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DeepRibo: precise gene annotation of prokaryotes using deep learning and ribosome profiling data

Jim Clauwaert, Gerben Menschaert, Willem Waegeman
doi: https://doi.org/10.1101/317180
Jim Clauwaert
1KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Gent, Belgium
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Gerben Menschaert
2BioBix, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Gent, Belgium
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Willem Waegeman
1KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Gent, Belgium
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Abstract

Annotation of gene expression in prokaryotes often finds itself corrected due to small variations of the annotated gene regions observed between different (sub)-species. It has become apparent that traditional sequence alignment algorithms, used for the curation of genomes, are not able to map the full complexity of the genomic landscape. We present DeepRibo, a novel neural network applying ribosome profiling data that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes. The neural network combines recurrent memory cells and convolutional layers, adapting the information gained from both the high-throughput ribosome profiling data and Shine-Dalgarno region into one model. DeepRibo is designed as a single model trained on a variety of ribosome profiling experiments, and is therefore evaluated on independent datasets. Through extensive validation of the model, including the use of multiple species sequence similarity and mass spectrometry, the effectiveness of the model is highlighted.

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Posted May 09, 2018.
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DeepRibo: precise gene annotation of prokaryotes using deep learning and ribosome profiling data
Jim Clauwaert, Gerben Menschaert, Willem Waegeman
bioRxiv 317180; doi: https://doi.org/10.1101/317180
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DeepRibo: precise gene annotation of prokaryotes using deep learning and ribosome profiling data
Jim Clauwaert, Gerben Menschaert, Willem Waegeman
bioRxiv 317180; doi: https://doi.org/10.1101/317180

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