Press Release
21 Jul 2021
Scientists Use Artificial Intelligence (AI) To Reveal Hidden Layer Of Information Above RNA
SINGAPORE – A team
of researchers from Agency for Science, Technology and Research’s (A*STAR)
Genome Institute of Singapore (GIS) has developed xPore, a software that
extracts RNA modifications (an additional layer of information above the
genetic molecule RNA) from genomics data. Their research was published in Nature Biotechnology on 20 July 2021.
"When we speak, the same
word can have very different meanings depending on the pronunciation and
context. For RNAs, we have something similar as chemical molecules may change
the function of the same RNA. These RNA modifications are widespread, but
because they do not change the letters of the RNA. They are very difficult to
identify," said Dr Jonathan Göke, Group Leader of Laboratory of
Computational Transcriptomics at GIS. More than 100 RNA modifications are known
to play different roles in cells. Some of these RNA modifications are
associated with disease risk, while others are used in mRNA vaccines. One of
the most common modifications is the m6A methylation of the
adenosine in RNAs. In the past, identifying RNA modifications required labour-
and time-intensive bench-experiment assays that only very few laboratories can
perform.
To overcome these
limitations, the team utilised Nanopore direct RNA-sequencing, a new transcriptomic
technology that sequences native RNA molecule with its modifications retained.
To extract the hidden layer of RNA modifications, they developed xPore, a
machine learning-based method that re-purposes tools from AI research to
precisely detect differences in RNA modifications. A property employed in the
method is the consistent data of the unmodified sites, and the existence of
modifications disrupts this consistency.
"Similar problems occur
in other data-rich areas such as finance or speech recognition that tap on
machine learning. Here, we adopted an existing statistical model that is used
frequently in data science, so that it can precisely identify these modified
sites," explained Dr Ploy Pratanwanich, formerly a Postdoctoral Fellow, Epigenetics
and Epitranscriptomics at GIS, current Lecturer at Chulalongkorn University
(Thailand), and first author of the study. In their study, the authors
demonstrated that xPore is highly accurate and is able to overcome many of the
previous limitations in studying RNA modifications.
Collaborating with Prof Chng
Wee Joo, Director of the National University Cancer Institute, Singapore
(NCIS), the team successfully detected the m6A RNA modification
using xPore in multiple myeloma cancer patient samples, showing xPore’s
potential for large-scale clinical analyses. "We have been interested in
studying m6A modification in myeloma as this may have important
clinical and therapeutic implications for patients with poor outcome. Now with
xPore, we have an important tool to facilitate our studies," Prof Chng
added.
Dr Sho Goh, Assistant Prof
from Shenzhen Bay Laboratory who co-led the study, said, “The ability to map
new RNA modifications is vital for determining their functions. Since xPore
does not require specific reagents that specialise in identifying only a single
RNA modification type, it can potentially detect other RNA modifications beyond
m6A. Therefore, xPore’s flexibility can expedite our efforts to
discover novel RNA modification functions.”
Prof Patrick Tan, Executive
Director of GIS, said, “This study introduces a computational method that
enables the profiling of differential RNA modifications transcriptome wide, and
provides a systematic resource of direct RNA-Seq data. It will be valuable as a
benchmark data set for modification detection, with the potential to lead to
better patient outcomes.”
– END –
Enclosed:
ANNEX A – Notes to Editor
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