Prokaryotic coding regions have little if any specific depletion of Shine-Dalgarno motifs

Yurovsky, Alisa; Amin, Mohammad Ruhul; Gardin, Justin; Chen, Yuping; Skiena, Steve; Futcher, Bruce

doi:10.1371/journal.pone.0202768

Cited by 7 publications

(3 citation statements)

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“…The RBS is complementary to an rRNA sequence that plays a crucial role in the binding of the ribosome to an mRNA transcript. In E. coli , it is AGGAGG and is known as Shine-Dalgarno (SD) sequence ( 71 , 72 ). However, how strongly the SD sequence affects translation efficiency remains controversial ( 6 , 35 , 67 , 73 ).…”

Section: Resultsmentioning

confidence: 99%

Decoding stoichiometric protein synthesis in E. coli through translation rate parameters

Irshad,

Sharma

2023

Biophysical Reports

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Section: Resultsmentioning

confidence: 99%

Decoding stoichiometric protein synthesis in E. coli through translation rate parameters

Irshad,

Sharma

2023

Biophysical Reports

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“…The mudrA cDNA is also extremely difficult to propagate in E. coli , as cloning products often bear frame-shift mutations in MURA due to point insertions or deletions [ 34 ]. The Shine–Dalgarno motif, in front of prokaryotic start codons, could guide the translation of intracellular mRNAs [ 35 ], which is proposed to result in the instability of MuDR from allowing the production of transposase in E. coli [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

Cloning of Maize TED Transposon into Escherichia coli Reveals the Polychromatic Sequence Landscape of Refractorily Propagated Plasmids

Cong

Tan

et al. 2022

IJMS

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MuDR, the founder member of the Mutator superfamily and its MURA transcripts, has been identified as toxic sequences to Escherichia coli (E. coli), which heavily hindered the elucidation of the biochemical features of MURA transposase and confined the broader application of the Mutator system in other organisms. To harness less constrained systems as alternatives, we attempted to clone TED and Jittery, two recently isolated autonomous Mutator-like elements (MULEs) from maize, respectively. Their full-length transcripts and genomic copies are successfully cloned when the incubation time for bacteria to recover from heat shock is extended appropriately prior to plating. However, during their proliferation in E. coli, TED transformed plasmids are unstable, as evidenced by derivatives from which frameshift, deletion mutations, or IS transposon insertions are readily detected. Our results suggest that neither leaky expression of the transposase nor the presence of terminal inverse repeats (TIRs) are responsible for the cloning barriers, which were once ascribed to the presence of the Shine–Dalgarno-like sequence. Instead, the internal sequence of TED (from 1250 to 2845 bp), especially the exons in this region, was the most likely causer. The findings provide novel insights into the property and function of the Mutator superfamily and shed light on the dissection of toxic effects on cloning from MULEs.

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“…This work was inspired by our experiences using existing annotations to study the depletion of Shine-Delgarno (SD) sequences [1]. In the classical model of prokaryotic gene transcription, the tail of the 16S ribosomal RNA binds to an SD sequence upstream of genes to initiate transcription.…”

Section: Introductionmentioning

confidence: 99%

DeepAnnotator

Amin

Yurovsky

Tian

et al. 2018

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

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Genome annotation is the process of labeling DNA sequences of an organism with its biological features, and is one of the fundamental problems in Bioinformatics. Public annotation pipelines such as NCBI integrate a variety of algorithms and homology searches on public and private databases. However, they build on the information of varying consistency and quality, produced over the last two decades. We identified 12,415 errors in NCBI RNA gene annotations, demonstrating the need for improved annotation programs.We use Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) to demonstrate the potential of deep learning networks to annotate genome sequences, and evaluate different approaches on prokaryotic sequences from NCBI database. Particularly, we evaluate DNA K−mer embeddings and the application of RNNs for genome annotation. We show how to improve the performance of our deep networks by incorporating intermediate objectives and downstream algorithms to achieve better accuracy.Our method, called DeepAnnotator, achieves an F-score of 94%, and establishes a generalized computational approach for genome annotation using deep learning. Our results are very encouraging as our method eliminates the requirement of hand crafted features and motivates further research in application of deep learning to full genome annotation. DeepAnnotator algorithms and models can be accessed in Github: https://github.com/ruhulsbu/DeepAnnotator.

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Prokaryotic coding regions have little if any specific depletion of Shine-Dalgarno motifs

Cited by 7 publications

References 31 publications

Decoding stoichiometric protein synthesis in E. coli through translation rate parameters

Decoding stoichiometric protein synthesis in E. coli through translation rate parameters

Cloning of Maize TED Transposon into Escherichia coli Reveals the Polychromatic Sequence Landscape of Refractorily Propagated Plasmids

DeepAnnotator

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