Featured Blogs
How to stay on top of your data management game: Enjoying and maintaining a great data management plan
20 Dec 2022
A great data management plan means you barely have to think about it anymore. This blog discusses the benefits of building the perfect data management plan and planning for future efficiency and success in its maintenance.
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Making the data management plan of your dreams: A step-by-step guide
13 Dec 2022
In the field of life sciences, it can be difficult to come to a common understanding of what data management means, and to put effective data management strategies into practice. In this blog, we’ll guide you through exactly how we accomplish that.
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Understanding data management: Save time, money, and headaches in life sciences
07 Dec 2022
Poor data management can take time away from your real focus at work. In this blog, we are going to cover the what, why, and how of data management, so that you can move toward implementing a data management plan that will work for you.
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Microbiome analysis: Correlation is not causation
24 Nov 2022
The difference between correlation and causation is an important consideration to keep in mind when analysing huge datasets, two points of information may correlate, but it does not necessarily mean that one has any influence on the other.
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How to get your microbiome research pixel perfect
16 Nov 2022
Back in the day, microbiome analysis was a very slow research method. But in microbial research today, a lot has changed. We now use modern approaches which provide much more data than before information. In this blog, we look at how microbiome research is performed and introduce methods to streamline your data analysis process.
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The great potential of microbiome analysis
03 Nov 2022
Microbiome analyses have shed a lot of light on a wide range of research areas, from investigating how soil microbiomes affect crop yields, to understanding the importance of microbes in aquatic environments, and more. Here, we highlight some of the main fields of research where microbiome analysis has a significant impact.
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Julia: Different datasets have different needs
21 Sep 2022
Just like we use different technologies to answer different scientific questions in the laboratory, different datasets have different needs in regard to programming languages that are best suited for their analysis.
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Rust: A promising programming language for large biological datasets
20 Sep 2022
Using effective software is often the keystone that can bring research in drug discovery or disease treatment to the next level. But depending on the project and the type of data, there will be different bioinformatic requirements and priorities.
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AI-based protein structure prediction at BioLizard
23 Aug 2022
As a team of computational biology and AI enthusiasts, BioLizard uses advanced computational methods to generate biological knowledge.
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AlphaFold – The end of experimental structural biology?
23 Aug 2022
Although a quick read through the many success stories of AlphaFold could create the impression that experimental structure determination is now a thing of the past, this is not completely the case.
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Breakthrough technology tackles one of science’s most complex problems
17 Aug 2022
Generations of scientists have tried to come up with computational solutions to solve the so-called “protein folding problem” for decades. They never quite succeeded, until recently…
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Statistical challenges faced when analysing single cell sequencing data
08 Nov 2021
Throughout recent years, single-cell sequencing has evolved into a multi-purpose toolkit that tackles various biological questions. However, different omics approaches come with their own unique analytical challenges.
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BioLizard PhD student develops a new platform in his first year: proBiome
19 Oct 2021
For Steff Taelman, August 2021 not only marked his first anniversary as a BioLizard employee, but also lead to the creation of a new BioLizard platform – proBiome.
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When should you use single-cell sequencing?
12 Aug 2021
Before you get into a single-cell project, ask yourself if this extra level of information is desirable for you. It might be interesting for your research, but it might not!
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