RNADOMAIN · Computational genomics of long noncoding RNA domains across metazoans
„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“
- Период
- 2021-07-01 → 2023-06-30
- Финансиране от ЕС
- 196 591 €
- Участници
- 2
- Схема
- MSCA-IF
Линиите свързват координатора с партньорите.
Накратко на български
Дългите некодиращи РНК се анализират чрез биоинформатика и изкуствен интелект, за да се открият функционални домейни в генома. Това помага за разбирането на механизмите при заболявания като рак, сърдечно-съдови и невродегенеративни проблеми.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
Computational genomics of long noncoding RNA domains across metazoans
The Human Genome Project revealed a surprising discovery - the existence of many RNAs that do not encode proteins but still play important roles in cells. Understanding how these long non-coding RNAs (lncRNAs) function has been a major challenge in biology, with implications for various diseases. Recent research has shown that some repetitive regions in the genome can act as functional domains within lncRNAs, possibly explaining how these RNAs evolve and interact with other molecules. This project aimed to develop new bioinformatics methods, aided by machine learning, to gain deeper insights into lncRNA biology, identify their importance, and disseminate the information using web-accessible databases. This project is important because it has the potential to unlock the mysteries of lncRNAs and their roles in cellular functions and disease, which could have critical implications for complex diseases such as cancer, neurodegenerative diseases, and cardiovascular diseases. By using artificial intelligence and interdisciplinary approaches, this project aimed to advance knowledge in this field and potentially lead to the development of new therapeutic strategies. In conclusion, this project made significant progress in the field of RNA biology by mapping functional domains across the human genome through the development of a novel software tool called RIDL-pipe that finds novel domains in the genome. This has paved the way for further exploration of the functional implications of these novel domains and the dissemination of findings to the scientific community. By linking these domains to specific functional observables, valuable information has been gained to begin describing the regulatory mechanisms and pathways in which these domains are involved, providing a deeper understanding of their functional significance. Finally, the establishment of a web server has allowed for the dissemination of domain maps through an interactive web resource, providing a valuable tool for researchers interested in lncRNA research. The web server is expected to be a valuable resource for the scientific community and contribute to the advancement of our understanding of the lncRNA sequence-function code and its functional implications.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
From junk DNA to genomic dark matter, the road to understanding RNAs that do not encode for proteins has been full of surprises. Compared to 19,000 protein-coding genes, recent estimates point that our genome contains between 25,000 and 100,000 long noncoding RNA (lncRNA) genes. Far from being inert, some lncRNAs are involved in development and disease, particularly, cancer. It has also been shown that the function of a lncRNA can be associated with its localisation in subcellular compartments. Nevertheless, to experimentally characterize and validate interesting lncRNAs is an arduous task. Computational approaches based on machine learning could be designed to complement and scale-up such efforts. Based on recent experimental discoveries, it has been proposed that lncRNAs are separable into functional domains, and that these domains are intimately related to transposable elements and repeats. Nevertheless, how functions are encoded in primary RNA sequence is a fundamental unsolved problem. I propose to develop the first high-throughput computational approach to map lncRNA domains across metazoan genomes. Domains will be first identified according to statistical evidence supported by current biological insights. Putative domains will be queried against state-of-the-art databases on lncRNA function, localisation, and disease. Machine learning algorithms will then be employed to predict new functional domains, and new mechanistic insights will be offered for promising candidates. Lastly, the obtained maps will be stored and disseminated in a database, that will be regularly updated and readily accessible for the research community. This will be a foundational resource to finally shed light on the role of lncRNAs, their regulation and involvement in disease.
Оригинален текст от CORDIS (на английски).
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Данни: CORDIS, © Европейски съюз