What You Receive

Every DataCore analysis ends in the same package: a report you can read, figures you can publish, and code you can rerun.

Research teams are often handed a folder of output files and left to work out what they mean. We do the opposite. The deliverable is written for the person who asked the research question, not for another bioinformatician.

Below is what is in every handover, regardless of whether the project was a two-week differential expression analysis or a six-month cohort study.

What Is In Every Handover

Delivered as a single archive plus a walkthrough call.

01

Analysis Report

A written document, typically 15 to 40 pages. Plain-language summary first, then methods, results, figures, interpretation and limitations. The methods section is written so it can go straight into your manuscript.

02

Publication-Ready Figures

Vector PDF and high-resolution PNG at journal specifications, with the plotting code included so you can adjust colours, labels or panel layout yourself.

03

Results Tables

Full result tables as CSV and Excel — differential expression results, variant annotations, model coefficients, summary statistics — with a data dictionary explaining every column.

04

Code and Environment

The complete analysis as a versioned repository: scripts or a Nextflow/Snakemake workflow, a Conda environment or container definition, and a README that tells your team how to rerun it.

05

Quality Control Summary

Read quality, alignment rates, duplication, batch effects, outliers and anything else that could affect interpretation — reported honestly, including problems we found.

06

Walkthrough Call

A recorded call where the analyst goes through the report with your team, answers questions and explains what each figure shows and does not show.

A Worked Example: RNA-Seq Differential Expression

Section 1 — Summary

One page, no jargon. What was compared, how many genes changed, which biological processes those genes belong to, and the two or three findings that matter most for your research question.

Section 2 — Quality Control

Per-sample read counts, alignment rates and duplication levels. A principal component plot showing how samples cluster, and an honest statement about whether any sample should be excluded and why.

Section 3 — Methods

Written in manuscript style, with every tool named and versioned. For example: reads trimmed with fastp v0.23.4, aligned to GRCh38 with STAR v2.7.11a, quantified with featureCounts, differential expression with DESeq2 v1.42.0 using an FDR threshold of 0.05.

Section 4 — Results

The figures your paper will use:

  • MA and volcano plots of differential expression
  • Heatmap of the top differentially expressed genes with sample annotation
  • Principal component and sample correlation plots
  • Gene set enrichment and over-representation results (GO, KEGG, Reactome)
  • Full results table: gene ID, symbol, log fold change, standard error, p-value, adjusted p-value, mean expression

Section 5 — Interpretation and Limitations

What the results support, what they do not, and what would be needed to go further. If the study was underpowered for the comparison you wanted, this section says so plainly — better to know before a reviewer tells you.

Section 6 — Reproducibility

File manifest with checksums, software versions, reference genome and annotation versions, random seeds, and the exact commands run. Anyone with the archive can reproduce every number in the report.

Deliverables By Analysis Type

Input dataWhat we doWhat you receive
Gene expression / RNA-SeqDifferential expression, clustering, pathway enrichmentVolcano and MA plots, heatmaps, enrichment tables, DESeq2/edgeR results
Single-cellQC, integration, clustering, cell type annotation, marker discoveryUMAP plots, annotated cluster markers, cell proportion tables, Seurat/Scanpy object
Genetic variationAlignment, variant calling, annotation, filtering, associationAnnotated VCF, variant tables, Manhattan and QQ plots, association statistics
Pathogen genomicsConsensus assembly, lineage assignment, phylogeneticsConsensus FASTA, lineage report, annotated phylogenetic tree, mutation table
MetagenomicsTaxonomic and functional profiling, diversity analysisAbundance tables, diversity plots, differential abundance results, ordination plots
Clinical & epidemiologicalStudy design, modelling, survival and regression analysisStatistical analysis plan, model outputs, forest and Kaplan-Meier plots, CONSORT-ready tables
Machine learningFeature selection, model training, validation, interpretationPerformance metrics against a baseline, ROC and calibration curves, feature importance, trained model and inference code
Contact DataCore Analytics

Tell us about your data and we will scope it — free, within one working day.

+233 558 017 827