Modern biology now looks deeper into the cells of living organisms than ever before. Researchers from the Chairs of Bioinformatics at the University of Würzburg have now made further technological progress in the analysis of ribonucleic acids (RNA).

The groups led by the Professors Thomas Dandekar and Kathi Zarnack have further developed a computer-based tool to enable even better investigation of the motifs and structure of RNA. This is important because the functionality of RNA depends heavily on its correct structure. RNA molecules control vital processes within cells; if they do not function properly, this can lead to numerous diseases.

The new tool and its unique features

The team from the Würzburg Biocentre presents the new tool, the RNAanalyzer³, in the renowned journal Nucleic Acids Research. Access to the tool is free, enabling researchers worldwide to work on solutions to medical challenges without financial barriers.

Previous computer programmes often examine only individual sections of an RNA. The RNAanalyzer³, however, takes a holistic approach. It considers not only the sequence of the molecular building blocks, but the entire RNA structure, and places the motifs in a biological context.

The new tool works for all living organisms as well as for viruses. This enables researchers, for example, to directly compare a virus dangerous to humans with one that infects plants – without having to switch tools. “This saves an enormous amount of time”, explains PhD student Aman Akash, the lead author of the publication.

The interactive visualisation is also a major advantage for laboratory work. The RNAanalyzer³ does not present the results as a confusing “jumble of letters” made up of the RNA building blocks A, C, G and U. Instead, the programme creates colourful, clickable maps of the RNA. “This allows researchers to see immediately where the strand forms loops or where important control centres are located”, says Thomas Dandekar, JMU Chair of Bioinformatics.

In use: iron metabolism and inflammation

So-called motifs are particularly important in RNA analyses. They can be thought of as QR codes or landing strips on the RNA strand that are scanned by the cell machinery so that proteins can dock at exactly the right spot.
The researchers demonstrate just how effective the tool is at identifying motifs using two case studies:

• The FTH1 gene (iron metabolism): It is crucial for iron storage. The RNAanalyzer³ accurately identified the “IRE motif” within it. This control centre is also medically significant: If researchers understand it better, they can gain a clearer insight into how cancer cells ‘steal’ iron from the body to grow faster.

• The TNF gene (inflammation): This gene controls inflammatory responses. The tool accurately identified the ‘ARE motifs’ at the rear end of the RNA strand. This region is particularly tightly regulated and determines how stable the RNA is.

Precision through logic and large databases

The researchers use the Perl programming language for their programme and access large international databases such as Rfam and miRbase. The tool retrieves known patterns from these ‘digital libraries’ to compare them with new samples.
Kathi Zarnack, JMU Chair of Bioinformatics II and Principle Investigator in the Würzburg-Munich Cluster of Excellence NUCLEATE, comments: “Compared to other programmes, RNAanalyzer³ makes fewer incorrect predictions because it combines structure and context.”

There are clear technical limitations:

• The calculation of the exact fold is limited to sequences of up to 5,000 nucleotides.
• The general pattern search works for up to 20,000 nucleotides.
• Users can analyse up to five sequences simultaneously.

NUCLEATE Cluster of Excellence

Nucleic acids such as RNA are the focus of the Würzburg-Munich Cluster of Excellence NUCLEATE: within this consortium, researchers from the fields of medicine, biology, chemistry and pharmacy investigate how nucleic acids function in biological processes and develop new approaches for nucleic acid-based medicine. In addition to the University of Würzburg, the Technical University of Munich and LMU Munich are involved in the cluster.