Advanced Model Predicts Nucleosome Positions with High Precision

DNA-;the molecule that carries the genetic information of all living organisms-;is packaged within cells in a complex manner that allows it to function efficiently. Nucleosomes facilitate DNA compaction and also play a crucial role in regulating gene expression and other biological processes.

A team of scientists led by Dr. Modesto Orozco at IRB Barcelona has developed an advanced computational technique to predict gene architecture through nucleosome position. The method combines experimental approaches with machine learning techniques and signal transmission theory. The study has been published in the journal Nucleic Acids Research.

A Predictive Model that Rivals Experimental Methods

Over the past few years, scientists have used experimental techniques such as MNase-seq to map nucleosomes. The model developed by Dr. Orozco´s team uses DNA sequence information and physical characteristics not only to reproduce experimental data but also to predict nucleosome locations more quickly and accurately. "The precision of our model is comparable with that of the most advanced experimental methods", says Dr. Orozco, head of the Molecular Modelling and Bioinformatics lab at IRB Barcelona and Full Professor at the University of Barcelona.

Implications for Gene Regulation and Biomedicine

The study demonstrates that nucleosomal architecture is greatly influenced by the DNA sequence and physical signals that are emitted by the ends of the genes. These signals determine the location of the first and last nucleosomes (+1 and -last) and also affect the position of the nucleosomes along the gene.

Our work suggests that nucleosome structure may impact gene expression in ways that are more complex than we thought."

Alba Sala, PhD student at IRB Barcelona and first author of the study

This approach is key for future research on how alterations in chromatin structure can influence the onset of diseases. By better understanding the organisation of DNA and nucleosomes, scientists can identify new therapeutic targets and develop more effective treatments.

The study has received support from the European Commission's Centre of Excellence for HPC (H2020) and the BioExcel-3 project, which are Centres of Excellence for Computational Biomolecular Research. Additionally, it has been funded by the Spanish Ministry of Science and Innovation, the Carlos III Health Institute through the National Institute of Bioinformatics, the European Regional Development Fund, the ERDF operational programme in Catalonia, and the Government of Catalonia through AGAUR. IRB Barcelona is also a beneficiary of the Severo Ochoa Excellence recognition from the Ministry of Science, Innovation and Universities.

Source:
Journal reference:

Sala, A., et al. (2024). An integrated machine-learning model to predict nucleosome architecture. Nucleic Acids Research. doi.org/10.1093/nar/gkae689.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of AZoLifeSciences.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
Genes2Genes Unlocks New Potential for Detecting Gene Expression Changes