From FASTQ or tables · Public data · Multi-omics integration

Analysis of Your Data

If you already have the data, we start from there. We analyze raw sequencing data, expression tables or results from other platforms, reuse public data to validate or extend your findings, and combine several omics layers to answer your question.

A dedicated scientist
Bespoke design
Direct access to the team
Full confidentiality
Starting point

Wherever your data are

Whatever stage of the process they are at, we pick up the analysis from the format you have.

Raw data

Sequencing reads or instrument files straight from the platform. We handle quality control and all processing.

FASTQBAM / CRAMmzML / RAW

Processed tables

Count matrices, abundance tables or results from another provider that need deeper analysis or a second opinion.

Count matricesNPX (Olink)VCFAbundance tables

Public data

Published studies in international repositories that can be reanalyzed with your question in mind.

GEO / SRATCGAGTExENA / PRIDE
Data types

Which omics we analyze

We work with data from every omics layer, from whole tissue or single cells.

Transcriptomics

mRNA-seq, Total RNA-seq, lncRNA, circRNA, small RNA and Iso-Seq.

Single-cell and spatial

scRNA-seq, snRNA-seq, TCR/BCR and spatial data.

Genomics

WGS and WES, variants, CNVs and genome assembly.

Epigenomics

DNA methylation, ChIP-seq and RIP-seq.

Metagenomics

16S/18S/ITS amplicons, shotgun and metatranscriptomics.

Proteomics

Olink (NPX) and mass spectrometry data.

Metabolomics

Untargeted LC-MS: detection, annotation and statistics.

Clinical data

Clinical and phenotypic variables to integrate with the omics.

Public data

Reusing public data

Thousands of published omics studies have accessible data. Properly reanalyzed, they add a lot of value, especially when your own samples are few.

Multi-omics integration

Several layers, one biological reading

Each omics layer tells part of the story. Integrating them shows how a change in the genome or in expression translates into proteins and metabolites.

  • Cross-layer correlation: which genes, proteins and metabolites change together
  • Latent factor models: sources of variation shared across several omics
  • Machine learning: multi-omics signatures to classify or predict a response
  • Networks and pathways: joint interpretation in biological processes
GenomeTranscriptomeProteomeMetabolomeIntegratedreading
The different omics layers are combined to explain the same biological phenomenon.
Deliverables

What you receive

  • Data quality report

    A review of your starting data before analysis, so you know what questions they can answer.

  • Complete analysis

    Differential expression, pathways, networks or integration, depending on your question.

  • Publication-ready figures

    Plots ready for a paper, report or committee.

  • Interpreted report and meeting

    What the results mean and what the next steps are.

Intusomics® Explorer

Access to our software to explore the results without coding: search any gene or protein, compare groups and build interactive plots.

Do you have data to analyze?

Tell us what data you have and what you want to answer. We will tell you what can be obtained from them and send you a proposal.