Advanced processing and analysis methods are applied on all types of data generated. Our team supports dedicated and custom analysis, tailored to the specific project needs.
Our advanced gene expression analysis including differential gene expression analysis (protein coding genes, miRNAs, lncRNAs), time series analysis for patient monitoring, and response to treatment.
Processing of RNA sequencing (RNAseq) reads is done by Cobra, our cloud-based pipeline for RNAseq data management and analysis developed in-house. Cobra processes small RNA, messenger RNA (mRNA), and long non-coding RNA (lncRNA) sequencing data with a strong focus on quality. Our automated data processing pipeline is composed of 3 main steps: data preparation, read mapping, and RNA quantification.
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Processing of small RNAs and miRNAs is built on proprietary small RNA quantification tool with tailored miRNA QC plots. For quantification of long non-coding RNAs we rely on a reference transcriptome enriched with more than 20,000 lncRNA gene models from LNCipedia. This lncRNA enriched reference annotation results in a significantly higher detection of lncRNAs and a better quantification of lncRNAs.
RNA sequencing data provides an excellent entry to go beyond classic RNA abundance analysis and simultaneously exploit the structural information encoded in the transcriptome, such as mutations and fusion genes in cancer cells.
Our RNA sequencing variant analysis pipeline allows the detection of single nucleotide variants, small insertions and deletions, and fusion genes. Candidate variants are queried using external resources providing genomic and functional annotation.
Our data analysis pipelines include access to dedicated lcnRNA and small RNA functional annotation databases.
Our database of predicted lncRNA functions, established through high-throughput perturbation by chemical compounds and silencing of transcription factors, allows for: mapping lncRNAs onto pathways, providing functional context for lncRNAs, and identifying upstream regulators of lncRNAs.
Another of our proprietary database provides functional context for human lncRNAs and miRNAs based on the guilt-by-association principle. Genes that share a similar expression profile are more likely to act in the same pathway or respond to the same upstream regulators. Based on this principle, we can apply data from the annotated part of the transcriptome (the protein coding mRNAs) to derive pathways associated with the unannotated part of the transcriptome (the non-coding RNAs). The guilt-by-association principle can be applied to derive functions associated to both miRNAs and long non-coding RNAs. Outside the public database, we can use the same guilt-by-association principle to predict functions on your gene expression data using gene set enrichment methods.
For qPCR data, our team provides an analysis based on peer-reviewed quantification models for PCR efficiency correction, error propagation, inter-run calibration and statistics. Advanced normalization methods and an improved geNorm algorithm for selection of stably expressed reference genes are also utilized to provide optimal data analysis.
For digital PCR data analysis, we go beyond classic assay validation when testing linearity and accuracy. We use generalized mixed linear models and reliable error propagation, e.g. when using multiple replicates and reference genes. Learn more about it.