NCBI Logo
GEO Logo
   NCBI > GEO > Accession DisplayHelp Not logged in | LoginHelp
GEO help: Mouse over screen elements for information.
          Go
Series GSE265754 Query DataSets for GSE265754
Status Public on Oct 28, 2024
Title m6ATM: a deep learning framework for demystifying m6A epitranscriptome via Nanopore long read RNA-seq data
Organism synthetic construct
Experiment type Expression profiling by high throughput sequencing
Summary N6-methyladenosine (m6A) has been one of the most abundant and well-known modifications in mRNA since its discovery in 1970s. Recent studies have demonstrated that m6A gets involved in various biological processes such as alternative splicing and RNA degradation, playing an important role in all kinds of diseases. To better understand the role of m6A, transcriptome-wide m6A profiling data is indispensable. In these years, the Oxford Nanopore Technology Direct RNA Sequencing (DRS) platform has shown promise in RNA modification detection based on current disruptions measured in transcripts. However, decoding current intensity data into modification profiles remains a challenging task. Here, we introduce m6A Transcriptome-wide Mapper (m6ATM), a novel Python-based computational pipeline that applies deep neural networks to predict m6A sites at single-base resolution using DRS data. The m6ATM model architecture incorporates a WaveNet encoder and a dual-stream multiple instance learning model to extract features from specific target sites and characterize the m6A epitranscriptome. For validation, m6ATM achieved an accuracy of 80 to 98% across in-vitro transcription datasets containing varying m6A modification ratios and outperformed other tools in benchmarking with human cell-line data. Moreover, we demonstrated the versatility of m6ATM in providing reliable stoichiometric information and used it to pinpoint PEG10 as a potential m6A target transcript in liver cancer cells. In conclusion, we showed that m6ATM is a high-performance m6A detection tool and our results paved the way for epitranscriptomic precision medicine.
 
Overall design To evalutate the performance of our m6A detection software, we generated in-vitro transcription RNA datasets containing sparsely m6A-modified sequences using Oxford Nanopore Technologies Direct RNA sequencing.
 
Contributor(s) Yu B, Nagae G, Midorikawa Y, Tatsuno K, Dasgupta B, Ota S, Aburatani H, Ueda H
Citation(s) 39438075
Submission date Apr 24, 2024
Last update date Dec 04, 2024
Contact name Boyi Yu
E-mail(s) bo-yi.yu@genome.rcast.u-tokyo.ac.jp
Organization name RCAST
Street address 4-6-1 Komaba, Meguro-ku
City Tokyo
ZIP/Postal code 153-8904
Country Japan
 
Platforms (1)
GPL25738 MinION (synthetic construct)
Samples (3)
GSM8228252 IVT product, unmodified, ONT DRS
GSM8228253 IVT product, m6A 20%, ONT DRS
GSM8228254 IVT product, m6A 50%, ONT DRS
Relations
BioProject PRJNA1104150

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE265754_IVTR.fa.gz 11.0 Kb (ftp)(http) FA
GSE265754_RAW.tar 50.0 Kb (http)(custom) TAR (of TSV)
SRA Run SelectorHelp
Raw data are available in SRA

| NLM | NIH | GEO Help | Disclaimer | Accessibility |
NCBI Home NCBI Search NCBI SiteMap