9 February 2023

The new method enables faster and easier reading of RNA modifications which can be applied to clinical samples, the study of plant RNA, or understanding their role in diseases.

m6Anet detects m6A modification from direct RNA sequencing data. (Credit: A*STAR’s Genome Institute of Singapore. Image designed by Dr Radhika Patnala (

SINGAPORE – A team of researchers from the Agency for Science, Technology and Research (A*STAR) and the National University of Singapore (NUS) has developed a software method that accurately predicts chemical modifications of RNA1 molecules from genomic data. Their method, called m6Anet, was published in Nature Methods on 10 November 2022.

Within the RNA, different types of chemical molecules added to the RNA determine how the RNA molecule functions. However, these RNA changes are often invisible to standard approaches used by scientists to read RNA. Presently, more than 160 RNA modifications have been discovered, of which the most prevalent RNA modification—N6-Methyladenosine (m6A)—is associated with human diseases such as cancer.

In the past, identifying RNA modifications required time-consuming and laborious bench experiments that were not accessible to most laboratories. Furthermore, previous methods failed to detect m6A at the single-molecule resolution, which is critical for understanding the biological mechanisms involving m6A.

The team overcame these limitations by leveraging direct Nanopore RNA sequencing, an emerging technology that sequences a raw RNA molecule together with its RNA modifications. In this study, they developed m6Anet, a software that trains deep neural networks with abundant direct Nanopore RNA sequencing data and Multiple-Instance Learning (MIL) approach, to accurately detect the presence of m6A.

"In traditional machine learning, we often have one label for each example we want to classify. For example, each image is either a cat or not a cat, and the algorithm learns to differentiate cat images from other images based on their labels. The issue with detecting m6A is that we have an overwhelming amount of data with unclear labels. Imagine having a large photo album with a cat photo hidden among millions of other photos, and attempting to identify that particular photo without having any labels to base your search upon. Fortunately, this has been studied in machine learning literature before and is known as the MIL problem," explained Christopher Hendra, current PhD student at A*STAR’s Genome Institute of Singapore (GIS) and NUS Institute of Data Science, and the first author of the study.

In this study, the team demonstrated that m6Anet can predict the presence of m6A with high accuracy at a single-molecule resolution from a single sample across species.

"Our AI model has only seen data from a human sample, but it is able to accurately identify RNA modifications even in samples from species that the model has not seen before," said Dr Jonathan Göke, Group Leader of the Laboratory of Computational Transcriptomics at A*STAR’s GIS and senior author of the study. “The ability to identify RNA modifications in different biological samples can be used to understand their role in many different applications such as in cancer research or plant genomics.”

"It is very satisfying to see how theoretically-grounded and well-studied machine-learning techniques such as the MIL can be leveraged to offer an elegant solution to this challenging problem. Witnessing the software being adopted so rapidly by the scientific community is a reward for our efforts!" said Associate Professor Alexandre Thiery, Department of Statistics and Data Science, NUS Faculty of Science, who co-led the study.

Prof Patrick Tan, Executive Director of A*STAR’s GIS, said, “Accurately and efficiently identifying RNA modifications had been a long-standing challenge, and m6Anet helps to address these limitations. To benefit the wider scientific community, this AI method, along with results from the study, have been made public for other scientists to accelerate their research.”

The source code for m6Anet is available at Installation instructions and online documentation are available at The code to reproduce results in this manuscript is available through Code Ocean.

1Ribonucleic acid (abbreviated RNA) is a nucleic acid present in all living cells that has structural similarities to DNA. Unlike DNA, however, RNA is most often single-stranded. An RNA molecule has a backbone made of alternating phosphate groups and sugar ribose, rather than the deoxyribose found in DNA.

For media queries and clarifications, please contact:

Genome Institute of Singapore, A*STAR
Winnie Lim
Manager, Office of Corporate Communications
Genome Institute of Singapore, A*STAR
Tel: +65 6808 8013
HP: +65 9669 1730
About A*STAR’s Genome Institute of Singapore (GIS)

The Genome Institute of Singapore (GIS) is an institute of the Agency for Science, Technology and Research (A*STAR). It has a global vision that seeks to use genomic sciences to achieve extraordinary improvements in human health and public prosperity. Established in 2000 as a centre for genomic discovery, the GIS pursues the integration of technology, genetics and biology towards academic, economic and societal impact, with a mission to "read, reveal and write DNA for a better Singapore and world".

Key research areas at the GIS include Precision Medicine & Population Genomics, Genome Informatics, Spatial & Single Cell Systems, Epigenetic & Epitranscriptomic Regulation, Genome Architecture & Design, and Sequencing Platforms. The genomics infrastructure at the GIS is also utilised to train new scientific talent, to function as a bridge for academic and industrial research, and to explore scientific questions of high impact.

For more information about GIS, please visit

About the Agency for Science, Technology and Research (A*STAR)

A*STAR is Singapore's lead public sector R&D agency. Through open innovation, we collaborate with our partners in both the public and private sectors to benefit the economy and society. As a Science and Technology Organisation, A*STAR bridges the gap between academia and industry. Our research creates economic growth and jobs for Singapore, and enhances lives by improving societal outcomes in healthcare, urban living, and sustainability. A*STAR plays a key role in nurturing scientific talent and leaders for the wider research community and industry. A*STAR’s R&D activities span biomedical sciences to physical sciences and engineering, with research entities primarily located in Biopolis and Fusionopolis. For ongoing news, visit

Follow us on
Facebook | LinkedIn | Instagram | YouTube

About National University of Singapore (NUS)

The National University of Singapore (NUS) is Singapore’s flagship university, which offers a global approach to education, research and entrepreneurship, with a focus on Asian perspectives and expertise. We have 16 colleges, faculties and schools across three campuses in Singapore, with more than 40,000 students from 100 countries enriching our vibrant and diverse campus community. We have also established our NUS Overseas Colleges programme in more than 15 cities around the world.

Our multidisciplinary and real-world approach to education, research and entrepreneurship enables us to work closely with industry, governments and academia to address crucial and complex issues relevant to Asia and the world. Researchers in our faculties, research centres of excellence, corporate labs and more than 30 university-level research institutes focus on themes that include energy; environmental and urban sustainability; treatment and prevention of diseases; active ageing; advanced materials; risk management and resilience of financial systems; Asian studies; and Smart Nation capabilities such as artificial intelligence, data science, operations research and cybersecurity.

SingHealth delivers comprehensive, multi-disciplinary and integrated care across a network of acute hospitals, national specialty centres, polyclinics and community hospitals. Offering over 40 clinical specialties, SingHealth is Singapore’s largest public healthcare cluster.

For more information on NUS, please visit