Overview
Transcriptomics shows which genes are active and how they change between conditions. We deliver complete expression analyses with statistics that stand up to review.
Salient features
- Alignment-based and alignment-free quantification
- Differential expression with proper multiple-testing control
- Splicing, fusion and non-coding RNA modules
Analyses we offer
RNA-seq analysis
Quantifies gene and transcript expression from raw reads, with alignment, QC and reporting.
Methods: STAR, salmon, kallisto, nf-core rnaseq.
Gene expression microarray analysis
Normalises and analyses microarray data to find differentially expressed genes.
Methods: limma, affy, oligo.
Non-coding RNA analysis
Detects and annotates miRNAs, lncRNAs and other non-coding RNAs from sequencing data.
Methods: infernal, mirdeep2, FEELnc.
miRNA array analysis
Analyses microRNA array data for differential expression between conditions.
Methods: AgiMicroRna, limma.
CLIP-seq analysis
Maps where RNA-binding proteins bind transcripts at nucleotide resolution.
Methods: clipper, nf-core clipseq, PureCLIP.
DGE analysis
Tests for differential gene expression from count data with robust statistical models.
Methods: DESeq2, PyDESeq2, edgeR.
CAGE analysis
Identifies transcription start sites and promoter activity from CAGE tags.
Methods: CAGEr, nf-core cageseq.
Ribo-seq analysis
Measures active translation with ribosome footprints, including periodicity checks and translation efficiency.
Methods: riboWaltz, ribotish, RiboCode.
GRO-seq analysis
Maps nascent transcription and enhancer RNAs from run-on sequencing.
Methods: dREG, nf-core nascent.
nCounter System
Normalises and analyses NanoString nCounter expression panels with quality control.
Methods: NanoTube, NanoStringDiff.
Degradome-seq analysis
Identifies small RNA cleavage targets from degradome sequencing.
Methods: CleaveLand4.
Alternative splicing analysis
Detects and quantifies differential splicing events between conditions.
Methods: rmats-turbo, SUPPA, leafcutter.
Fusion gene detection
Finds gene fusions from RNA-seq, common in cancer, with filtering of artefacts.
Methods: STAR-Fusion, arriba.
What you receive
- A written report with methods, parameters and interpretation
- Processed data tables and publication-quality figures
- Scripts and settings so every result can be reproduced
Not sure which analysis fits your data? Contact us and we will advise.