ArticleBMC bioinformatics2010
Computational identification of rare codons of Escherichia coli based on codon pairs preference.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Unravelling Prokaryotic Codon Usage: Insights from Phylogeny, Influencing Factors and Pathogenicity.Current genomics · 2025Review
- Article
- Article
- Computationally validated SARS-CoV-2 CTL and HTL Multi-Patch vaccines, designed by reverse epitomics approach, show potential to cover large ethnically distributed human population worldwide.Journal of biomolecular structure & dynamics · 2022Article
- Vaccines Targeting Numerous Coronavirus Antigens, Ensuring Broader Global Population Coverage: Multi-epitope and Multi-patch Vaccines.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Codon-based indices for modeling gene expression and transcript evolution.Computational and structural biotechnology journal · 2021Review
- Structural basis of development of multi-epitope vaccine against Middle East respiratory syndrome using in silico approach.Infection and drug resistance · 2018Article
- Article
- Article
- Structural Basis for Designing Multiepitope Vaccines Against COVID-19 Infection: In Silico Vaccine Design and Validation.JMIR bioinformatics and biotechnologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCodon bias is believed to play an important role in the control of gene expression. In Escherichia coli, some rare codons, which can limit the expression level of exogenous protein, have been defined by gene engineering operations. Previous studies have confirmed the existence of codon pair's preference in many genomes, but the underlying cause of this bias has not been well established. Here we focus on the patterns of rarely-used synonymous codons. A novel method was introduced to identify the rare codons merely by codon pair bias in Escherichia coli.
resultsIn Escherichia coli, we defined the "rare codon pairs" by calculating the frequency of occurrence of all codon pairs in coding sequences. Rare codons which are disliked in genes could make great contributions to forming rare codon pairs. Meanwhile our investigation showed that many of these rare codon pairs contain termination codons and the recognized sites of restriction enzymes. Furthermore, a new index (F(rare)) was developed. Through comparison with the classical indices we found a significant negative correlation between F(rare) and the indices which depend on reference datasets.
conclusionsOur approach suggests that we can identify rare codons by studying the context in which a codon lies. Also, the frequency of rare codons (F(rare)) could be a useful index of codon bias regardless of the lack of expression abundance information.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.