xPore Software Detects Over 100 RNA Modifications With AI

Researchers at the Agency for Science, Technology and Research’s Genome Institute of Singapore have developed xPore, software that identifies over 100 RNA modifications within genomic data using artificial intelligence. These RNA modifications represent a “hidden layer of information” beyond the standard RNA sequence, impacting cellular function and potentially linking to disease risk and mRNA vaccine development. Unlike previous methods requiring extensive lab work, xPore utilizes Nanopore direct RNA-sequencing to analyze native RNA while retaining its modifications; the team repurposed existing AI tools to detect subtle differences caused by these changes. “When we speak, the same word can have very different meanings depending on the pronunciation and context,” said Dr. Jonathan Göke, Group Leader Transcriptomics at GIS, drawing a parallel to how chemical changes can alter RNA function.

xPore Software Extracts RNA Modifications from Genomics Data

Over 100 distinct RNA modifications are now known to influence cellular function, adding complexity to the conventional understanding of RNA sequencing and prompting a re-evaluation of genomic data analysis. Researchers have long recognized these modifications impact processes ranging from disease susceptibility to the efficacy of mRNA vaccines, with m6A methylation of adenosine as a prevalent example. Previously, identifying these modifications demanded extensive laboratory work, limiting the scale and speed of research; however, xPore is designed to address these limitations. The software employs a machine learning approach, repurposing tools from artificial intelligence research to pinpoint subtle differences indicative of RNA modifications. The team focused on identifying consistent data from unmodified RNA sites, recognizing that modifications disrupt this consistency, allowing the algorithm to accurately detect changes. This approach has already demonstrated its potential in clinical settings.

Collaborating with Prof Chng Wee Joo, Director of the National University Cancer Institute, Singapore, the researchers successfully used xPore to detect m6A RNA modification in samples from patients with multiple myeloma. “We have been interested in studying m6A modification in myeloma as this may have important clinical and therapeutic implications for patients with poor outcome,” Prof Chng added, “and now with xPore, we have an important tool to facilitate our studies.” Dr Sho Goh, Assistant Prof from Shenzhen Bay Laboratory, highlighted xPore’s flexibility, noting that its ability to map new RNA modifications without requiring specific reagents could expedite the discovery of novel RNA modification functions.

Beyond the familiar sequence of RNA bases, over 100 distinct RNA modifications exist, influencing cellular function and representing a complex regulatory landscape scientists are increasingly equipped to explore. This technology sequences native RNA, crucially retaining its modifications, a significant advancement over earlier methods that required destructive chemical treatments. The core of xPore lies in a machine learning approach, repurposing established artificial intelligence tools to discern subtle differences in RNA molecules. The team adopted a statistical model commonly used in data science to precisely pinpoint these modified sites, a strategy that allows for the detection of multiple RNA modifications simultaneously.