The Swiss army knife of genome research – Genetics News

It’s every molecular geneticist’s dream: an easy-to-use program that compares data sets from different cellular conditions, identifies activating regions, and then assigns them to their target genes. A research team led by Martin Vingron at the Max Planck Institute for Molecular Genetics in Berlin has now developed a program that masters all of this.

“DNA is quite boring, because it’s practically the same in every cell,” says Martin Vingron, director and head of the bioinformatics department at the Max Planck Institute for Molecular Genetics in Berlin. “When the genome is like the book of life, what interests me the most are the secondary notes. These “notes” are small chemical marks attached to the DNA molecule that do not alter the genetic information itself, but influence what happens to the DNA at the respective site. In other words, these marks have an epigenetic effect. They serve as regulators of genomic regions responsible for turning genes on and off, such as promoters and enhancers.

In many complex diseases, the epigenetic control of genes does not work properly and is of great interest to scientists. However, analysis of these regions in the laboratory is often cumbersome, time-consuming and complicated. This is why Vingron and his team decided to develop a new software package called CRUP (Condition-specific Regulatory Units Prediction) which simplifies the analysis and solves several practical problems.

“We wanted to combine the common steps of the enhancer prediction process into a simple, universal program,” says bioinformatician Verena Heinrich who developed the package. CRUP simplifies analysis in many ways. The machine learning algorithm is not limited to specific cell or tissue types. It does not need to be recalibrated before each analysis of a dataset and allows a comparative study of several datasets. The tool, which was developed by Heinrich and doctoral student Anna Ramisch, is still easy to use.

The stimulating activity of the activator

CRUP specifically identifies and characterizes enhancers – segments of DNA that stimulate or “enhance” gene transcription. These regions attract proteins that attach to promoter sequences that function as a switch for each gene. However, which activator controls the right genes at the right time often remains a mystery. “Enhancers and their associated genes can be located far from each other,” Heinrich explains. “This makes it difficult for us to assign the regulatory sequences to their respective targets. »

The genome contains hundreds of thousands of enhancers that are active at different phases of a cell’s life, such as during growth, maintenance, or disease. When DNA is tight like a thread of wool on coils of carrier proteins called histones, the regulatory sequences are in a “resting” state. They become active only through chemical modifications of histone proteins. Then, sections unwind from the DNA clusters, are exposed and become accessible to molecules that activate genes. Analysis of histone proteins by chromatin immunoprecipitation (ChIP) in tandem with DNA sequencing then reveals which activators are active and which are not.

In three steps for a complete analysis

These ChIP data are the input for the newly developed program. The CRUP first examines all the sequences and then decides whether it is an activator or not. The classification algorithm is based on artificial intelligence, which is trained with information from mouse embryonic stem cells. It detects enhancer regions in many other animal species or tissues, as Heinrich and colleagues have demonstrated on a diverse set of data provided by the German Epigenome Program (DEEP).

In the second step, CRUP can be fed with multiple datasets and the program finds where they differ. This makes it possible to interpret a series of measurements or to identify differences between tissues. Epigenetic changes in enhancers become apparent – ​​over time or when comparing healthy and diseased tissue. The third and final step in the analysis is the mapping of genes to their respective activators. “We asked: what part of the genome is active at the same time in the same place? explains Heinrich. To achieve this, CRUP links enhancer analysis with transcription data that reveals which genes are active and experiments that indicate which parts of the DNA strand are close together.

Finally, the researchers tested their program in a practical setting. They analyzed tissue from mice with rheumatoid arthritis, an immune disease, and compared it to data from healthy animals. CRUP identified more than 200 differences in enhancer regions, some of which had previously been associated with disease in other studies. The genes that CRUP assigned to these enhancers have also been shown to play a role in the disease.

A catalyst for research

“Our program reliably identifies candidates for disease-associated enhancers and links them to their target genes,” says Vingron. His team hopes the new tool will make the field more accessible and accelerate research to help identify the causes of complex human diseases. “CRUP should be particularly useful for all research groups that do not have a team of bioinformaticians at hand. »

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The Swiss army knife of genome research – Genetics News

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