Bulk Transcriptomics

Our OP² bulk transcriptomics pipeline is a bioinformatics analysis workflow used for bulk RNA sequencing data.
It allows you to analyze your RNA sequencing data using this gold standard analysis pipeline.
You get insights into the quality of your data, differential expression levels of multiple genes, and gene enrichment analysis.

The workflow processes raw data from FastQ inputs, aligns the reads, generates counts relative to genes and performs extensive quality-control on the results.
These results are made available to you via two interactive reports, and a data package with all essential intermediate files to perform more in-depth data analysis.
The pre-processing workflow processes your raw sequence data until QC approved aligned data.
Next, the post-processing workflow enables you to review the biological meaning of your data via a statistical analysis approach.

Example Pre-processing Report
Example Post-processing Report
  • 1


    Whole (un)stranded transcriptome data (e.g. Illumina)
    Single- or paired-en compressed raw FastQ files
    Reference transcriptome (hg19 or hg38 or mm10)

  • 2

    Sequence QC

    Reads with low-quality are discarded

  • 3


    Adaptor and quality trimming of reads

  • 4


    STAR aligns reads to reference transcriptome

  • 5

    Alignment QC

    Alignment statistics: read depths, per base, GC content, …

  • 6

    Transcript quantification

    Aligned reads are assigned to genomic featuresConstruction of expression matrices

  • 1


    Load cell-gene count matrices

  • 2

    Produce high count matrix

    Mean-variance trend

  • 3

    Matrix QC

    Identification of low quality libraries
    Low expressed genes and other summary stats

  • 4

    Normalize data

    Library size normalisation to remove technical biases

  • 5

    Identify highly variable features

    Select most variable genes that contain useful information about the biology
    Remove genes that contain noise

  • 6

    Integrate DESeq2 objects

    Format object to perform statistical analysis

  • 7

    Scale data

    Linear transformation to give equal weights to all genes
    Avoid highly-expressed gene to dominate
    Shift gene expression values to cell mean of 0
    Shift gene variance values to cell mean of 1

  • 8

    Linear dimension reduction

    Principal components analysis (PCA) is performed to denoise and compact the data prior to post-processing.

  • 9

    Determine dimensionality

    Select components based on the Elbow Plot

  • 10

    Cluster samples

    Construct K-Nearest neighbor graph on Euclidean distance in PCA space
    Refine by Jaccard similarity
    Cluster samples by modularity optimization Louvain algorithms

  • 11

    Identify differential expressed genes

    Comparative analyses is performed on the differences induced by stimulation/treatment.
    We take the average expression of all clusters and generate the scatter plots, highlighting genes that are identified in previous step.

  • 12

    Gene ontology

    Check over-representations of genes or gene products across conditions.