Analyse & interpret

Bioinformatics & AI Solutions

Find the right support for your data type or research objective.

Discuss Your Requirements

Discuss Your Requirements

Explore

Six areas of analysis

Find the right support for your data.

Choose the area closest to your data or question. Each one shows what we help with, what you bring and what you receive.

Omics & Multi-Omics Analysis

01

Omics & Multi-Omics Analysis

Individual molecular layers and integrated biological insight.

AI & Machine Learning Development

02

AI & Machine Learning Development

Research models built around your data and scientific question.

Biomarker Discovery & Validation

03

Biomarker Discovery & Validation

Identify and assess molecular indicators of disease and response.

Drug Discovery & Target Prioritisation

04

Drug Discovery & Target Prioritisation

Molecular evidence to guide therapeutic research.

Clinical Data & Biostatistics

05

Clinical Data & Biostatistics

Connect research data with clinical characteristics and outcomes.

Public Dataset Research

06

Public Dataset Research

Investigate your question using existing research datasets.

Omics & Multi-Omics Analysis

Individual molecular layers and integrated biological insight.

Strong biology rarely sits in one data type. We analyse each molecular layer on its own terms, then bring them together to find shared patterns and subgroups.

Questions we help with

01 Which layers does my question need, and how should each be processed?

02 Do the layers agree, and where do they add new information?

03 Which molecular subgroups or pathways emerge when I integrate them?

04 How do I present the result so it is clear and reproducible?

Where we can help

01

Genomics & Variant Analysis

Mutation landscapes, variant annotation, copy-number alterations and mutational signatures.

02

Bulk RNA-seq

Quality control, expression quantification, differential expression and interpretation.

03

Single-Cell & Spatial Transcriptomics

Cell populations, expression patterns and tissue context.

04

Proteomics & Phosphoproteomics

Protein abundance, phosphorylation and signalling analysis.

05

Metabolomics & Lipidomics

Metabolic profiles, lipid composition and differential abundance.

06

Multi-Omics Integration

Shared patterns, molecular subgroups and cross-omics associations.

07

Pathway & Network Analysis

Enrichment, biological pathways and molecular relationships.

Your starting point

Your starting point

Research objectives, available data, current challenges and intended outcomes.

Agreed deliverables

Agreed deliverables

Analysis outputs, figures, interpretation and methods documentation.

Sample project

See how a multi-omics project comes together.

Click through the tabs. Illustrative sample data, no client information.

DataQualityIntegrationInsightmulti-omics · 4 layers96samples4omics layers38,400features94%matchedLAYERS MEASURED4 layerseach layer profiled on the same samplesGenomics21,000RNA-seq12,400Proteomics3,800Metabolites1,200SAMPLE COVERAGE96 samplesoverlap across layers is what we integrateDNARNAProtMetabfirst 24 samples shownFEATURE MIX38,400features per layer, scaled for comparisonGenomics55%RNA-seq32%Proteomics10%Metabolites3%BATCH CORRECTIONbefore → afterbatch effects removed, biology keptBatch 1Batch 2Batch 3→MISSING VALUES% missingimputed carefully, layer by layerMetabolites14%Proteomics9%RNA-seq3%Genomics1%CROSS-LAYER LINKpairswhich layers agree before integrationDNADNARNARNAProtProtMetMet96samples kept3flagged0.97mean correlation5QC checksSAMPLE QCper layeroutliers flagged for review, not hiddenDNARNAProtMetlowhigh qualitySEQUENCING DEPTHreads / sampledepth is even across the cohortstandardised valueMISSINGNESSby samplesamples with gaps are reviewedS01S12BATCH EFFECTPC1 vs PC2before correction: samples cluster by batchAFTER CORRECTIONPC1 vs PC2batches mix, biology is preservedQC CHECKLIST5 checksevery layer passes before analysisSample identitypassedDepth & coveragepassedBatch reviewpassedOutlier reviewpassedReference matchpassed4layers combined8shared factors61%variance explained1joint modelHOW LAYERS CONNECTjoint modelone model sees all four layers togetherFactorsDNARNAProtMetVARIANCE BY FACTOR% explainedeach factor draws on different layersF130%F222%F316%F412%F58%F66%LAYER CONTRIBUTIONper factorno single layer explains everythingDNARNAProtMetF1F2F3F4F5F6SAMPLE MAPfactor 1 vs 2samples separate by joint signalTOP FEATURESfactor 2features ranked within each layerRNA0.92Prot0.81Met0.66DNA0.52RNA0.41STABILITYresamplesfactors hold across repeated runsruns →3sample groups12key pathways46priority features4layers agreeSAMPLE GROUPS3 groupsgroups defined by the joint signalPATHWAY EVIDENCEstrengthpathways supported by several layersImmune92Cell cycle78Lipid64Stress51Signalling40SUPPORT BY LAYERper pathwaymore layers, stronger evidenceImmuneCell cycleLipidStressDNARNAProtMetPRIORITY FEATUREStop 46ranked for follow-uprank 1rank 9FOLLOW-UPnext stepswhat the evidence supports1Validate in an independent cohort2Test features in a targeted assay3Link groups to clinical outcome4Report methods and limitationsREPORTdeliveredfigures, tables and plain-language summaryillustrative sample data
Omics & Multi-Omics Analysis

01

Omics & Multi-Omics Analysis

Individual molecular layers and integrated biological insight.

AI & Machine Learning Development

02

AI & Machine Learning Development

Research models built around your data and scientific question.

Biomarker Discovery & Validation

03

Biomarker Discovery & Validation

Identify and assess molecular indicators of disease and response.

Drug Discovery & Target Prioritisation

04

Drug Discovery & Target Prioritisation

Molecular evidence to guide therapeutic research.

Clinical Data & Biostatistics

05

Clinical Data & Biostatistics

Connect research data with clinical characteristics and outcomes.

Public Dataset Research

06

Public Dataset Research

Investigate your question using existing research datasets.

Omics & Multi-Omics Analysis

Individual molecular layers and integrated biological insight.

Strong biology rarely sits in one data type. We analyse each molecular layer on its own terms, then bring them together to find shared patterns and subgroups.

Questions we help with

01 Which layers does my question need, and how should each be processed?

02 Do the layers agree, and where do they add new information?

03 Which molecular subgroups or pathways emerge when I integrate them?

04 How do I present the result so it is clear and reproducible?

Where we can help

01

Genomics & Variant Analysis

Mutation landscapes, variant annotation, copy-number alterations and mutational signatures.

02

Bulk RNA-seq

Quality control, expression quantification, differential expression and interpretation.

03

Single-Cell & Spatial Transcriptomics

Cell populations, expression patterns and tissue context.

04

Proteomics & Phosphoproteomics

Protein abundance, phosphorylation and signalling analysis.

05

Metabolomics & Lipidomics

Metabolic profiles, lipid composition and differential abundance.

06

Multi-Omics Integration

Shared patterns, molecular subgroups and cross-omics associations.

07

Pathway & Network Analysis

Enrichment, biological pathways and molecular relationships.

Your starting point

Your starting point

Research objectives, available data, current challenges and intended outcomes.

Agreed deliverables

Agreed deliverables

Analysis outputs, figures, interpretation and methods documentation.

Sample project

See how a multi-omics project comes together.

Click through the tabs. Illustrative sample data, no client information.

DataQualityIntegrationInsightmulti-omics · 4 layers96samples4omics layers38,400features94%matchedLAYERS MEASURED4 layerseach layer profiled on the same samplesGenomics21,000RNA-seq12,400Proteomics3,800Metabolites1,200SAMPLE COVERAGE96 samplesoverlap across layers is what we integrateDNARNAProtMetabfirst 24 samples shownFEATURE MIX38,400features per layer, scaled for comparisonGenomics55%RNA-seq32%Proteomics10%Metabolites3%BATCH CORRECTIONbefore → afterbatch effects removed, biology keptBatch 1Batch 2Batch 3→MISSING VALUES% missingimputed carefully, layer by layerMetabolites14%Proteomics9%RNA-seq3%Genomics1%CROSS-LAYER LINKpairswhich layers agree before integrationDNADNARNARNAProtProtMetMet96samples kept3flagged0.97mean correlation5QC checksSAMPLE QCper layeroutliers flagged for review, not hiddenDNARNAProtMetlowhigh qualitySEQUENCING DEPTHreads / sampledepth is even across the cohortstandardised valueMISSINGNESSby samplesamples with gaps are reviewedS01S12BATCH EFFECTPC1 vs PC2before correction: samples cluster by batchAFTER CORRECTIONPC1 vs PC2batches mix, biology is preservedQC CHECKLIST5 checksevery layer passes before analysisSample identitypassedDepth & coveragepassedBatch reviewpassedOutlier reviewpassedReference matchpassed4layers combined8shared factors61%variance explained1joint modelHOW LAYERS CONNECTjoint modelone model sees all four layers togetherFactorsDNARNAProtMetVARIANCE BY FACTOR% explainedeach factor draws on different layersF130%F222%F316%F412%F58%F66%LAYER CONTRIBUTIONper factorno single layer explains everythingDNARNAProtMetF1F2F3F4F5F6SAMPLE MAPfactor 1 vs 2samples separate by joint signalTOP FEATURESfactor 2features ranked within each layerRNA0.92Prot0.81Met0.66DNA0.52RNA0.41STABILITYresamplesfactors hold across repeated runsruns →3sample groups12key pathways46priority features4layers agreeSAMPLE GROUPS3 groupsgroups defined by the joint signalPATHWAY EVIDENCEstrengthpathways supported by several layersImmune92Cell cycle78Lipid64Stress51Signalling40SUPPORT BY LAYERper pathwaymore layers, stronger evidenceImmuneCell cycleLipidStressDNARNAProtMetPRIORITY FEATUREStop 46ranked for follow-uprank 1rank 9FOLLOW-UPnext stepswhat the evidence supports1Validate in an independent cohort2Test features in a targeted assay3Link groups to clinical outcome4Report methods and limitationsREPORTdeliveredfigures, tables and plain-language summaryillustrative sample data
Omics & Multi-Omics Analysis

01

Omics & Multi-Omics Analysis

Individual molecular layers and integrated biological insight.

AI & Machine Learning Development

02

AI & Machine Learning Development

Research models built around your data and scientific question.

Biomarker Discovery & Validation

03

Biomarker Discovery & Validation

Identify and assess molecular indicators of disease and response.

Drug Discovery & Target Prioritisation

04

Drug Discovery & Target Prioritisation

Molecular evidence to guide therapeutic research.

Clinical Data & Biostatistics

05

Clinical Data & Biostatistics

Connect research data with clinical characteristics and outcomes.

Public Dataset Research

06

Public Dataset Research

Investigate your question using existing research datasets.

Omics & Multi-Omics Analysis

Individual molecular layers and integrated biological insight.

Strong biology rarely sits in one data type. We analyse each molecular layer on its own terms, then bring them together to find shared patterns and subgroups.

Questions we help with

01 Which layers does my question need, and how should each be processed?

02 Do the layers agree, and where do they add new information?

03 Which molecular subgroups or pathways emerge when I integrate them?

04 How do I present the result so it is clear and reproducible?

Where we can help

01

Genomics & Variant Analysis

Mutation landscapes, variant annotation, copy-number alterations and mutational signatures.

02

Bulk RNA-seq

Quality control, expression quantification, differential expression and interpretation.

03

Single-Cell & Spatial Transcriptomics

Cell populations, expression patterns and tissue context.

04

Proteomics & Phosphoproteomics

Protein abundance, phosphorylation and signalling analysis.

05

Metabolomics & Lipidomics

Metabolic profiles, lipid composition and differential abundance.

06

Multi-Omics Integration

Shared patterns, molecular subgroups and cross-omics associations.

07

Pathway & Network Analysis

Enrichment, biological pathways and molecular relationships.

Your starting point

Your starting point

Research objectives, available data, current challenges and intended outcomes.

Agreed deliverables

Agreed deliverables

Analysis outputs, figures, interpretation and methods documentation.

Sample project

See how a multi-omics project comes together.

Click through the tabs. Illustrative sample data, no client information.

DataQualityIntegrationInsightmulti-omics · 4 layers96samples4omics layers38,400features94%matchedLAYERS MEASURED4 layerseach layer profiled on the same samplesGenomics21,000RNA-seq12,400Proteomics3,800Metabolites1,200SAMPLE COVERAGE96 samplesoverlap across layers is what we integrateDNARNAProtMetabfirst 24 samples shownFEATURE MIX38,400features per layer, scaled for comparisonGenomics55%RNA-seq32%Proteomics10%Metabolites3%BATCH CORRECTIONbefore → afterbatch effects removed, biology keptBatch 1Batch 2Batch 3→MISSING VALUES% missingimputed carefully, layer by layerMetabolites14%Proteomics9%RNA-seq3%Genomics1%CROSS-LAYER LINKpairswhich layers agree before integrationDNADNARNARNAProtProtMetMet96samples kept3flagged0.97mean correlation5QC checksSAMPLE QCper layeroutliers flagged for review, not hiddenDNARNAProtMetlowhigh qualitySEQUENCING DEPTHreads / sampledepth is even across the cohortstandardised valueMISSINGNESSby samplesamples with gaps are reviewedS01S12BATCH EFFECTPC1 vs PC2before correction: samples cluster by batchAFTER CORRECTIONPC1 vs PC2batches mix, biology is preservedQC CHECKLIST5 checksevery layer passes before analysisSample identitypassedDepth & coveragepassedBatch reviewpassedOutlier reviewpassedReference matchpassed4layers combined8shared factors61%variance explained1joint modelHOW LAYERS CONNECTjoint modelone model sees all four layers togetherFactorsDNARNAProtMetVARIANCE BY FACTOR% explainedeach factor draws on different layersF130%F222%F316%F412%F58%F66%LAYER CONTRIBUTIONper factorno single layer explains everythingDNARNAProtMetF1F2F3F4F5F6SAMPLE MAPfactor 1 vs 2samples separate by joint signalTOP FEATURESfactor 2features ranked within each layerRNA0.92Prot0.81Met0.66DNA0.52RNA0.41STABILITYresamplesfactors hold across repeated runsruns →3sample groups12key pathways46priority features4layers agreeSAMPLE GROUPS3 groupsgroups defined by the joint signalPATHWAY EVIDENCEstrengthpathways supported by several layersImmune92Cell cycle78Lipid64Stress51Signalling40SUPPORT BY LAYERper pathwaymore layers, stronger evidenceImmuneCell cycleLipidStressDNARNAProtMetPRIORITY FEATUREStop 46ranked for follow-uprank 1rank 9FOLLOW-UPnext stepswhat the evidence supports1Validate in an independent cohort2Test features in a targeted assay3Link groups to clinical outcome4Report methods and limitationsREPORTdeliveredfigures, tables and plain-language summaryillustrative sample data

See delivery in action

Follow your project from first call to final report.

Every project has a delivery dashboard. Click through the four phases to see what happens and what you receive. Illustrative sample data.

DiscussDefineDeliverReviewweek 7 of 11 · on track1kick-off call5objectives agreed5data sources100%completeOBJECTIVESagreedResearch question definedSuccess criteria agreedData sources listedConstraints notedKick-off call heldDATA AVAILABLE5 sourcesClinicalRNAProteinMetabolitesOutcomesourceswhat we start fromFROM QUESTION TO PLANnarrowingOpen questions12Candidate aims6Priorities3Agreed plan1scope agreed with youPRIORITIESrelative valueQuestion A9Question B7Question C5Question D3ranked with your teamWHO IS INVOLVEDrolesYour team40%Scientific lead35%Technical lead25%clear owners from day onePROJECT PLANphasesDiscusscompleteDefineplannedDeliverplannedReviewplanned1scope signed off7deliverables agreed11weeks planned100%completeSCOPE AND DATAchecksData receivedQuality checkedScope agreedDeliverables listedTimeline setDATA QUALITYsamplesquality scorelow-quality samples flagged earlyDELIVERABLES AGREEDby typeDocumentation43%Analysis outputs29%Interpretation29%seven items, agreed up frontEFFORT BY PHASEweeksDeliver6Review2Define2Discuss1eleven weeks in totalDEPENDENCIEStrackedData accessCompute planClinical metadataone open itemSIGN-OFFstepsScopecompletePlancompleteStartcomplete62%phase complete2 / 7delivered4 / 8milestones3open actionsMILESTONE PROGRESS% completeQC and processing100Integrated analysis70Figures30Interpretation15where each step standsANALYSIS WORKFLOWstepsQCcompleteProcesscompleteIntegratein progressInterpretplannedFiguresplannedDELIVERABLESstatusData quality summaryProcessed data and methods logResults tables (in review)Scientific figuresInterpretationPROGRESS VS PLANon trackActualPlanproject weektracking the planINTERIM RESULTshared for commentlog₂ fold change−log₁₀ pdraft output reviewed with youOPEN ACTIONSownersConfirm outcome definitionShare draft figuresSchedule review meetingWk 9draft report1review meeting7deliverables0%startedREPORT CONTENTSplannedSummaryMethodsResultsInterpretationLimitationsNext stepsREVIEW PATHstepsDraftplannedCommentsplannedMeetingplannedFinalplannedFINDINGSeffect, 95% CIFinding 1Finding 2Finding 3effect size (95% CI)evidence shown with each resultNEXT-STEP OPTIONSrankedValidate9Extend cohort7Follow-up study5agreed in the review meetingHANDOVER PACKcontentsResults tablesFiguresFinal reportMethods documentationCode and logsREVIEW MEETINGagendaFindings walked throughQuestions capturedActions agreedillustrative sample project, no client data

Delivered with every project

Analysis outputs

Results tables and scientific figures

Interpretation

Findings, evidence and limitations

Documentation

Agreed methods and technical outputs

Start a conversation

What are you working on?

Discuss your research question and the support you need.

Discuss Your Project

Discuss Your Project