ArticleBMC bioinformatics2017
Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- Universal evolutionary selection for high dimensional silent patterns of information hidden in the redundancy of viral genetic code.Bioinformatics (Oxford, England) · 2018Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe regulation of all gene expression steps (e.g., Transcription, RNA processing, Translation, and mRNA Degradation) is known to be primarily encoded in different parts of genes and in genomic regions in proximity to genes (e.g., promoters, untranslated regions, coding regions, introns, etc.). However, the entire gene expression codes and the genomic regions where they are encoded are still unknown.
resultsHere, we employ an unsupervised approach to estimate the concentration of gene expression codes in different non-coding parts of genes and transcripts, such as introns and untranslated regions, focusing on three model organisms (Escherichia coli, Saccharomyces cerevisiae, and Schizosaccharomyces pombe). Our analyses support the conjecture that regions adjacent to the beginning and end of ORFs and the beginning and end of introns tend to include higher concentration of gene expression information relatively to regions further away. In addition, we report the exact regions with elevated concentration of gene expression codes. Furthermore, we demonstrate that the concentration of these codes in different genetic regions is correlated with the expression levels of the corresponding genes, and with splicing efficiency measurements and meiotic stage gene expression measurements in S. cerevisiae.
conclusionWe suggest that these discoveries improve our understanding of gene expression regulation and evolution; they can also be used for developing improved models of genome/gene evolution and for engineering gene expression in various biotechnological and synthetic biology applications.
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