Trusted by thousands of researchers and 48 papers published so far. Over 420.000 samples analyzed 104.000 times!

Huge collections of samples

InSilico DB aggregates more than 250,000 samples of microarray and RNA–Seq data (human, mouse and rat) coming from public repositories including GEO and TCGA.

As an InSilico DB user

You have access to these data in a standardized format.

You can run the following analysis tools: GENE–E, Morpheus, GenePattern, GenomeSpace, Integrative Genomics Viewer, R/Bioconductor and QIAGEN Ingenuity Pathways Analysis, without any programming skills.

You can also upload your own data coming from your own research and run the same tools.

You can aggregate analyses of your private data with public data.

You can share your private data with selected users.

You belong to a huge community of users contributing by adding data, curations and compilations.

Access public data from one central place

I can access public data from one central place

  • More than 250,000 samples from various public repositories including GEO and TCGA, updated daily.
  • A powerful yet easy-to-use search interface: search by disease, author or gene
  • All data is uniformly processed and formatted
Access public data from one central place
Easily run analysis tools on these data

I can easily run analysis tools on these data

  • The main analysis tools seamlessly integrated: GENE–E, Morpheus, GenePattern, GenomeSpace, Integrative Genomics Viewer, R/Bioconductor and Ingenuity Pathways Analysis
  • No need for a bioinformatician, data are ready to be analyzed
  • All results available through one convenient web interface
Do the same with your own data and compare them with public data

I can do the same with my own data and compare them with public data

  • Simply create an Excel spreadsheet and upload it, or upload your raw data
  • Studies are composed of multiple samples. Advanced data manipulation lets you pick samples across studies then assemble them into a new aggregated study that contains exactly what you need.
  • Make data compilations, create subsets of samples from a large study, or compare your own data to existing published data.
Do the same with your own data and compare them with public data

What users are saying?

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Become a better biologist by getting started in genomics without programming.

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