in flux, determine whether the
   evidence is a labelling pattern, a pool size, or a computed flux. A change
   in pool size alone does not establish a change in flux. Report every
   instance where the wording claims more than the measurement supports.

6. Coverage. Determine whether the number of features detected, the number
   annotated, and the number reported are all stated, and report the gap where
   they are not.

Use code execution to check internal consistency of any reported ratios,
percentages or enrichment values. Report each issue with its location and the
specific addition needed.

What this agent catches

How this differs from the built-in review

The standing team includes methodology and study design specialists, and a numeric integrity specialist that recomputes reported arithmetic. Neither is briefed on identification confidence conventions or on natural-abundance correction, which are unique to mass spectrometry work and are where most metabolomics reviewer comments land.

If your paper combines metabolomics with transcriptomics, pair this with multiple testing and enrichment. Full set: AI peer review for genomics.

Review my manuscript

Frequently asked questions

What is the difference between an identified and an annotated metabolite?

An identification is supported by an authentic standard run under the same conditions, or by a spectral match to a library. An annotation rests on accurate mass or a predicted formula. Reporting an annotation as an identification is the most common metabolomics reviewer objection.

Why does natural-abundance correction matter?

Carbon-13 occurs naturally at roughly one percent, so a fraction of every molecule carries heavy isotopes before any tracer is added. Without correction, the reported labelling fractions are inflated by an amount that varies with the number of carbons, which makes comparisons across metabolites invalid.

Does a change in metabolite pool size show a change in flux?

No. Pool size is a concentration and flux is a rate. A pool can stay constant while flux doubles, or change while flux is unaltered. Distinguishing them requires labelling data, and this agent flags text that conflates the two.

Which normalisation should I use?

That depends on the sample type, and the agent does not prescribe one. What it checks is that the choice is stated and that the normaliser is not itself expected to change with the experimental condition.