miCORE mRNA Sequencing

Uncover transcriptome dynamics with precision
mRNA sequencing (RNA-Seq) provides a comprehensive view of gene expression changes under different conditions. From transcript to pathway level, it enables you to analyze differential expression, discover novel genes, and detect RNA variants with high sensitivity.

What You Can Achieve

Compare gene expression profiles across experimental conditions
Identify differentially expressed genes, transcripts, and pathways
Study alternative splicing and exon usage in eukaryotic transcriptomes
Detect novel genes and non-coding RNAs
Explore drug responses, functional changes, and disease mechanisms
Perform variant calling for SNVs and small InDels in the transcriptome

Before You Start

To design a successful mRNA sequencing project, consider:

Scientific objective: expression survey, functional study, or variant detection?
Sample preparation: starting material, RNA fraction (poly(A) enrichment, ribo-depletion)
Organism & reference: model organism or de novo transcriptome?
Sequencing depth: sensitivity needed to capture rare transcripts
Replicates: number required for statistical confidence
Read length: balance between specificity and cost
Experimental setup: number of samples, replicates, and conditions

Our experts guide you from design to analysis.

Workflow

A typical workflow for an mRNA sequencing project is shown in the graphic below.
Our modular service design lets you outsource the entire RNA-Seq pipeline or select individual steps

Bioinformatics Analysis

Without Bioinformatics – Raw Data Package

Sequencing QC & yield assessment (.xlsx)
Raw sequencing reads per sample (.fastq)
Project summary report (.pdf)

This option provides maximum flexibility if you prefer to conduct your own downstream analyses.

With Bioinformatics – Standard Analysis

Our RNA-Seq bioinformatics module delivers actionable insights:

Comprehensive interactive report (.html) with sortable and filterable results
Read alignment & mapping files (.bam/.bai) for complete traceability
Gene- & transcript-level counts (.tsv)
Statistical visualizations (heatmaps, volcano plots, enriched pathways, exon maps)

Exclusive to eukaryotic transcriptomes:

Differential gene expression (.tsv & interactive .html)
Gene set & pathway enrichment analysis (.tsv & .html)
Differential exon usage / splicing analysis (.tsv & .html)

Complementary Analysis (Optional)

Variant calling for SNVs and small (<50 bp) InDels in the transcriptome (.vcf)

This allows combined insights into expression changes and sequence-level variation.

Example Results

Typical outputs include :

[below] from left to right

Heatmaps of gene expression patterns, showing similarities and clustering between samples
Volcano plots highlighting significantly up- and downregulated genes
Pathway enrichment diagrams linking expression changes to biological functions
Exon usage plots for identifying alternative splicing events

All results are provided as publication-ready figures and, where available, interactive files for deeper exploration.