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In this study, we develop a novel deep reinforcement learning-based framework, named Riboexp, to model the determinants of the uneven distribution of ribosomes on mRNA transcripts during translation elongation. In particular, our model employs a policy network to perform a context-dependent feature selection in the setting of ribosome density prediction. Our extensive tests demonstrated that Riboexp can significantly outperform the state-of-the-art methods in predicting ribosome density by up to 5.9% in terms of per-gene Pearson correlation coefficient on the datasets from three species. In addition, Riboexp can indicate more informative sequence features for the prediction task than other commonly used attribution methods in deep learning. In-depth analyses also revealed the meaningful biological insights generated by the Riboexp framework. Moreover, the application of Riboexp in codon optimization resulted in an increase of protein production by around 31% over the previous state-of-the-art method that models ribosome density. These results have established Riboexp as a powerful and useful computational tool in the studies of translation dynamics and protein synthesis. Availability: The data and code of this study are available on GitHub: https:\/\/github.com\/Liuxg16\/Riboexp. Contact:zengjy321@tsinghua.edu.cn; songsen@tsinghua.edu.cn<\/jats:p>","DOI":"10.1093\/bib\/bbaa412","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T16:59:08Z","timestamp":1608051548000},"source":"Crossref","is-referenced-by-count":5,"title":["Riboexp: an interpretable reinforcement learning framework for ribosome density modeling"],"prefix":"10.1093","volume":"22","author":[{"given":"Hailin","family":"Hu","sequence":"first","affiliation":[{"name":"School of Medicine, Tsinghua University, Beijing, 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianggen","family":"Liu","sequence":"additional","affiliation":[{"name":"Laboratory for Brain and Intelligence and Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China"},{"name":"Beijing Innovation Center for Future Chip, Tsinghua University, Beijing, 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"An","family":"Xiao","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YangYang","family":"Li","sequence":"additional","affiliation":[{"name":"Comprehensive AIDS Research Center, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, School of Life Sciences, and School of Medicine, Tsinghua University, Beijing, 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengdong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Biorise Life Sciences Co, Ltd., Beijing, 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA"},{"name":"Bioinformatics Division, BNRIST\/Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China"},{"name":"Institute of Integrative Genome Biology, University of California, Riverside, CA 92521, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Song","sequence":"additional","affiliation":[{"name":"Laboratory for Brain and Intelligence and Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China; 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