Jan Hellemans

Jan Hellemans
Jan Hellemans is co-founder and CEO of Biogazelle. He obtained a Master of Science in Biotechnology (2000) and a PhD in Medical Genetics (2007). He is the author of multiple peer reviewed papers and developer of the qBase software. As an expert in qPCR he has been teaching qPCR courses since 2008.

Recent Posts

Functional validation of a qPCR instrument

Jan Hellemans - Nov 21, 2017

There is the saying “quantitative PCR is easy to perform, but hard to do it right”. With high quality instruments, robust reagents and pre-designed (pre-validated) assays, even novice users can easily generate qPCR data. The challenge is in ensuring that the qPCR data accurately reflect the measure you are interested in. Many variables may negatively impact results: use of non-validated reference genes, non-specific assays, trace amounts of genomic DNA that may be co-amplified, non-calibrated pipets or instruments with too large well-to-well variation. In this blog, I will describe a method to assess instrument related measurement error.

Topics: quality control- testing- homgeneity

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Benchmarking RNA sequencing sensitivity using transcriptome-wide RT-qPCR data

Jan Hellemans - Aug 26, 2014

In the US FDA-led SEQC (aka MAQC-III) study, different sequencing platforms were tested across more than ten sites using well established reference RNA samples with built-in truths in order to assess the discovery and expression profiling performances of platforms and analysis pipelines ( Su, Łabaj, Li et al., Nature Biotechnology, 2014 ). The entire SEQC data set comprises over 100 billion reads (10 Tb) thus providing a unique resource for thorough assessment of RNA-seq performance. Biogazelle co-authored and complemented this study by defining the first human transcriptome using well-established RT-qPCR technology. Using almost 21,000 PrimePCR qPCR assays (jointly developed by Biogazelle and Bio-Rad), the mRNA expression repertoire of the four MAQC samples was established.

Topics: RNA sequencing- sensitivity- RT-qPCR- read depth

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The impact of pipetting errors on PCR amplification efficiencies

Jan Hellemans - Aug 14, 2014

Bioazelle has always validated quantitative PCR assays by combining specificity analysis (targeted sequencing or microfluidic electrophoresis) with an assessment of the PCR amplification efficiency from the slope of a standard curve or dilution series, in accordance with the MIQE guidelines ( Bustin et al., Clinical Chemistry, 2009 ). Having wet-lab validated more than 100,000 assays [see tech note 6262 for materials and methods; assays commercialized as PrimePCR assays by Bio-Rad] with an average efficiency of 99% and more than 98% of the assays with an efficiency of at least 90% [Figure 1] we were quite satisfied with the observed performance. However, recently we started to see more assays failing to meet our quality criterion of acceptable PCR efficiency within the 90-110% range. Nothing that would raise an eyebrow for a handful of assays – some assays are simply not good enough – but worrisome when seeing large numbers deviate. Not only did we observe a drop in efficiency but also a concurrent increase in the y-intercept of the standard curve. Once again this could indicate inferior assay performance, but it was suspicious when observed as a trend across thousands of assays (we have a peak wet lab validation capacity of over 2000 assays per week).

Topics: pipetting error- PCR efficiency

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Novel splice junctions identified in RNA sequencing studies: noise or interesting biology?

Jan Hellemans - Apr 23, 2014

What is a gene? Perhaps a simple question, but no clear answer is given by geneticists. Although the concept of a gene has been shown to be much more complex than ‘a DNA sequence transcribed into RNA and translated into a single protein product’, scientists often ignore this complexity. For example, while the majority of human genes show differential splicing, expression analysis is typically performed in function of genes, not transcripts (PubMed for instance has more than 800,000 ‘gene expression’ papers but only around 2,000 ‘transcript expression’ papers). I do believe that most of the authors of these papers are not ignorant of the concept of alternative splicing, but rather that transcripts are harder to study because less is known about specific transcripts functions than about gene functions, and because transcript analysis used to be more challenging from a technological perspective.

Topics: splice variants- RNA sequencing- RT-qPCR

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