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Detecting m6A RNA modification from nanopore sequencing using a semisupervised learning framework
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itemtype="http://schema.org/Person"><span class="name" itemprop="name"><a class="name-search" href="/search?author1=Haotian+Teng&sortspec=date&submit=Submit">Haotian Teng</a></span><a id="xref-aff-1-1" class="xref-aff" href="#aff-1">1</a>, </li> <li class="contributor" id="contrib-2" itemprop="author" itemscope="itemscope" itemtype="http://schema.org/Person"><span class="name" itemprop="name"><a class="name-search" href="/search?author1=Marcus+Stoiber&sortspec=date&submit=Submit">Marcus Stoiber</a></span><a id="xref-aff-2-1" class="xref-aff" href="#aff-2">2</a>, </li> <li class="contributor" id="contrib-3" itemprop="author" itemscope="itemscope" itemtype="http://schema.org/Person"><span class="name" itemprop="name"><a class="name-search" href="/search?author1=Ziv+Bar-Joseph&sortspec=date&submit=Submit">Ziv Bar-Joseph</a></span><a id="xref-aff-1-2" class="xref-aff" href="#aff-1">1</a> and </li> <li class="last" id="contrib-4"><span class="name"><a class="name-search" href="/search?author1=Carl+Kingsford&sortspec=date&submit=Submit">Carl Kingsford</a></span><a id="xref-aff-1-3" class="xref-aff" href="#aff-1">1</a></li> </ol> <ol class="affiliation-list"> <li class="aff"><a id="aff-1" name="aff-1"></a><address><sup>1</sup>Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA; </address> </li> <li class="aff"><a id="aff-2" name="aff-2"></a><address><sup>2</sup>Oxford Nanopore Technologies, Alameda, California 94501-1170, USA </address> </li> </ol> </div> <li class="corresp" id="corresp-1">Corresponding author: <span class="em-link"><span class="em-addr">carlk{at}cs.cmu.edu</span></span></li> <div class="section abstract" id="abstract-1" itemprop="description"> <h2>Abstract</h2> <p id="p-2">Direct nanopore-based RNA sequencing can be used to detect posttranscriptional base modifications, such as N6-methyladenosine (m6A) methylation, based on the electric current signals produced by the distinct chemical structures of modified bases. A key challenge is the scarcity of adequate training data with known methylation modifications. We present Xron, a hybrid encoder鈥揹ecoder framework that delivers a direct methylation-distinguishing basecaller by training on synthetic RNA data and immunoprecipitation (IP)-based experimental data in two steps. First, we generate data with more diverse modification combinations through in silico cross-linking. Second, we use this data set to train an end-to-end neural network basecaller followed by fine-tuning on IP-based experimental data with label smoothing. The trained neural network basecaller outperforms existing methylation detection methods on both read-level and site-level prediction scores. Xron is a standalone, end-to-end m6A-distinguishing basecaller capable of detecting methylated bases directly from raw sequencing signals, enabling de novo methylome assembly. </p> </div> <div class="section fn-group" id="fn-group-1"> <h2>Footnotes</h2> <ul> <li class="fn-supplementary-material" id="fn-5"> <p id="p-60">[Supplemental material is available for this article.]</p> </li> <li class="fn" id="fn-6"> <p id="p-61">Article published online before print. Article, supplemental material, and publication date are at <a href="https://www.genome.org/cgi/doi/10.1101/gr.278960.124">https://www.genome.org/cgi/doi/10.1101/gr.278960.124</a>. </p> </li> <li class="fn" id="fn-7"> <p id="p-62">Freely available online through the <em>Genome Research</em> Open Access option. </p> </li> </ul> </div> <ul class="history-list"> <li xmlns:hwp="http://schema.highwire.org/Journal" class="received" hwp:start="2024-01-17"><span class="received-label">Received </span>January 17, 2024. </li> <li xmlns:hwp="http://schema.highwire.org/Journal" class="accepted" hwp:start="2024-10-03"><span class="accepted-label">Accepted </span>October 3, 2024. </li> </ul> <ul class="copyright-statement"> <li class="fn" id="copyright-statement-1"><a href="/site/misc/terms.xhtml">漏 2024 Teng et al.; Published by Cold Spring Harbor Laboratory Press</a></li> </ul> <div class="license" id="license-1"> <p id="p-1">This article, published in <em>Genome Research</em>, is available under 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