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Ribosome signatures aid bacterial translation initiation site identification

Overview of attention for article published in BMC Biology, August 2017
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Title
Ribosome signatures aid bacterial translation initiation site identification
Published in
BMC Biology, August 2017
DOI 10.1186/s12915-017-0416-0
Pubmed ID
Authors

Adam Giess, Veronique Jonckheere, Elvis Ndah, Katarzyna Chyżyńska, Petra Van Damme, Eivind Valen

Abstract

While methods for annotation of genes are increasingly reliable, the exact identification of translation initiation sites remains a challenging problem. Since the N-termini of proteins often contain regulatory and targeting information, developing a robust method for start site identification is crucial. Ribosome profiling reads show distinct patterns of read length distributions around translation initiation sites. These patterns are typically lost in standard ribosome profiling analysis pipelines, when reads from footprints are adjusted to determine the specific codon being translated. Utilising these signatures in combination with nucleotide sequence information, we build a model capable of predicting translation initiation sites and demonstrate its high accuracy using N-terminal proteomics. Applying this to prokaryotic translatomes, we re-annotate translation initiation sites and provide evidence of N-terminal truncations and extensions of previously annotated coding sequences. These re-annotations are supported by the presence of structural and sequence-based features next to N-terminal peptide evidence. Finally, our model identifies 61 novel genes previously undiscovered in the Salmonella enterica genome. Signatures within ribosome profiling read length distributions can be used in combination with nucleotide sequence information to provide accurate genome-wide identification of translation initiation sites.

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Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 24%
Researcher 10 20%
Student > Master 7 14%
Student > Bachelor 3 6%
Student > Doctoral Student 3 6%
Other 7 14%
Unknown 8 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 58%
Agricultural and Biological Sciences 7 14%
Computer Science 2 4%
Chemistry 2 4%
Medicine and Dentistry 1 2%
Other 1 2%
Unknown 8 16%