By Dr Sophie Arthur

There is a vast amount of DNA packed into our cells. To make sure it functions properly and efficiently, it requires very high levels of organisation. If our DNA is not packaged up properly then specific sections of the DNA can’t come together to switch on crucial genes as an example, and ultimately poor organisation leads to an array of developmental diseases and disorders.

Researchers across the world are studying how the DNA and our genome are packaged up in 3D in the cell nucleus. They are unravelling which parts connect to what, and what genes they help to switch on, but they are also unpacking what might be going wrong with those connections in diseases like cancer for example.

Packing a 2-metre-long DNA molecule into something with dimensions ten times smaller than those of a human hair takes a lot of organisation, so there are a lot of complex layers to this. You could look at the whole chromosome level. You could look at a region of the chromosome, or you could examine just one specific DNA loop. Each layer, also, generates immense amounts of data for researchers to analyse. Because of how different techniques have developed and the specific questions that interested researchers have been trying to answer, there is a wide array of different tools at their disposal to start to unpick these layers of complexity. Each research group may have produced a computational tool to analyse this data, but its tweaked slightly so it is more helpful to answer their specific research question. Overall, this has resulted in many tools and many file formats that makes this analysis of all these layers of complexity tedious and time-consuming.

The latest publication from the Developmental Epigenomics group at the LMS, published on 17 December in the journal Genome Biology, presents a new tool to be able to streamline this data analysis, and also help to analyse more of these layers of regulation at the same time. This tool called FAN-C doesn’t contain any ‘fancy’ new methods but combines other tools that have been developed to create a pipeline. A pipeline that is easy to use and install, is versatile so you can enter the pipeline at many different levels depending on your research question, but also that it can be highly customised to link with other tools that you may use.

Juanma Vaquerizas, Head of the Developmental Epigenomics group and senior author of this paper, shared:

“We compared 13 different tools that are used for Hi-C data analysis – that is a technique which tells us which parts of the genome interact with each other. All these different methods are at your disposal, but here we have linked them all together so make data analysis easy and more insightful. This pipeline is designed to be used as a backbone to simplify analysis, but can also be adjusted for your team’s specific needs to set up a standard for consistent and reliable research.”

By using their own specific tools, researchers have been previously looking at just one of these complex layers of DNA organisation, without looking at anything else. This tool can help researchers to consider these other layers of complexity in a simple and efficient way that might help to uncover more knowledge, and exploit the full potential of their dataset. Together, this will help us to get a firmer grasp collectively on what is happening when the DNA is packaged up, and what part of the genome interact in certain situations. As a result, the sooner we might be able to apply this knowledge to help treat disease and disorders.

‘FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data’ was published on 17 December in Genome Biology. Read the full article here.