Biogazelle's publications

MicroRNA expression analysis using small RNA sequencing discovery and RT-qPCR-based validation

Van Goethem A, et al.

Functional genomics. Methods in molecular biology

Schematic overview of the TruSeq small RNA library preparation protocol

Quality control of digital PCR assays and platforms

Vynck M, et al.

Analytical and Bioanalytical Chemistry

decodeRNA— predicting non-coding RNA functions using guilt-by-association

Lefever S, et al.



Non-coding after all: Biases in proteomics data do not explain observed absence of lncRNA translation products

Verheggen K, et al.

Journal of Proteome Research

Biases in proteomics data do not explain observed absence of lncRNA translation products

Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data

Everaert C, et al.

Scientific Reports

Everaert et al.

Zipper plot: visualizing transcriptional activity of genomic regions

Avila Cobos F, et al.

BMC Bioinformatics

Model based classification for digital PCR: your Umbrella for rain

Jacobs B, et al.

Analytical Chemistry

miSTAR: miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure

Van Peer G, et al.

Nucleic Acids Research

Depletion of tRNA-halves enables effective small RNA sequencing of low-input murine serum samples

Van Goethem A, et al.

Scientific Reports

Why non-coding RNA research for cancer is key

Vandesompele J

Long non-coding RNA expression profiling in the NCI60 cancer cell line panel using high-throughput RT-qPCR

Mestdagh P, et al.

Scientific Data

Flexible analysis of digital PCR experiments using generalized linear mixed models

Vynck M, et al.

Biomolecular Detection and Quantification

Melanoma addiction to the long non-coding RNA SAMMSON

Leucci E, et al.



Straightforward and sensitive RT-qPCR based gene expression gene analysis of FFPE samples

Zeka F, et al.

Scientific Reports

Boxplot analysis for comparison of gene expression levels in preamplified samples

RT-qPCR-based quantification of small non-coding RNAs

Zeka F, Mestdagh P, Vandesompele J

Methods in Molecular Biology 

Purified RNA concentration in serum from healthy individuals measured by NanoDrop 1000

The impact of disparate isolation methods for extracellular vesicles on downstream RNA profiling

Van Deun J, et al.

Journal of Extracellular Vesicles

Gene Set Enrichment Analysis of UC versus ODG

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

SEQC consortium, Vandesompele J, Hellemans J

Nature Biotechnology

probe expression as the sum of effects from transcripts with a probe-match

miRBase Tracker: keeping track of microRNA annotation changes

Van Peer G, et al.


miRBas Tracker

Some cautionary notes on the petite “Holy Grail” of molecular diagnostics

Vandesompele J, Mestdagh P


Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study

Mestdagh P, et al.

Nature Methods

Hierarchically clustered heatmap indicating miRNA concordance between all platform combinations

The need for transparency and good practices in the qPCR literature

Bustin S A, at al.

Nature Methods

MIQE impact on commercial assays used in 2012–2013 publications

Single-Nucleotide Polymorphisms and Other Mismatches Reduce Performance of Quantitative PCR Assays

Lefever S, et al.

Clinical Chemistry

dCq values between results for perfect-match (PM) and mismatch (MM) reactions (rxns) as a function of MM type and master mix

Effective Alu Repeat Based RT-Qpcr Normalization in Cancer Cell Perturbation Experiments

Rihani A, et al.


Average expression stability values of the reference genes

Guidelines for Minimum Information for Publication of Quantitative Digital PCR Experiments

Hugget J, et la.

Clinical Chemistry

Example of data output from a droplet dPCR instrument (Bio-Rad QX100)

Accurate RT-qPCR gene expression analysis on cell culture lysates

Van Peer G, Mestdagh P, Vandesompele J

Scientific Reports

DNAse treatment

miRNA expression profiling - from reference genes to global mean normalization

D’haene B, et al.

Methods in Molecular Biology

Average fold change expression difference of each miRNA in neuroblastoma with respect to the MYCN amplifi cation status

The microRNA body map: dissecting microRNA function through integrative genomics

Mestdagh P, et al.

Nucleic Acids Research

Mechanistic models of miRNA-directed gene expression regulation

Measurable impact of RNA quality on gene expression results from quantitative PCR

Vermeulen J, et al.

Nucleic Acids Research

The effect of RNA quality on the significance of differential expression of a marker gene between tumours from two risk groups of neuroblastoma patients

RNA pre-amplification enables large-scale RT-qPCR gene-expression studies on limiting sample amounts

Vermeulen J, et al.

BMC Research Notes

Preservation of differential expression after pre-amplification

External oligonucleotide standards enable cross laboratory comparison and exchange of real-time quantitative PCR data

Vermeulen J, et al.

Nucleic Acids Research

A novel and universal method for microRNA RT-qPCR data normalization

Mestdagh P, et al.

Genome Biology

Cumulative distribution of miRNA coefficient of variation (CV) values

The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments

Bustin S A, et al.

Clinical Chemistry

Standardization of real-time PCR gene expression data from independent biological replicates

Willems E, Leyns L, Vandesompele J

Analytical Biochemistry

qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data

Hellemans J, et al.

Genome Biology


Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

Vandesompele J, et al.

Genome Biology

Pairwise variation (Vn/n+1) analysis between the normalization factors NFn and NFn+1 to determine the number of control genes required for accurate normalization (arrowhead = optimal number of control genes for normalization)