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	<a class="visited" href="">Home</a>
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		<h2>Welcome to UTRGEN</h2>
		<p>
            <b>UTRGEN</b> can be used to predict the effect of mRNA 5'UTR sequences on protein translation efficiency(measured by ribosome load), and then generate 5'UTR sequences with higher ribosome load based on the original sequence.

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            We constructed a deep learning model with Convolutional Neural Network(CNN) and Gate Recurrent Unit(GRU) to predict the effect of a given UTR sequence on ribosome loading. Then each UTR sequence is processed by an autoencoder that changes a small number of bases in the original sequence to generate a sequence that is similar to the original sequence but has higher ribosome load. Different from genetic algorithm-based optimization methods, our method minimally changes the original sequence, which is safer and faster.
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		<p><b>Contact us</b></p>
		<p class="comments">
            <b>Dr. Qinghua Cui</b>
		    Department of Biomedical Informatics
		    Peking University Health Science Center
		    38 Xueyuan Rd, Beijing 100191 China
		    Email: cuiqinghua@hsc.pku.edu.cn
        </p>
        <p class="comments">
            <b>Liu Yu</b>
		    Department of Biomedical Informatics
            Peking University Health Science Center
	        38 Xueyuan Rd, Beijing 100191 China
	        Email: 1810305222@pku.edu.cn
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