Ref H9
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Metabolomics data is rich and unusually easy to over-interpret. We process untargeted and targeted LC-MS and GC-MS data from raw files through peak detection, alignment, normalisation and statistical analysis to pathway interpretation — and we report metabolite identifications with confidence levels rather than presenting every annotation as a confirmed compound. Quality control samples, drift correction and batch structure are treated as central, because analytical variation is often larger than the biology.
Key Features & Advantages
Identification Confidence Levels Stated
Annotations are reported against community confidence levels, so a match on accurate mass alone is clearly distinguished from one confirmed with a standard.
Drift And Batch Corrected
Pooled quality control samples are used to correct signal drift and batch effects, and the correction is shown rather than silently applied.
Statistics Appropriate To The Data
Univariate and multivariate methods are used together, with cross-validation and permutation testing that guard against the overfitting endemic to metabolomics studies.
Pathway Interpretation With Context
Enrichment is performed against the measurable metabolome rather than the entire database, which substantially changes which pathways come out as significant.
Where This Service Is Used
1Comparing metabolic profiles between disease and control groups
2Characterising metabolic response to a drug, diet or environmental exposure
3Discovering candidate metabolite biomarkers for later targeted validation
4Integrating metabolomics with transcriptomics or proteomics for pathway-level interpretation
5Profiling natural product extracts and secondary metabolites
Methods and Techniques Applied
1Raw data conversion, peak picking, retention time alignment, grouping and gap filling
2Quality control assessment with pooled samples, drift correction and batch adjustment
3Normalisation, transformation and scaling chosen for the data and documented
4Univariate testing with multiple testing correction and multivariate modelling with proper validation
5Metabolite annotation against spectral libraries with confidence level assignment, including molecular networking where useful
6Pathway and enrichment analysis against an appropriate measurable background
What We Need From You
1Raw mass spectrometry files with the chromatographic and acquisition method described
2Sample metadata including group, batch, run order and the quality control sample scheme
3Any internal standards used and, for targeted work, the compound list and calibration data
4The biological question and which comparisons matter
What You Receive
1Processed feature table with retention time, mass, intensity and quality metrics
2Annotated metabolite list with identification confidence levels
3Statistical results including univariate tables and validated multivariate models
4Pathway analysis and integrated interpretation with other omics layers where supplied
5Full report with methods, parameters, library versions and analysis code
What We Compute and Report
1Feature tables with retention time, accurate mass, intensity and quality metrics
2Metabolite annotations reported against community identification confidence levels
3Drift and batch correction results shown before and after adjustment
4Univariate statistics and cross-validated multivariate models with permutation testing
5Pathway enrichment computed against the measurable metabolome as background
Questions We Are Asked Before Scoping
How many features will be identified?
▸Typically only a minority of detected features receive a confident annotation, and a considerable proportion of any untargeted dataset remains unidentified. We report this honestly rather than inflating the annotated fraction.
Is a database match a confirmed identification?
▸No. Accurate mass and spectral similarity give a putative annotation. Confirmation requires a matching authentic standard under the same conditions, which is why confidence levels are reported.
Can metabolomics identify biomarkers?
▸It can generate candidates. Biomarker claims require independent validation in a separate cohort with a targeted quantitative assay, and untargeted discovery studies routinely fail that step.
Interpretation and Limitations
Computational predictions prioritise hypotheses; they do not independently establish binding, biological activity, therapeutic efficacy, safety or clinical validity. Performance depends on input quality, target class, the structural evidence available and the applicability domain of the selected methods. Scores used for ranking are comparative devices and should not be read as measured quantities. All deliverables are for research use only and are not intended for diagnostic or therapeutic use.
Is This the Right Service for You?
Choose this when
Untargeted profiling needs processing, annotation and statistics that will survive review
Analytical drift and batch structure risk swamping the biological signal
Metabolite candidates need prioritising for targeted validation
Consider another service when
The question is about proteins rather than small molecules
Proteomics & PTM Analysis Structural characterisation of a purified compound is required
Polysaccharide Structure & Spectral Analysis What Goes Wrong, and How We Handle It
These are the failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.
Database matches presented as confirmed identifications
Annotations are reported against community confidence levels, so a mass-only match is visibly different from a standard-confirmed one.
Multivariate models overfitted to small sample numbers
Cross-validation and permutation testing are applied, and models that fail permutation are reported as failing.
Enrichment computed against the full database rather than what was measurable
The measurable metabolome is used as background, which substantially changes which pathways appear significant.
A Worked Example
A biomarker candidate that was a run-order artefact
The situation
An untargeted study produced a strongly discriminating feature that the team wanted to develop into an assay.
What the work involved
Pooled quality control samples were used to characterise drift, batch and run-order effects were modelled, and the analysis was repeated after correction.
What came back
The feature tracked run order almost perfectly and did not survive correction. Four other features did survive, and were taken forward to targeted quantification instead.
Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.
Service Workflow
1
Scientific Consultation
We discuss the objective, the decision it supports and the data you hold. Nothing is quoted before we understand what the result has to answer.
2
Feasibility Review
We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.
3
Technical Proposal
Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.
4
Computation and QC
The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.
5
Report and Follow-Up
You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.
Service Process
1
Online Consultation
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2
Solution Matching
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3
Service Contract
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4
Project Execution
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5
Data Delivery
Specifications & Turnaround
Note: ICDMO delivers computational (in silico) services. Results prioritise hypotheses for testing; they do not by themselves establish binding, biological activity, efficacy or safety. All deliverables are for research use only and are not intended for diagnostic or therapeutic use. Downstream experimental work is scoped separately and executed through qualified partners.
Get a Custom Quote
Our scientific team responds within 24 hours with a detailed technical proposal and pricing tailored to your project.
Contact Us Online ConsultationStandard Deliverables
✓Sequence-verified final product
✓Full QC documentation
✓Certificate of Analysis (CoA)
✓Electronic data files
✓Project completion report
Why ICDMO?
Computational CDMO Focus
Reproducible, Versioned Pipelines
Confidence & Limitations Reported
Experiment-Aware Recommendations
Response Within One Business Day