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DeepRibo : a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns

Jim Clauwaert (UGent) , Gerben Menschaert (UGent) and Willem Waegeman (UGent)
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Abstract
Annotation of gene expression in prokaryotes of-ten finds itself corrected due to small variations ofthe annotated gene regions observed between differ-ent (sub)-species. It has become apparent that tradi-tional sequence alignment algorithms, used for thecuration of genomes, are not able to map the fullcomplexity of the genomic landscape. We presentDeepRibo, a novel neural network utilizing featuresextracted from ribosome profiling information andbinding site sequence patterns that shows to be aprecise tool for the delineation and annotation of ex-pressed genes in prokaryotes. The neural networkcombines recurrent memory cells and convolutionallayers, adapting the information gained from boththe high-throughput ribosome profiling data and ri-bosome binding translation initiation sequence re-gion into one model. DeepRibo is designed as a sin-gle model trained on a variety of ribosome profil-ing experiments, used for the identification of openreading frames in prokaryotes withoutaprioriknowl-edge of the translational landscape. Through exten-sive validation of the model trained on various setsof data, multiple species sequence similarity, massspectrometry and Edman degradation verified pro-teins, the effectiveness of DeepRibo is highlighted.
Keywords
TRANSLATION INITIATION SITES, START SITES, IDENTIFICATION, REVEALS, COMPLEXITY, PROTEOMICS, LANDSCAPE, CELLS

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MLA
Clauwaert, Jim, Gerben Menschaert, and Willem Waegeman. “DeepRibo : a Neural Network for Precise Gene Annotation of Prokaryotes by Combining Ribosome Profiling Signal and Binding Site Patterns.” NUCLEIC ACIDS RESEARCH 47.6 (2019): n. pag. Print.
APA
Clauwaert, J., Menschaert, G., & Waegeman, W. (2019). DeepRibo : a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns. NUCLEIC ACIDS RESEARCH, 47(6).
Chicago author-date
Clauwaert, Jim, Gerben Menschaert, and Willem Waegeman. 2019. “DeepRibo : a Neural Network for Precise Gene Annotation of Prokaryotes by Combining Ribosome Profiling Signal and Binding Site Patterns.” Nucleic Acids Research 47 (6).
Chicago author-date (all authors)
Clauwaert, Jim, Gerben Menschaert, and Willem Waegeman. 2019. “DeepRibo : a Neural Network for Precise Gene Annotation of Prokaryotes by Combining Ribosome Profiling Signal and Binding Site Patterns.” Nucleic Acids Research 47 (6).
Vancouver
1.
Clauwaert J, Menschaert G, Waegeman W. DeepRibo : a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns. NUCLEIC ACIDS RESEARCH. 2019;47(6).
IEEE
[1]
J. Clauwaert, G. Menschaert, and W. Waegeman, “DeepRibo : a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns,” NUCLEIC ACIDS RESEARCH, vol. 47, no. 6, 2019.
@article{8617668,
  abstract     = {Annotation of gene expression in prokaryotes of-ten finds itself corrected due to small variations ofthe annotated gene regions observed between differ-ent (sub)-species. It has become apparent that tradi-tional sequence alignment algorithms, used for thecuration of genomes, are not able to map the fullcomplexity of the genomic landscape. We presentDeepRibo, a novel neural network utilizing featuresextracted from ribosome profiling information andbinding site sequence patterns that shows to be aprecise tool for the delineation and annotation of ex-pressed genes in prokaryotes. The neural networkcombines recurrent memory cells and convolutionallayers, adapting the information gained from boththe high-throughput ribosome profiling data and ri-bosome binding translation initiation sequence re-gion into one model. DeepRibo is designed as a sin-gle model trained on a variety of ribosome profil-ing experiments, used for the identification of openreading frames in prokaryotes withoutaprioriknowl-edge of the translational landscape. Through exten-sive validation of the model trained on various setsof data, multiple species sequence similarity, massspectrometry and Edman degradation verified pro-teins, the effectiveness of DeepRibo is highlighted.},
  articleno    = {e36},
  author       = {Clauwaert, Jim and Menschaert, Gerben and Waegeman, Willem},
  issn         = {0305-1048},
  journal      = {NUCLEIC ACIDS RESEARCH},
  keywords     = {TRANSLATION INITIATION SITES,START SITES,IDENTIFICATION,REVEALS,COMPLEXITY,PROTEOMICS,LANDSCAPE,CELLS},
  language     = {eng},
  number       = {6},
  title        = {DeepRibo : a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns},
  url          = {http://dx.doi.org/10.1093/nar/gkz061},
  volume       = {47},
  year         = {2019},
}

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