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.
Whole (un)stranded transcriptome data (e.g. Illumina)
Single- or paired-en compressed raw FastQ files
Reference transcriptome (hg19 or hg38 or mm10)
Reads with low-quality are discarded
Adaptor and quality trimming of reads
STAR aligns reads to reference transcriptome
Alignment statistics: read depths, per base, GC content, …
Aligned reads are assigned to genomic featuresConstruction of expression matrices
Load cell-gene count matrices
Produce high count matrix
Identification of low quality libraries
Low expressed genes and other summary stats
Library size normalisation to remove technical biases
Identify highly variable features
Select most variable genes that contain useful information about the biology
Remove genes that contain noise
Integrate DESeq2 objects
Format object to perform statistical analysis
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
Linear dimension reduction
Principal components analysis (PCA) is performed to denoise and compact the data prior to post-processing.
Select components based on the Elbow Plot
Construct K-Nearest neighbor graph on Euclidean distance in PCA space
Refine by Jaccard similarity
Cluster samples by modularity optimization Louvain algorithms
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.
Check over-representations of genes or gene products across conditions.