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IEEE/ACM Transactions on Computational Biology and Bioinformatics
Article . 2011 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
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A Genetic Optimization Approach for Isolating Translational Efficiency Bias

Authors: Raiford, Douglas W.; Krane, Dan E.; Doom, Travis E.; Raymer, Michael L.;

A Genetic Optimization Approach for Isolating Translational Efficiency Bias

Abstract

The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution, phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most well-studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both genomic sequence information and prior knowledge of a set of highly expressed genes. This novel approach provides more accurate results from sequence information alone by resisting the confounding effects of other biases. We validate this increase in accuracy in isolating translational efficiency bias on 10 microbial genomes, five of which have proven particularly difficult for existing approaches due to the presence of strong confounding biases.

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Keywords

Computer Science and Engineering, Databases and Information Systems, Bioinformatics, OS and Networks, Gene Expression, Strand Bias, Social and Behavioral Sciences, Science and Technology Studies, Evolution, Molecular, Translational Efficiency, Physical Sciences and Mathematics, Evolutionary Computing and Genetic Algorithms, Codon, Oligonucleotide Array Sequence Analysis, Computer Sciences, Communication, Life Sciences, Genomics, Biological Sciences, Genes, Bacterial, Protein Biosynthesis, GC-Content, Mutation, Communication Technology and New Media, Ribosomes, Codon Usage Bias, Genome, Bacterial

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
bronze