The transcriptome is comprised of all RNA molecules including mRNA, rRNA, tRNA, and other non-coding RNA transcribed in one cell or a population of cells. IntegraGen is a leading expert in the study of the transcriptome and the measure of RNA expression.


RNA-Seq uses deep sequencing technologies for transcriptome profiling that provides more precise measurement of levels of transcripts and their isoforms than other methods. IntegraGen is an expert with RNA-Seq and can assist researchers to design and implement a wide variety of RNA-Seq related studies.

  •  Experience across a full range of RNA-Seq study types
  •  We also offer clinical grade RNA-Seq for personalized medicine studies
  •  Cutting-edge, constantly updated bioinformatic pipelines
  •  We can provide you with advice on experimental design
  •  Support from study design through tertiary biostatistical analysis via our GeCo Advanced Genomic Consulting Service


3′ RNA-Seq

We have developed a powerful workflow to access cancer gene expression signatures. Dowload our paper to discover the technical features of our 3’ RNA-seq protocol for accurate gene transcriptomic analysis of challenging tumor samples.

Small RNA Sequencing

IntegraGen has developed and streamlined a process for measuring small RNA expression in tissue samples and liquid biopsies (serum or plasma) via next generation sequencing. We offer researchers a small RNA sequencing service for examining gene regulation at both the transcriptional and post-transcriptional level.



  • Fully optimized process – from extraction to bioinformatics
  • Start with either frozen or FFPE tissue, circulating blood cells, or liquid biopsies
  • Able to extract total RNA from either plasma or serum
  • Library prep uses random adapters via in-house developed protocol
    •  Reduces bias representation
    •  Provides better correlation
  • State-of-the-art analysis using snRNAbench mutational signatures


Bioinformatic analyses

IntegraGen assists researchers to overcome RNA-Seq related bioinformatics challenges through the development of efficient methods to extract the relevant information from large amounts of data.

  •  Identification of differentially expressed genes and pathways
  •  de novo event discovery
    •  Fusion
    •  Splice variants
    •  Long non-coding RNA

genomic data analysis workflow

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